Knowledge organisation by means of concept process mapping
Knowledge organisation by means of
concept-process mapping
Mark GREGORY
ESC Rennes School of Business
§0
Preface to this working paper
Conceprocity – concept <-> process reciprocity – is a visual and textual language and
toolset intended for capturing, expressing, communicating and co-creating models of topic
areas of domain knowledge by domain experts or learners. This working paper introduces,
positions, compares and justifies Conceprocity. The paper positions Conceprocity within
existing spectra of approaches to data and knowledge organisation. It also introduces some
of the more immediate use cases for which it has been created. Specifically it discusses how
Conceprocity is to be used in my PhD research and teaching.
Readers of this document might like also to make reference to the online presentations of
Conceprocity which can be found on the website www.MarkRogerGregory.net. In addition
to a narrated audio presentation, these presentations include video demonstrating the actual
construction of a Conceprocity model using Lucidchart. The presentations are of varying
lengths, with the longest being intended only for people who intend actually to learn how to
use Conceprocity in anger.
Readers should please understand that this is very much a working paper, not particularly
well structured and in places still somewhat tentative. Nevertheless the author would very
much appreciate your comments.
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Knowledge organisation by means of concept process mapping
Table of Contents
§0
Preface to this working paper .......................................................................................................... 1
§1
An introduction to Conceprocity ...................................................................................................... 6
§2
1.1
Why model personal knowledge conceptually? ................................................................. 6
1.2
Why Conceprocity is important and indeed essential to my work ..................................... 7
1.3
Designing your working life: learning how to get things done better ................................ 7
1.4
Simple Conceprocity model ................................................................................................ 7
1.5
Positioning Conceprocity as a Knowledge Organisation Representation ........................... 8
Conceprocity described .................................................................................................................... 9
2.1
An example Conceprocity model and how it has been created ......................................... 9
2.2
Conceprocity as a modelling language .............................................................................. 10
2.3
Conceprocity: Notions ....................................................................................................... 11
2.4
Representing Conceprocity relationships ......................................................................... 12
2.5
Why Conceprocity distinguishes concepts, procedures and principles ............................ 13
2.6
Conceprocity relationships ................................................................................................ 14
2.7
Events and Logical Connectors .......................................................................................... 18
2.8
A summary of Conceprocity grammar rules ..................................................................... 21
2.9
Structuring Conceprocity maps ......................................................................................... 22
2.10
Conceprocity Usage Profiles .............................................................................................. 23
2.11
Learning to use Conceprocity: moving on from the beginner’s profile ............................ 23
2.12
Conceprocity for the Right Brain ....................................................................................... 24
2.13
Specific PhD research process as a Conceprocity concept map ....................................... 25
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Knowledge organisation by means of concept process mapping
§3
§4
§5
The role of Conceprocity in the PhD research of Mark Gregory: some criticisms and the
ways in which they are addressed in the research design ............................................................. 26
3.1
Why Conceprocity is important in my PhD research ........................................................ 26
3.2
The challenge according to David Weir............................................................................. 26
3.3
My response to David Weir’s challenge ............................................................................ 27
3.4
Renaud Macgilchrist’s challenge ....................................................................................... 28
3.5
My response to Renaud Macgilchrist’s challenge ............................................................. 29
3.6
Recap: why are concept maps essential to this Ph.D. research? ...................................... 29
Ways of organising personal knowledge and data ........................................................................ 30
4.1
Systems thinking and modelling........................................................................................ 30
4.2
A Wikipedia introduction to Knowledge Organisation ..................................................... 31
4.3
Schema representation ..................................................................................................... 31
4.4
Knowledge Representation ............................................................................................... 32
4.5
Personal Information Management System PIMS Data Structures .................................. 34
4.6
Knowledge Organisation: an LIS (library and information science) perspective .............. 38
Positioning Conceprocity among Knowledge Organisation Systems ............................................. 39
5.1
Knowledge Representation (KR) as the primary dimension for classifying and
comparing Knowledge Organisation Systems KOS ........................................................... 39
5.2
Analytics based on Conceprocity models .......................................................................... 43
5.3
A functional perspective: (Zeng, 2008) ............................................................................. 43
5.4
Some further evaluative comments on concept mapping................................................ 45
5.5
Usage profiles .................................................................................................................... 45
5.6
How and why Conceprocity differs from G-MOT .............................................................. 47
5.7
G-MOT strengths ............................................................................................................... 48
5.8
Conceprocity conceptual data structures ......................................................................... 49
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Knowledge organisation by means of concept process mapping
§6
A critical evaluation of Conceprocity and some suggestions for future work ............................... 51
6.1
The tentative nature of these initial conclusions: further research proposed ................. 51
6.2
More fundamental difficulties and objections .................................................................. 52
6.3
Towards an ontological evaluation of Conceprocity ......................................................... 55
6.4
Learning by enquiry: some parallels with Checkland’s LUMAS......................................... 57
6.5
An application to student learning .................................................................................... 60
6.6
Complementary approaches to concept mapping as part of a mixed-methods
research approach............................................................................................................. 61
“What is the contribution of personal information management systems PIMS to the working model
and personal work system of knowledge workers?” ...................................................................... 61
1.
Conceprocity concept-process maps. Conceprocity CAPRI or CAPRILOPE models are the
result of conscious analysis and specific design by Conceprocity modellers. ............................... 61
2.
Leximancer “fuzzy” concept maps. ................................................................................................ 61
References ................................................................................................................................................... 64
Table of Figures
Figure 1 The Working Model of a knowledge worker. Source: author ......................................................... 7
Figure 2 A concrete model: Kat sitting in Mark's lounge .............................................................................. 9
Figure 3 More general Conceprocity map ................................................................................................... 10
Figure 4 Conceprocity representation of abstract notions and concrete facts .......................................... 12
Figure 5 Conceprocity relationship syntax .................................................................................................. 13
Figure 6 An association between two concepts .......................................................................................... 14
Figure 7 An aggregation relationship .......................................................................................................... 15
Figure 8 A composition relationship ........................................................................................................... 15
Figure 9 Specialisation / generalisation relationship .................................................................................. 16
Figure 10 Regulation relationships for actors ............................................................................................. 17
Figure 11 Regulation relationships for principles........................................................................................ 17
Figure 12 Precedence relationships ............................................................................................................ 17
Figure 13 Basic sequence of events and procedures .................................................................................. 19
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Knowledge organisation by means of concept process mapping
Figure 14 AND logical connector ................................................................................................................. 19
Figure 15 Inclusive OR logical connector..................................................................................................... 20
Figure 16 An example of combining events, procedures and logical connectors ....................................... 20
Figure 17 Summary of Grammar Rules ....................................................................................................... 22
Figure 18 Concepts and procedures which are expanded elsewhere - signified by highlighted border .... 22
Figure 19 The PhD research process of the author represented as a Conceprocity concept <-> process
map.............................................................................................................................................................. 25
Figure 20 A tentative set of types of KOS (from Rocha Souza et al., 2010, FIG 1) ...................................... 40
Figure 21 KOS Spectrum Source: (Daconta et al., 2003) ............................................................................. 41
Figure 22 Levels of ontological precision - (Guarino, 2006) ........................................................................ 42
Figure 23 KOS Spectrum from (Zeng, 2008, p.161) with suggested functional effectiveness .................... 44
Figure 24 Illustrative summary of some of our propositions ...................................................................... 58
Figure 25 Checkland's LUMAS model Source: (Checkland, 2000) ............................................................... 59
Figure 26 Fuzzy concept map of the author's PhD journal produced using Leximancer ............................ 62
Table of Tables
Table 1 Conceprocity Usage Profiles ........................................................................................................... 24
Table 2 How the current research design addresses Delamont’s objections to auto-ethnography. Source:
author .......................................................................................................................................................... 27
Table 3 Knowledge representation according to (Hjørland and Nicolaisen, 2005) with additional
commentary in italics .................................................................................................................................. 32
Table 4 A suggested positioning of Conceprocity and other KOS in Functional Effectiveness terms ........ 44
Table 5 Conceprocity Usage Profiles ........................................................................................................... 46
Table 6 How Conceprocity differs from G-MOT .......................................................................................... 47
Table 7 Conceptual data structures and their associated metadata .......................................................... 49
Table 8 Conceptual Modelling Framework Elements (based on (Wand and Weber, 2002, p.364)) .......... 56
Table 9 Experiments planned or underway in the current research of Mark Gregory ............................... 63
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Knowledge organisation by means of concept process mapping
§1
An introduction to Conceprocity
Conceprocity is an essentially pragmatic approach to the representation and organisation of
explicit knowledge. It is based on the earlier work by the Canadian research centre LICEF
(Paquette, 2010). Version 1.0 of Conceprocity was introduced on 09/05/2013.
Conceprocity – concept <-> process reciprocity – is a visual and textual language and
toolset intended for capturing, expressing, communicating and co-creating models of topic
areas of domain knowledge by domain experts or learners. The modeller decides the
vocabulary and constructs a Conceprocity model in accordance with what can be very
simple grammar rules. If the modeller is prepared to learn a little bit more about
Conceprocity, then more sophisticated semiotics are available to her as she take advantage
of more advanced usage profiles.
1.1
Why model personal knowledge conceptually?
We want to achieve models which are isomorphic with the situation that we are
seeking to regulate or control. Why? (Conant and Ashby, 1970) tell us that:
“The design of a complex regulator often includes the making of a model of
the system to be regulated. The making of such a model has hitherto been
regarded as optional, as merely one of many possible ways… A theorem is
presented which shows, under very broad conditions, that any regulator that
is maximally both successful and simple must be isomorphic with the
system being regulated… Making a model is thus necessary. The theorem
has the interesting corollary that the living brain, so far as it is to be
successful and efficient as a regulator for survival, must proceed, in
learning, by the formation of a model (or models) of its environment.”
Each of us needs to regulate our own working lives. Modelling for action requires
conceptual modelling of the personal work system (Baskerville, 2011) which
embodies:
•
The explicit elements of a person’s personal information
•
Their intentions and means of enacting the knowledge which governs the
derivation and use of that personal information.
and
We need a modelling formalism for concepts and behavioural elements – processes
acting on concepts.
The author’s pragmatic choice is to introduce a new modelling language:
Conceprocity. This modelling language is intended to allow novice and
experienced knowledge mapping workers to create a myriad of models. The
purpose of these models may vary. Sometimes a model will represent a means of
communication between stakeholders in a situation. Sometimes the process of
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Knowledge organisation by means of concept process mapping
modelling will increase the understanding of a complex situation and lead to better
learning by stakeholders.
1.2
Why Conceprocity is important and indeed essential to my
work
Pragmatism (following Pierce as collected by (Hartshorne et al., 1931)) leads us to
suggest :

No model means no regulation and imperfect understanding.

Abductively, we conclude that we all have a personal work system and we must
therefore have a model – but what is that model? Can we surface it, as individuals
and as researchers? Surfacing this model is at the heart of my PhD research.
1.3
Designing your working life: learning how to get things done
better
The act of living our lives involves:
 Your working model: your mental view of who you are and how you act
 Your personal work system: how you act to get things done (Allen, 2003)
 Your personal information management system: how you keep things
found (Jones, 2007)
 Your use of technology
1.4
Simple Conceprocity model
Figure 1 The Working Model of a knowledge worker. Source: author
Easy to criticise – but any model is better than the alternative, which is not none, but
an inexplicit one!
An initial model is refined in use, in action
How you can design a better work system
You can choose between:
 Explicit personal information system design
However, for most of us with our current state of learning this is unlikely to
be an option.
 Serendipitous bricolage: (Ciborra and Jelassi, 1994)
 Guided learning
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Knowledge organisation by means of concept process mapping
My research largely takes the form of action research (design school) or observation
(behaviour school) – see (Hevner et al., 2004). The work of (Hevner and Chatterjee,
2010; Hevner, 2010; Hevner et al., 2004) on design science in IS research, and of
(Wand and Weber, 2002) on conceptual modelling and information systems have
informed both this paper and the design of Conceprocity. To the extent that one
potential use of Conceprocity is to conceptualise and therefore support the design of
target personal information management systems, the design perspective identified
by Hevner and his colleagues is perhaps sometimes appropriate. However, since we
suspect that most personal information management systems are the result of
serendipitous bricolage rather than the product of deliberate design, it is the more
behavioural perspective identified by Hevner which is likely to be significant in the
study of actual personal information management systems.
1.5
Positioning Conceprocity as a Knowledge Organisation
Representation
Concepts may be held both visually and linguistically. Indeed, some hold that there
exists right brain and left brain thinking. This notion has developed from the
late-1960s research of psycho-biologist Roger W Sperry (see for example the
popular presentation (Sperry, 1975)), who discovered that the human brain has two
very different ways of thinking corresponding more-or-less to the two separate
brain hemispheres):

Right brain thinking is visual and processes information in an intuitive and
simultaneous way, looking first at the whole picture then the details

Left brain thinking is verbal and processes information in an analytical and
sequential way, looking first at the pieces then putting them together to get
the whole
There are various approaches that have been developed to represent knowledge in
ways which explicitly exploit right-brain approaches or seek to stimulate right-brain
reactions. These include:

Mind Maps –Tony Buzan (Buzan and Buzan, 1996)

Concept maps – Joseph Novak and collaborators (Novak and Cañas, 2008)
following David Ausubel (Ausubel, 2000, 1963)

Concept maps with typed concepts and relationships: LICEF (Paquette,
2010); (Basque, 2013)

Concept <-> Process maps: Conceprocity: Mark Gregory
(www.markrogergregory.net)
Using both the visual and the linguistic (written and spoken language) stimulates
better understanding of a situation and – perhaps later – better learning.
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Knowledge organisation by means of concept process mapping
All these approaches are based on the active creation by individuals of
specifically-crafted or designed maps of concepts and their relationships. An
alternative and, we believe, complementary approach is discussed below in section
4.4 and included in Table 3.
§2
Conceprocity described
2.1
An example Conceprocity model and how it has been created
 Start with a simple English sentence: “The cat sat on the mat”

Give a specific instance: “The cat called Kat sat on the mat in my lounge”

A concrete Conceprocity map follows:
Figure 2 A concrete model: Kat sitting in Mark's lounge
 Identify concepts, any static relationships and any activities
 Create a specific and a more general model using the meta-concepts (Conceprocity calls
them notions) of concept, procedure and relationship

Consider concrete and abstract representations
 Observe, maybe discuss and then refine the resulting map
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Knowledge organisation by means of concept process mapping

Here we choose to remove the concrete and retain the abstract elements in a
conceptual model of the general situation of creatures acting in a geographical
context
Figure 3 More general Conceprocity map
 The model that results depends upon the viewpoint and the purpose of the modeller – what
(Checkland, 1981) identified as the Weltanschauung of this important participant

A cat specialist (and a cat lover!) will take a different view from an expert in
cognitive science applied to animals

But the process of dialogue and of mutual understanding can be aided by visual
concept mapping and by dialogue around the models
2.2
Conceprocity as a modelling language
A modeller creates an initial model which is then refined (evolved and simplified)

Either alone by the original modeller

Or by means of co-modelling by the original modeller and other modellers who may
be better “wielders” of Conceprocity

Or by exchange between the modeller(s) and domain experts who are not (yet)
Conceprocity modellers
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Knowledge organisation by means of concept process mapping
The aim is to create well-expressed models which enhance both the understanding
of the phenomenon being modelled and the understanding and ongoing learning of
the modeller(s).
It is essential to distinguish between Conceprocity as a modelling language, often
used for co-modelling by persons more or less skilled in knowledge mapping; from
the actual models produced in Conceprocity.
2.3
Conceprocity: Notions
Paquette’s G-MOT and Conceprocity differ from the more usual concept maps of
(Novak and Cañas, 2008) by distinguishing between types (classes) of objects:

Concepts (things, ideas, etc.; these are usable and (sometimes) decidable classes of
knowledge)

Procedures (the means of enacting knowledge in the form of specific activities,
repeatable actions and processes – the latter being templates for repeated actions)

Principles (rules, constraints, permissions; also computer programs – viewed as
concrete expressions of algorithms and an encoding by programmers of knowledge)

Actors (people, organisations, external systems)
Conceprocity goes beyond G-MOT in a number of ways. In particular, it supports
the notions of:

Events: events describe changes of condition or state; they typically characterise the
result of an activity and in turn trigger the next activity

Logical connectors: OR XOR AND NOT
Conceprocity can therefore be used for modelling algorithms and heuristics.
See Figure 4 Conceprocity representation of abstract notions and concrete facts.
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Knowledge organisation by means of concept process mapping
Figure 4 Conceprocity representation of abstract notions and concrete facts
These typed classes (e.g. cat) or instances of objects (e.g. Kat) are related by
relationships or relationship instances (links) which are themselves also typed.
We contend that greater semantic precision permits more expressive and more
meaningful models.
2.4
Representing Conceprocity relationships
Conceprocity relationships very broadly follow UML class diagram conventions
rather than G-MOT ones. This is because the UML conventions are more visually
expressive than the letters used in G-MOT and therefore Conceprocity models can
be made more semantically precise.
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Knowledge organisation by means of concept process mapping
The relationship syntax is:
Symbol
Meaning
Flow of control or of data
Influences, governs, directs…
Is instantiated as
Commentary concerning the diagram.
This relationship is also used for links
between icons, images and sketches
and the concept to which they relate
Figure 5 Conceprocity relationship syntax
Conceprocity relationships are also referred to as notions. Thus notions in
Conceprocity is a word which embraces all of concepts, procedures, actors,
principles, events and relationships.
2.5
Why Conceprocity distinguishes concepts, procedures and
principles
In his book (Paquette, 2010) Gilbert Paquette suggests as a reason for distinguishing
the notions of concepts, procedures and principles the need to address the
weaknesses of existing modelling approaches – such as flowcharts and decision
trees:
1.
2.
3.
4.
Imprecise meaning of the links between the entities that compose the model.
The ambiguity in graphs where objects, actions on objects and statements of properties that
those objects possess are all mixed up and are not represented in a way that helps to
differentiate them and uncover their relationships. Paquette suggests distinguishing classes
of objects as concepts, actions on concepts as procedures and statements of properties as
principles.
The difficulty of combining in one model objects which at a high summary level in the
model need to be developed at a lower level with sub-models whose nature is not the same.
Thus a principle at a high level might need to be developed as a procedural or conceptual
sub-model.
Existing visual representation formalisms have emerged largely from the computer science
and software engineering communities. Formalisms such as Entity Relationship models,
modern structured systems analysis, conceptual graphs (John Sowa (Sowa, 2000, 1984)
following Charles Pierce), the object modelling technique and the successor Unified
Modelling Language UML are all representation approaches which have been built
primarily for the analysis and architectural design of complex software systems. Even to
read such diagrams and the links between them is hard, and to create such models requires
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Knowledge organisation by means of concept process mapping
considerable expertise and an abstraction and conceptualisation capability which may be
lacking among the more general knowledge workers whom Paquette (and I) wish to address
and empower. Paquette states
“Our goal is different. We need a visual representation system that is both simple
enough to be used by educational specialists and learners who are not computer
scientists, yet general and powerful enough to represent the structure of
knowledge and learning/working scenarios. The distinction and the integration of
basic types of knowledge and links in the same language are essential… We
present three major steps starting with (1) informal visual modelling for the
educated layperson, to help represent interesting knowledge. We then (2) move
onto semi-formal modelling to help define target competencies and activity
scenarios for knowledge and competency acquisition by learners and workers.
Finally (3) we present the more formal visual models (Ontologies) that can be used
by software agents to ensure execution of knowledge-based processes on the
semantic web.” [(Paquette, 2010, p.xiv) slightly amended for clarity.]
2.6
Conceprocity relationships
2.6.1 The types of relationships in Conceprocity
Here we largely follow G-MOT.
 Association: simple connection
 Aggregation: is-a, is-made-of independent parts
 Composition: is-a, is-made-of dependent parts
 Specialisation / Generalisation: kind-of
 Regulation: controls, directs, influences…
 Precedence: comes-after, comes-before…
 Entrant-Product (Input-Output): causes, gives rise
to…
 Instantiation: is an example (instance) of…
Grammar Rules govern the valid types of links that may join the knowledge types
2.6.2 Associations
The Association link (G-MOT: A) is simply an untyped connection between concepts. By
untyped, we mean that the modeller either does not yet know the type of the relationship or
is not yet capable of deciding its more precise type.
Figure 6 An association between two concepts
2.6.3 Aggregations
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Knowledge organisation by means of concept process mapping
The Aggregation link (G-MOT: G) associates multiplicity – ordinality or cardinality – with
a relationship.
Aggregation is an extension of the G-MOT model.
It is essential in data modelling in accordance with the relational model of (Codd 1970).
The Aggregation link (G-MOT: G) is a kind of association which says that one concept is
part of another, together with others of the same type, so that all the parts are together a
group of parts which constitute a whole concept: a part-whole relationship. Aggregation is a
special type of association used to model a "whole to its parts" relationship. In basic
aggregation relationships, the lifecycle of a part class is independent from the whole class's
lifecycle. This is in contrast with composition, which follows.
Figure 7 An aggregation relationship
2.6.4 Compositions
The Composition link (G-MOT: C) also connects a knowledge (object) with one of its
constituents or its constitutive parts. The composition aggregation relationship is just
another form of the aggregation relationship, but the child class's instance lifecycle is
dependent on the parent class's instance lifecycle.
Thus a table is composed of legs and of a flat surface. Similarly, a chessboard is composed
of squares, each of which may be black or white.
Figure 8 A composition relationship
2.6.5 Multiplicities, cardinality and ordinality
We can ascribe a multiplicity to either or both ends of an association, an aggregation or a
composition. This multiplicity can be:
 An exact number
 A range of numbers, separated by two dots
 An arbitrary unspecified number represented as
an asterisk *
 Example multiplicities:
 1
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Knowledge organisation by means of concept process mapping
 1..1
 0..1
 1..*
 0..*
 3..4 – e.g. number of legs on a stool
 0..0 – this means that there is NO relationship
 Cardinality and ordinality distinguished
Cardinality specifies the maximum number in relationships and ordinality specifies the
absolute minimum number in relationships. When the minimum number is zero, the
relationship is usually called optional and when the minimum number is one or more,
the relationship is usually called mandatory.
2.6.6 Specialisation and Generalisation
One knowledge type may be a particular case of another knowledge type: a sort-of
relationship exists between the two concepts. This is termed specialisation. Conversely,
reading the other way between the two concepts, we recognise that the first concept is a
generalisation of the second. Thus a table is a specialisation of furniture. One type of
furniture is a table; there are of course others.
Figure 9 Specialisation / generalisation relationship
2.6.7 Regulation
The regulation link exists to enable certain more complex links to be expressed:
In conjunction with CONCEPTs: Here the principle defines some constraints that must be
satisfied or establishes a law or a relation between two or more concepts
In conjunction with a PROCEDURE OR ANOTHER PRINCIPLE: Here the principle
controls or governs the execution of a procedure or the selection of other principles.
A regulation link can be used from a principle or actor towards another abstract knowledge
which can be a concept, a procedure or another principle.
A regulation link between two concepts defines some constraints that must be satisfied or
establishes a law that governs the relationship.
A regulation link from a principle to a procedure or to another principle controls or governs
the execution of a procedure or the selection of other principles.
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Figure 10 Regulation relationships for actors
Figure 11 Regulation relationships for principles
2.6.8 Precedence
Figure 12 Precedence relationships
The precedence link connects two procedures or principles, the first of which must be ended
or estimated before the second begins or can be applied. For example: prepare an outline
precedes write a text.
2.6.9 Entrant-Product
This relationship, which is broadly equivalent to input/output, is used to identify concepts
that are entrants to a procedure (the arrow goes from concept to procedure) or a product of a
procedure (the arrow goes from the procedure to the concept). For example, outline is an
entrant to write a text; text is the product of write a text.
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Knowledge organisation by means of concept process mapping
A procedure may either be defined entirely informally, or it may have a more or less formal
definition. If it is expressed sufficiently formally, for example in a programming language,
it can be viewed as a function which transforms its entrants into its products.
2.6.10
Instantiation
This relationship connects an abstract knowledge to a concrete one. Abstract concepts have
as their concrete equivalent facts. Abstract procedures have as their concrete equivalent
traces. Abstract principles have as their concrete equivalent statements.
2.6.11
Labelling relationships in Conceprocity
One form of label which is commonly used in Conceprocity is that of multiplicity.
Conceprocity also permits relationships to be labelled with text but it doesn’t insist that that
be the case. In fact the most common use of labels in relationships is in the simple usage
profile of Conceprocity where the only relationship type which is supported is Association.
There is a basic issue with labelling relationships, which is that it is often necessary to label
a relationship twice - once in one sense and once in another.
For this and for other reasons, Conceprocity tends to prefer the use of more notions – often
procedures – to make clearer the relationship between, for example, two concepts. An
intermediate procedure can be a function which transforms one concept to another. Then if
concepts, procedures and principles are well named, there is no additional value in labelling
relationships.
2.7
Events and Logical Connectors
Conceprocity goes beyond G-MOT in recognising the notions of event and logical
connector. An event is a named change of state, typically represented in English
using a past participle such as arrived or departed. Typically, events are the result of
one procedure and trigger another; this is the case for P1 in the diagram which
follows.
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Figure 13 Basic sequence of events and procedures
In Conceprocity, events must be preceded and followed by either a procedure or a
logical connector. The logical connectors available in Conceprocity are
Exclusive-Or, Inclusive-Or and And.
Figure 14 AND logical connector
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Knowledge organisation by means of concept process mapping
Figure 15 Inclusive OR logical connector
Figure 16 An example of combining events, procedures and logical connectors
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Knowledge organisation by means of concept process mapping
2.8
A summary of Conceprocity grammar rules
(Rosemann and Green, 2002) discuss how Wand and Weber have developed a
series of models based on the ontological theory of Mario Bunge; they named these
models the Bunge-Wand-Weber BWW models. The BWW models have as their
intent the formalisation of modelling techniques. Rosemann and Green identify two
particular criticisms of the models, one being the understandability of the constructs
within those models and the second being the difficulty in applying the models to
specific modelling techniques. They propose a meta-model of the BWW constructs
using a meta-language that is in effect an extended entity relationship model. Their
justification for that choice is that the entity relationship model is already very
familiar to many Information Systems professionals. Furthermore the extended
entity relationship model is the meta-language by which (Scheer, 2000) has
modelled and formalised his
(Paquette, 2010) and G-MOT set out certain grammar rules which together
constrain and add meaning to concept process models.
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Knowledge organisation by means of concept process mapping
Figure 17 Summary of Grammar Rules
2.9
Structuring Conceprocity maps
2.9.1 The need for multi-level models
Conceprocity allows a knowledge model to be developed at several levels. The principal
(top level) model gives a general overview of the domain. The subordinate models add a
new level of detail, more and more specific to the representation of the domain: an
expansion. Each level of representation should be simplified so as to retain only the
knowledge essential at that level. By this means the benefits of encapsulation – low
coupling and high internal cohesion – can be realised in the context of knowledge
representation. In Conceprocity, the existence of an expansion of Concepts and Procedures
is signalled by a highlighted border. The notion is then linked to another page in the same
model or to another model.
Figure 18 Concepts and procedures which are expanded elsewhere - signified by highlighted
border
2.9.2 Structuring a model
A Level 1 diagram is potentially supported by several Level 2 diagrams. Each Level 2
diagram might be supported by several Level 3 diagrams. There is but one Main Model and
a series of nested models stemming from the Main Model.
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2.10 Conceprocity Usage Profiles
A Usage Profile is a named usage of Conceprocity. These various usage profiles
require few or no extensions to the Conceprocity basic notation which is richly
expressive. It is possible and desirable to start with a beginners’ profile “Simple
concept mapping for beginners”, in which the only available relationship is
association and no use is made of principles, and only then to move on to typed
relationships and principles. In particular in this profile, we place strong emphasis
on the use of sketches, icons and images.
2.11 Learning to use Conceprocity: moving on from the beginner’s
profile
Conceprocity is cloud-based and essentially multi-user. It also provides implicit
support for the notion of usage profiles. A Usage Profile is a named usage of
Conceprocity.
The various usage profiles require few or no extensions to the Conceprocity basic
notation which is richly expressive.
It is possible and desirable to start with a beginners’ profile “Simple concept
mapping for beginners”, in which the only available relationship is association and
no use is made of principles, and only then to move on to typed relationships and
principles. Particularly in this profile, we place strong emphasis on the use of
sketches, icons and images.
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Table 1 Conceprocity Usage Profiles
Conceprocity Usage Profile
Simple concept mapping for beginners
Makes use of a deliberately restricted range of Conceprocity objects. In particular,
the only relationship type supported is association. In order to give more
expressiveness, this profile permits association relationships to be named.
Knowledge mapping
Very general with the full range of Conceprocity objects. Relationships should not
normally be named. Instead, the nature of the two notions linked by a typed
relationship should normally provide full context sufficient to make the meaning of
the relationship clear. Where this is not the case, Conceprocity permits
commentary/notes.
Typical uses include: self-observation, research design, representing knowledge
as-is and as-ought, demonstrating understanding, documenting a body of
knowledge, design and delivery of teaching (e.g. to act as the “advance organiser”
or signposting originally suggested by (Ausubel, 2000)), learning and evaluation,
representation of algorithms and of heuristics
Use case diagrams
Event-driven process chains
Data (entity-relationship) models
System architecture and components
Taxonomy creation and maintenance
Conceprocity is not currently intended for the representation of full ontologies; it
can however be used effectively to represent taxonomies.
Causal loop diagrams (Eden, 2004)
2.12 Conceprocity for the Right Brain
As currently implemented, Conceprocity is a Lucidchart application (see
https://www.lucidchart.com/). Therefore Conceprocity makes it easy to include
visual elements because Lucidchart does so. Beyond Conceprocity’s own symbols,
we can include images and icons. The modeller can either locate these for herself, or
she can use the built-in Google Images search.
Sketches – less formal diagrams – sometimes have a role, particularly in the early
development or the informal presentation of a model (especially during whiteboard
sessions). This is the way in which a concept process model can include and
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Knowledge organisation by means of concept process mapping
embrace rich pictures or elements of a rich picture. Rich pictures were originally
introduced by Peter Checkland (Checkland, 1981); see also (Checkland and
Tsouvalis, 1997). We note that the recent widespread use of tablet computers makes
it much easier to create such sketches and then to incorporate them in Conceprocity
models. We note too hat sketches can be created using pen and paper and then
captured digitally as images: the person responsible for the sketch takes a
photograph of the outcome using her smartphone or tablet.
2.13 Specific PhD research process as a Conceprocity concept map
Figure 19 shows the author’s current PhD research process:
Figure 19 The PhD research process of the author represented as a Conceprocity concept <->
process map
This map is by no means the only possible conceptualisation possible of the PhD
work. Furthermore, it can easily be criticised on multiple grounds. But the very fact
of there being such a model enables dialogue and offers evaluative possibilities.
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§3
The role of Conceprocity in the PhD research of Mark Gregory:
some criticisms and the ways in which they are addressed in
the research design
3.1
Why Conceprocity is important in my PhD research
Conceprocity is a semi-formal visual knowledge representation language which
enables and encourages the modeller to be more precise in defining, bounding and
relating conceptual and procedural knowledge.
It is in effect a means to constrain and enhance natural language expression and
thereby to increase the precision of the meaning which the modeller seeks to
express.
To the extent to which two modellers can agree upon a Conceprocity model, it is
also a means to establish and to verify communication of ideas and concepts.
It or something like it is essential to completing my PhD!
My use of concept maps is motivated by the following felt needs:


Structuring my understanding of the published work of others. For examples, see
(elsewhere) my concept maps concerning the work of (Weick et al., 2005) on sensemaking
and of (Polya, 1988) as introduced by (Macgilchrist, n.d.) on heuristics.
Planning my PhD research, which has conceptual and process elements.
The main initial Conceprocity test use case is in fact work system modelling,
particularly personal work system modelling. This is because the immediate
application of Conceprocity will be to help me as the research monitor, in parallel
with research volunteers, to model their and my knowledge management in the
action research phase of my PhD research.
3.2 The challenge according to David Weir
My director of studies, Prof. David Weir has challenged me thus:
“I see this in a way as verging on the Autoethnographic:
http://www.qualitative-research.net/index.php/fqs/article/view/1589/3095
and thus susceptible to a special kind of critique about self-based knowledge,
referentialism etc. Thus:
"Efforts at self-revelation flop not because the personal voice has been used,
but because it has been poorly used, leaving unscrutinized the connection,
intellectual and emotional, between the observer and the observed” (Berar)
Quoted in (Spry, 2001) at http://eppl604.wmwikis.net/file/view/spry.pdf
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So can Conceprocity function in such a way as to avoid critiques like those of
(Delamont, 2007)1? ”
3.3 My response to David Weir’s challenge
(Spry, 2001, p. 710) quotes (Denzin, 1992): “autoethnography is a radical reaction
to realist agendas in ethnography and sociology… which privilege the researcher
over the subject, method over subject matter, and maintain commitments to
outmoded conceptions of validity, truth, and generalizability” (p. 20)”. She goes on
to quote Ruth Behar, working from the writings of George Devereux as she asserts:
“What happens within the observer must be made known, Devereux
insisted, if the nature of what has been observed is to be understood” (Behar,
1997, p.6).
We can summarise (1) Sara Delamont’s arguments (Delamont, 2007) against
auto-ethnography, which she views as pernicious and lazy and (2) the extent to
which the use of Conceprocity and the overall research design might help as in
Table 2 which follows:
Table 2 How the current research design addresses Delamont’s objections to auto-ethnography.
Source: author
Objection to auto-ethnography
1. Auto-Ethnography cannot fight familiarity
– it is hard to fight familiarity in our own
society anyway even when we have data.
2. Auto - Ethnography is almost impossible to
write and publish ethically.
3.
Research is supposed to be analytic not
1
Response
We prefer the use of the phrase “structured
self-observation” (Rodriguez and Ryave,
2002) to auto-ethnography. We do this to
emphasise the fact that we are not here
talking about self-revelatory writing as
associated with the work particularly of
Carolyn Ellis or illustrated by that of Ruth
Behar. In addition, the results of this
structured self-observation are in no way
regarded as true in an ontological sense.
Instead, they are intended as a particular
exemplar. Any very tentative findings or
suggestions of ways forward find their way
into the working documents associated with
this research. Those working documents are
intended to be refined in action research in
which the experience of a number of
research volunteers are individually
expressed and then, perhaps, to a certain
degree synthesised. Conceprocity gives
shape and a degree of objectivity to these
individual and collective expressions.
The construction of a Conceprocity model is
See http://www.leeds.ac.uk/educol/documents/168227.htm and
http://www.cardiff.ac.uk/socsi/qualiti/QualitativeResearcher/QR_Issue4_Feb07.pdf
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Knowledge organisation by means of concept process mapping
merely experiential. Autoethnography is all
experience, and is noticeably lacking in
analytic outcome.
4.
It focuses on the wrong side of the power
divide.
5.
It abrogates our duty to go out and collect
data: we are not paid generous salaries to sit
in our offices obsessing about ourselves.
Sociology is an empirical discipline and we
are supposed to study the social.
6.
Finally and most importantly ‘we’ are not
interesting enough to write about in
journals, to teach about, to expect attention
from others. We are not interesting enough
to be the subject matter.
a significant mental exercise which is
inherently and intensely analytical.
Although this does not in and of itself
prevent an experiential and self-centred
approach, it makes it less likely.
Subsequently the model can be refined in
discussion between an individual and a peer
or mentor, and the outcome is at the very
least a surfaced and carefully-expressed
model.
Concerning autoethnography or structured
self-observation in personal information
management, I genuinely doubt that any
power-divide issues arise.
In connection with action research, there is
indeed a very serious danger associated with
the unbalanced power relationship between
research volunteer and research mentor and
the prejudices of the latter. However, the
research design emphasises and seeks to
promote the establishment of a forum-based
community of practice in which the voice of
the research mentor will be but one of many.
In this research design, structured
self-observation is but one of a number of
complementary data-collection techniques.
The others are textual analysis of the
existing corpus of literature concerning
personal information management systems,
and explicitly-authorised analysis of the
writings of research volunteers. We make
absolutely no pretence to statistical validity.
Instead we are particularly concerned in this
research to look at certain outliers - see
(Boisot and McKelvey, 2010). These
outliers are experts in personal information
management systems who may even have a
vanguard role to play in the establishment of
this small field of academic enquiry.
Certainly. There is no danger that anyone’s
individual experience will find its way into
published work.
3.4 Renaud Macgilchrist’s challenge
According to my colleague Renaud Macgilchrist, I need clearly to distinguish
between
1.
Models as pseudo-formal languages systems – e.g. predicate logic, semantic nets, frames,
objects, UML and the like – even Prolog;
and:
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2.
The infinite range of specific models, instances of models, which can be constructed using
those language systems.
I need to set out a Table of Criteria for choosing a language system which gives a
weighted evaluation of the various alternative language systems.
3.5
My response to Renaud Macgilchrist’s challenge
The need to distinguish between modelling language systems and their use in
expressing specific models is addressed in Table 7.
An initial table of criteria for choosing a language system appears as Table 4.
The remainder of this paper can be regarded as my more general response to this
challenge.
3.6
Recap: why are concept maps essential to this Ph.D.
research?
Concepts and their relationships have been recognised as central to knowledge and
understanding since the days of Plato and Aristotle. More recently, as highlighted
by (Hjørland, 2009, p.1519):
“Thomas Kuhn (1922–1996) … developed a theory of concepts that
corresponds with his theory of paradigms and that has been considered an
important contribution to concept theory. This connection between
“paradigms” and “concepts” is the point of departure for the present article.
An important view of concepts today can be said to be “post-Kuhnian” in
the sense that it is recognized that different theories and “paradigms” may be
considered the most important mechanism for the development of concepts.
However… different “paradigms” do not totally replace each other but exist
together and compete with each other in all domains all the time (see, e.g.,
Mayr, 1997, pp. 98–994). These criticisms are the reason for using the term
“post-Kuhnian” rather than “Kuhnian” in the present article. The term “postKuhnian” should not, however, be seen as an indication that the underlying
view is primarily taken from Kuhn. There are perspectives, such as
pragmatism, activity theory, and hermeneutics that are both older and have
played a greater role for the views developed in the present article.”
My own stance here is both pragmatic and pragmatist. I can illustrate the pragmatic
by drawing an analogy between my work here on personal information
management systems within the context of personal knowledge work and that of
librarians and information scientists in cataloguing and classifying knowledge made
explicit as books. It is no coincidence that the majority of serious academic writing
about concept mapping is associated with library and information science (but also
with cognitive science). Writing a Ph.D. about personal information management is
an exercise in personal knowledge management. Much of social science concerns
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Knowledge organisation by means of concept process mapping
itself with constructs and their relationships. In areas of enquiry such as that of
personal information management systems - where I hold that the understanding of
the field of enquiry is as yet too imprecise and incomplete to admit of the possibility
of traditional positivist research - what is instead necessary is to identify and to map
out concepts. Thus pragmatically I need to carry out concept mapping. The
prototype pragmatist, Charles Peirce, created what he called existential graphs and
what (Sowa, 1992) has more recently renamed conceptual graphs. A conceptual
graph (CG) is a graph representation for logic based on the semantic networks of
artificial intelligence and existential graphs. Conceptual graphs are admirably
precise - they can be directly transposed into the RDF semantic Web knowledge
representation. I prefer concept maps because it is possible to start from the
informal stance adopted by people who are not specialists in logic or computer
science and then gradually, often by means of dialogue or even by dialogic
mentoring, gradually to refine what is understood into ever more precise knowledge
maps. These too can be formalised and directly transposed into RDF and OWL if
that is appropriate. It is not appropriate when the primary purpose of concept maps
is to attempt to give greater precision to the sometimes essentially imprecise or
ambiguous notions partially and incompletely understood by individual knowledge
workers. Thus pragmatically I have preferred Conceprocity concept and process
maps to more formal knowledge representation techniques.
§4
Ways of organising personal knowledge and data
4.1
Systems thinking and modelling
(Stowell and Welch, 2012, p. xiv) identify as the basic building blocks of systems
thinking (1) emergence, (2) hierarchy, (3) communication and (4) control. They
discuss how a system is defined from the perspective of an observer, who chooses to
draw a boundary reflecting a field of interest and giving to the system so defined a
name. They remind us of the taxonomy of three systemic models originally
identified by Russell Ackoff (Ackoff et al., 1962) and they extend it with a fourth
following Brian Wilson (Wilson, 1984) to yield:
1. An iconic model is a model of reality, the properties of which equate to
those of the real article such that (albeit on a different scale) the model can
be expected to behave in the same way as the real thing. I would give as an
example of such a model the wind tunnel model of a new aircraft.
2. An analogical model is an attempt to simulate the behaviour of the original
although its physical appearance is quite different to that of the original.
Most simulation models fall into this category.
3. An analytic model is created from mathematical or logical relationships that
are believed to lead to the behaviour of some situation of interest. Typical
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Knowledge organisation by means of concept process mapping
examples include spreadsheet models. Analytic models may subsequently
provide the data for analogical models.
4. A conceptual model includes pictures or symbols which are used to
represent the subjective and qualitative aspects of a situation.
(Stowell and Welch, 2012) present modelling as a kind of surrogate representation
of some situation. It is in the process of forming, reforming and structuring that
model that we begin to learn about the situation of interest and its similarities and
differences to the situation that we are modelling. Among the dangers inherent in
such modelling are that it becomes an end in and of itself. Instead a model is only an
abstraction of our perception of reality. As a simplification it is also often
subjective.
Conceprocity enables the creating and maintenance of shared conceptual models.
4.2
A Wikipedia introduction to Knowledge Organisation
Wikipedia http://en.wikipedia.org/wiki/Knowledge_organization accessed
12/06/2013 suggests:
“The term knowledge organization (KO) (or "organization of knowledge",
"organization of information" or "information organization") designates a
field of study related to Library and Information Science (LIS). In this
meaning, KO is about activities such as document description, indexing and
classification performed in libraries, databases, archives etc. These activities
are done by librarians, archivists, subject specialists as well as by computer
algorithms. KO as a field of study is concerned with the nature and quality
of such knowledge organizing processes (KOP) as well as the knowledge
organizing systems (KOS) used to organize documents, document
representations and concepts.
The leading journal in this field is Knowledge Organization published by
the International Society for Knowledge Organization (ISKO).
Simple Knowledge Organization System (SKOS) is a W3C
recommendation designed for representation of thesauri, classification
schemes, taxonomies, subject-heading systems, or any other type of
structured controlled vocabulary. SKOS is part of the Semantic Web family
of standards built upon RDF and RDFS, and its main objective is to enable
easy publication and use of such vocabularies as linked data. See
http://en.wikipedia.org/wiki/Simple_Knowledge_Organization_System.”
4.3
Schema representation
(Paquette, 2010) discusses the relationship between structured knowledge
representation and learning, which he sees as being inextricably linked. Thus
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Knowledge organisation by means of concept process mapping
understanding is impossible without identifying and classifying objects and ideas
and linking them by association in some organised way. These mental structures or
schemas vary in complexity. The concept of schema as the building block of mental
structures is now well established in cognitive psychology. The language and the
thinking derive initially from the work of Jean Piaget (Inhelder and Piaget, 1955),
who discussed the meta-concepts of schema, structure, strategy and operation to
describe cognitive processes. According to Piaget, growth of the intellect is
achieved through increasingly logical, numerous and complex schemas. Such
schemas play a central role in the construction of knowledge which in turn is
essential to the learning process.
“Learning is a process by which a representation of a certain knowledge
representation is transformed into another representation of that knowledge.
Learning is a process, whereas the representation of knowledge is both the
starting point and result.” (Paquette, 2010, p. 15)
The G-MOT (and therefore Conceprocity) representation system is based on the
theory of schemas. We distinguish between two broad categories of schemas, these
being declarative or conceptual; and procedural. The first category involves data
while the second includes the procedures and methods used in processing data in
order to organise information. We also follow Paquette in recognising a third
category of conditional or strategic schemas which consist of principles having one
or more conditions that describe context and conditional sequences. Those
conditions can either be embedded in principles (in both G-MOT and Conceprocity)
or they can be made explicit in the form of logical connectors attached to events
(Conceprocity only).
4.4
Knowledge Representation
(Hjørland and Nicolaisen, 2005) discuss knowledge representation. They remind us
that “Knowledge representation is thus depending both on the objective pole: what
knowledge exists to be represented and on the subjective pole: the representator or
selector.” (Hjørland and Nicolaisen, 2005).
We can summarise their findings as in Table 3:
Table 3 Knowledge representation according to (Hjørland and Nicolaisen, 2005) with additional
commentary in italics
Framework
Technique
Characteristics
AI: symbol
representation and
manipulation
Logic based representations
Declarative sentences and inferencing. Comment: We
would suggest that propositional calculus, predicate
calculus, first order logic and Horn clauses (as used in
Prolog) fall within this category.
AI: symbol
representation and
Procedure based
The meaning of a knowledge base is in its use.
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Knowledge organisation by means of concept process mapping
Framework
Technique
Characteristics
manipulation
representations
AI: symbol
representation and
manipulation
Frame based representations
AI: artificial neural
networks
“Frame-based systems are knowledge representation
systems that use frames, a notion originally introduced by
(Minsky, 1975) as their primary means to represent
domain knowledge. A frame is a structure for representing
a concept or situation such as "restaurant" or "being in a
restaurant". Attached to a frame are several kinds of
information, for instance, definitional and descriptive
information and how to use the frame. Frames are
supposed to capture the essence of concepts or
stereotypical situations, for example going out for dinner,
by clustering all relevant information for these situations
together. This means, in particular, that a great deal of
procedurally expressed knowledge should be part of the
frames. Collections of such frames are to be organized in
frame systems in which the frames are interconnected.”
(Hjørland and Nicolaisen, 2005)
Parallels are drawn between neural nets and behaviourism.
There is an emphasis on noting stimulus and response in an
empiricist tradition and comparatively little interest in
what is happening within the black box. Feedback and/or
feedforward are emphasised.
“The statistical approach to AI involves taking
very large corpora of data, and analyzing them in
great depth using statistical techniques. These
statistics can then be used to guide new tasks. The
resulting data, as compared to the
knowledge-based approach, are extremely
shallow in terms of their semantic content, since
the categories extracted must be easily derived
from the data, but they can be immensely detailed
and precise in terms of statistical relations.
Moreover, techniques - such as maximum entropy
analysis - exist that allow a collection of statistical
indicators, each individually quite weak, to be
combined effectively into strong collective
evidence. From the point of view of knowledge
representation, the most interesting data corpora
are online libraries of text. Libraries of pure text
exist online containing billions of words; libraries
of extensively annotated texts exist containing
hundreds of thousands to millions of words,
depending on the type of annotation. Now, in
2001, statistical methods of natural language
analysis are, in general, comparable in quality to
carefully hand-crafted natural language analyzers;
however, they can be created for a new language
or a new domain at a small fraction of the cost in
human labor”
Statistical analysis of
large corpora of data
(Davis, 2001, p.8133)
Large corpora of data may be approached by methods
related to empiricism, which seems to be what Ernest
Davis is suggesting. There is an important difference,
however, between traditional empiricist approaches to
knowledge representation and “text corpora” approaches.
The traditional approach represents what is considered
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Knowledge organisation by means of concept process mapping
Framework
Technique
Characteristics
knowledge by the person doing the representation. There is
only one voice present. In large corpora of texts many
voices are present (what kind of voices varies according to
how the text corpus is selected, e.g. if it consists of
newspapers or scholarly papers).
Author’s comment: textual analysis tools such as
Leximancer are capable of analysing large text corpora
and summarising their findings in the form of concept
maps. The remark concerning “many voices” is valid and
important. For this reason it is pragmatically desirable to
subset large text corpora and to analyse them separately
as well as together.
Semantic networks
Involve nodes and links
between nodes. The nodes
represent objects or contents.
(Davis et al., 1993) discuss knowledge representation. Randall Davis and his
co-authors make a clear distinction between what they call reasoning and
representation, which they point out are intertwined in many knowledge
representations such as Minsky’s frames. They suggest user-supplied axioms,
theorems and lemmas as parts of a logic-based approach to reasoning.
“The good news here is that by remaining purposely silent on the issue of
recommended inferences, logic offers both a degree of generality and the
possibility of making information about recommended inferences explicit
and available to be reasoned about in turn” (Davis et al., 1993, p.26)
(Davis et al., 1993) do considerable service, inter alia by emphasising the necessity
for triggers and procedural elements in knowledge representation and they point out
that these are implicit in Minsky’s frames. However, we regard as inadmissible their
suggestion of logic as a programming language – as proposed by (Kowalski, 1974) since this approach is inaccessible to the large majority of knowledge workers.
Furthermore, we are making no suggestion that Conceprocity should develop in the
direction of machine execution. Instead, the emphasis is very much on enabling
ordinary knowledge workers, perhaps mentored or working collaboratively, to
achieve real understanding and learning about the situation that they are facing.
4.5
Personal Information Management System PIMS Data
Structures
We go on to look at approaches to structuring data as a necessary precursor to
knowledgeable derivation of information.
4.5.1 Standard approaches to data and information management for
individuals and small virtual teams
An earlier paper (Gregory and Norbis, 2008) presented the hypotheses that individuals
working in groups should be encouraged and educated to make better use of the available
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Knowledge organisation by means of concept process mapping
tools for information management and that the tools themselves should evolve into (or be
replaced by) better ways of representing information and knowledge. See also (Sauermann
et al., 2005).
Our earlier paper discussed the representation of personal data, suggesting that the ways in
which data is stored on a computer influence how it can subsequently be used. We therefore
identified several possible, or candidate, data representation approaches and analysed the
consequences of choosing them. We suggested a classification scheme for tools based
primarily on their data representation. We here reproduce and enhance the suggested
categories.
4.5.2 Spreadsheets
Spreadsheets consist of an array of cells, each of which can store a value or a formula. A
formula relates the value of the current cell to other cells which can be considered as
exporting their value to be used in the formula.
Spreadsheets are a very powerful combination of the nearest approach to widely available
end-user computer programming so far invented; and ways of storing (more or less)
structured data in which the relationship between items of data is imposed by the use of
formulae.
The paper (Gregory and Norbis, 2008) introduced the idea of what it called a functional
spreadsheet which simplifies and restricts the scope of spreadsheets to make them capable
of formal representation. The idea was based on an insight documented in (Peyton Jones
et al., 2003). In the suggested functional spreadsheet approach, rows are hierarchically
organized in an outline that groups and sub-groups the data. Cells are limited to contain
only values (such as text labels, dates and numbers). Column and or row headers may
contain the names of functions which may be applied either to all the values in a column or
row; or to all the values in a group or sub-group defined as a hierarchical outline.
4.5.3 Relational databases, in which all data is stored as relations or
sets
Databases generally have a more limited remit which they fulfil with greater precision than
do spreadsheets. The most widely accepted, implemented and used type of database is the
so-called “relational” database (Date 2003). He suggests as an informal initial definition
that
“
A relational system is one in which the data is perceived by the user as tables (and
nothing but tables); and the operators at the user’s disposal (e.g. for data retrieval)
are operators that generate new tables from old. For example, there will be one
operator to extract a subset of the rows of a table, and another to extract a subset of
the columns – and of course a row subset and a column subset of a table can both
be regarded as tables themselves. The reason such systems are called ‘relational’ is
that the term ‘relation’ is essentially just a mathematical term for a table.
”
4.5.4 Outliners
An outline is a hierarchical way to display related items of text to graphically depict their
relationships. Outlining is a technique which may be implemented in general office
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programs or in specific computer programs known as “outliners”. An outliner is a program
which stores and depicts outlines: a special text editor that allows text to be structured as an
outline. Outliners are typically used for computer programming, collecting or organizing
ideas, tasks or even project management. Outlining is the technique widely used in
programs such as Microsoft Office PowerPoint, in which the main headings of a
presentation appear as separate slides and on each slide appear points and sub-points. The
same technique is available in a more powerful but perhaps less widely-used form in word
processing packages such as Microsoft Office Word, which supports a very useful and
underused Outline mode.
In an outline, a data item is given meaning by being shown in its owning hierarchy. Thus a
person’s surname is a component of a composite Contact object. The relative positioning of
an item conveys meaning in that the label of the owner classifies or otherwise gives
contextual information concerning the owned item; and the depth in the hierarchy gives
some idea of the relative importance or significance of the item.
Some programs allow a data item to participate in more than one hierarchy. Thus for
example an appointment for a meeting can appear in an overall agenda or calendar, but also
be linked to the name of each participant in the meeting. Effectively, the same datum is
classified in more than one way. To the extent that knowledge is a product of the
recognition by intelligent agents of connections between information otherwise not
explicitly linked, this kind of tool can be used as a mechanism for storing relatively
unsophisticated knowledge.
4.5.5 Spreadsheets as a basis for databases
An exciting new commercial approach to the construction of database-enabled websites is
the STOIC platform proposed by http://sutoiku.com/. Conversation with the founder of this
company, Ismael Chang Ghalimi, suggests that reflection on personal information
management was part of the motivation for STOIC. STOIC turns spreadsheets into
complete applications, with a cloud-based relational database and a mobile user interface.
An application is created as a new spreadsheet. An object is then a new worksheet. To add a
field to an object, it is necessary to add a new column. To add a record to an object, simply
add a new row.
4.5.6 A possible classification of PIMS conceptual data structures
I suggest the following initial classification; this is partially corroborated by (Völkel and
Haller, 2009):



Natural language and text
Tables
Spreadsheets and functional spreadsheets
Spreadsheets add functional programming capability to data tables




Hierarchical Outlines
Relational databases
Linking and multiple classification (Tagging)
Graphs and graph theory
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Knowledge organisation by means of concept process mapping

Concept maps
Formally, concept maps are graphs; the objects are nodes and the relationships are
edges. The topic is extensively discussed by (Friedman and Smiraglia, 2013).

Mathematical models, statistics and matrix
manipulation
Here there are a number of candidate data approaches, including categorical data
analysis and clustering.


Specific PIM (Personal Information Management)
programs
Semantic web and web science
Other approaches include object oriented databases, XML documents, RDF and OWL
See (Davies et al., 2006).
Two possibilities exist when applying semantic web approaches to personal
information: either specialist PIM software or services which incorporate semantic
web techniques; or systems which apply semantic web techniques to pre-existing data
stored on a specific computer. The latter approach is referred to as the semantic
desktop (Sauermann, 2005; Sauermann et al., 2005).
Enhancing the usability and usefulness of the Web and its interconnected resources
might be achieved by:
1.
2.
3.
Servers which expose existing data systems using the RDF and SPARQL
standards. Many converters to RDF exist from different applications.
Relational databases are an important source. The semantic web server
attaches to the existing system without affecting its operation.
Documents “marked up” with semantic information (an extension of the
HTML <meta> tags used in today’s Web pages to supply information for
Web search engines using web crawlers). This could be
machine-understandable information about the human-understandable
content of the document (such as the creator, title, description, etc., of the
document) or it could be purely metadata representing a set of facts (such as
resources and services elsewhere in the site). (Note that anything that can be
identified with a Uniform Resource Identifier (URI) can be described, so the
semantic web can reason about animals, people, places, ideas, etc.) Semantic
mark-up is often generated automatically, rather than manually.
Common metadata vocabularies (ontologies) and maps between vocabularies
that allow document creators to know how to mark up their documents so that
agents can use the information in the supplied metadata (so that Author in the
sense of ‘the Author of the page’ won’t be confused with Author in the sense
of a book that is the subject of a book review).
 A very important issue: whose ontology?
If we accept the necessity for imposing some sort of classification mechanism to
achieve accuracy and precision in searching for information, the next question
which inevitably arises is “whose ontology shall we adopt?” We can identify three
broad and overlapping alternatives:
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Knowledge organisation by means of concept process mapping
 Standardisation by committee (or by
professional body, or by employer): top-down
imposition
This is frequently done within communities of experts, such as pharmacists or
medical practitioners.
 Emergent ontology - ontologies shared
between workers in small, often virtual,
groups: bottom-up conceptualisation
This situation is common in areas of fast-changing technology or practice. A
common vocabulary and classification system “emerges” and almost imposes
itself. Evolution, when it occurs, is ad hoc.
 Specialist programs which recognise or
implement user-defined ontology
4.6
Knowledge Organisation: an LIS (library and information
science) perspective
Section 4.2 introduced knowledge organisation KO. In this section we establish a
connection between concepts and the KO Knowledge Organization sub-domain of
LIS library and information science.
(Friedman and Smiraglia, 2013) seek to
“improve comprehension of the socially-negotiated identity of concepts in
the domain of knowledge organization. Because knowledge organization as
a domain has as its focus the order of concepts, both from a theoretical
perspective and from an applied perspective, it is important to understand
how the domain itself understands the meaning of a concept. ” (Friedman
and Smiraglia, 2013, p.27).
They do so within the frame of what they mean by Knowledge Organisation KO,
which is identified as the:
“entire population of formal proceedings in knowledge organization – all
proceedings of the International Society for Knowledge Organization’s
international conferences (1990-2010) and those of the annual classification
workshops of the Special Interest Group for Classification Research of the
American Society for Information Science and Technology (SIG/CR).”
(Friedman and Smiraglia, 2013, p.27).
Thus knowledge organization KO is defined and identified with the perspective
adopted by ISKO and its journal, the Journal of Knowledge Organization. Most
writers in this field come from the LIS Library and Information Science discipline.
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Knowledge organisation by means of concept process mapping
Their perspective is very important but subtly different both from the information
systems discipline and the cognitive science approaches.
The literature on knowledge organisation makes frequent reference to knowledge
organisation systems KOS. Although I instinctively dislike this term because the
literature surrounding it does not always appear to understand what a system is, I
will use it because others do.
§5
Positioning Conceprocity among Knowledge Organisation
Systems
We have already mentioned Ernest Davis’ summary: (Davis, 2001) of knowledge
representation.
5.1
Knowledge Representation (KR) as the primary dimension
for classifying and comparing Knowledge Organisation
Systems KOS
See (Rocha Souza et al., 2010) for an extensive discussion of knowledge
organisation systems (KOS) and the related topic of knowledge representation.
They themselves use a CMap concept map (cf. Novak and Cañas, 2008) to suggest a
tentative set of types of KOS (Rocha Souza et al., 2010). This is reproduced here as
Figure 20:
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Knowledge organisation by means of concept process mapping
Figure 20 A tentative set of types of KOS (from Rocha Souza et al., 2010, FIG 1)
Conceprocity could appear at more than one point in this essentially hierarchic
classification, since it can be used as a form of concept mapping system, but it can
also be used to make a taxonomy or classification scheme (Jacob, 2004) and in the
construction of semi-formal ontologies.
(Rocha Souza et al., 2010: figure 3) reproduces a KOS Spectrum originally
proposed by (Daconta et al., 2003). It is repeated here as Figure 21:
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Knowledge organisation by means of concept process mapping
Figure 21 KOS Spectrum Source: (Daconta et al., 2003)
This uses a single dimension, identified as semantic strength, along which they
position various KOS. (Rocha Souza et al., 2010) comment that (Daconta et al.,
2003) tend to present KOS and their representational languages together; but we
would comment that they themselves tend to identify and equate KR and KOS as we
have previously seen in Figure 20. Here in Figure 21, travelling from weak
semantics to strong is identified with the formality of the knowledge representation
language.
Conceprocity has rather weak semantics. It can be used to represent data structures
in a way which is as precise as the ER Entity Relationship model and indeed Chen’s
E/R Entity Relationship model (Chen, 1976).
Conceprocity is in fact considerably more expressive than even an extended ER
model, which concerns itself with data and its semantics. Conceprocity recognises
procedures, logical connectors, principles and events. However the semantics
associated with the event and process elements are deliberately not very precise
since they depend on the modeller’s use of natural language text. It is possible to
represent relational data structures, processes, events and Boolean decisions in
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Knowledge organisation by means of concept process mapping
Conceprocity but no pretence is made to sufficiently formal semantics to permit, for
example, the execution of a Conceprocity model – it is not a programming language
and does not support automatic inferencing. In any concept mapping approach, the
precise form of the name given to a concept and to a relationship – expressed as they
are in natural language – are at one and the same time extremely important and
extremely difficult to get right. We present Conceprocity as a visual language and
intend it to be used for communication between human observers.
(Rocha Souza et al., 2010) quote (Guarino, 2006) as proposing the term
« ontological precision ». They reproduce a figure from Nicola Guarino as their
FIG. 8, reproduced here as Figure 22:
Figure 22 Levels of ontological precision - (Guarino, 2006)
(Guarino, 1998) had earlier clarified the terminology associated with ontology as a
philosophical term versus ontology as an artefact in the following terms:
 Ontology: the philosophical discipline
 Study of what there (possibly) is
 Study of the nature and structure of reality

Domain of entities

Categories and relations

Characterizing properties
 An ontology: a theoretical or computational artefact
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Knowledge organisation by means of concept process mapping
 “An explicit and formal specification of a conceptualization”
(Gruber, 1993)
 A specific artefact expressing the intended meaning of a
vocabulary in terms of the nature and structure of the
entities it refers to
In its current form, Conceprocity can be used to represent taxonomies and informal
ontologies.
5.2
Analytics based on Conceprocity models
Conceprocity models are stored in the Lucidchart server system. They can be saved
in .vdx format, this being the Microsoft Visio XML format – see
http://msdn.microsoft.com/en-us/library/office/aa218409%28v=office.10%29.aspx
It is therefore potentially possible to:
1. Create concept dictionaries and a database of Conceprocity terms
and usages; these in turn can form the basis for semi-automatic
ontological analysis and perhaps comparison of personal ontologies.
2. Carry out various forms of data analysis, potentially including
metrics such as Betweenness, Closeness, Diameter, Clustering
Coefficient, Average shortest path…
See https://gephi.org/ (but no direct XML import) and
http://www.liquid-technologies.com/xml-schema-editor.aspx
I do NOT anticipate carrying out such analysis in PhD timescales.
5.3
A functional perspective: (Zeng, 2008)
(Zeng, 2008, p.161) suggests a table of functional effectiveness reproduced as FIG
9. in (Rocha Souza et al., 2010) and below as Figure 23:
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Knowledge organisation by means of concept process mapping
Figure 23 KOS Spectrum from (Zeng, 2008, p.161) with suggested functional effectiveness
We now suggest a positioning of Conceprocity based on Figure 23 – see Table 4 following, which:
1.
2.
3.
reproduces Zeng’s table
extends it with additional columns both with two additional KOS which I here identify
([1]the semantic web and its RDF and OWL knowledge representation and [2] first-order
logic) and also with Conceprocity
extends it with additional rows: visualisation and suitability for machine processing
Table 4 A suggested positioning of Conceprocity and other KOS in Functional Effectiveness
terms
****
**
***
**
****
**
*
****
***
****
Establishing
associative
relationships
44 / 67
Conceprocity
**
First order
logic
***
Semantic web:
RDF, OWL
Ontologies and
semantic
networks
Thesauri
Establishing
hierarchical
relationships
****
Classification
schemes and
Taxonomies
Controlling synonyms
***
Gazetteers and
directories
Eliminating ambiguity
Synonym rings
KOS →
Term lists
Function ↓
***
***
*
***
**
**
***
*****
**
**
***
Knowledge organisation by means of concept process mapping
Presenting properties
*****
Visualisation
**
Machine processing
5.4
*
*
****
*****
****
Some further evaluative comments on concept mapping
(Trochim, 1989) introduces the use and usefulness of concept maps for planning
and evaluation. The same author discusses their reliability in (Trochim, 1993).
More recently he and colleagues have discussed concept mapping as an alternative
approach for the analysis of open-ended survey responses (Jackson and Trochim,
2002).
More speculatively, (Thimor and Fidelman, 1995, page 35) have suggested:
“Concepts are related to logic. There are several approaches to logic and in
every approach concepts are created and interrelated differently. There are
two possibilities for the hierarchic set-up of hierarchic concept-maps: one
possibility is a top-down map; the other is a bottom-up map. Therefore,
individual differences in concept-mapping may be related to individual
differences in the approach to logic. It was [earlier] suggested by Fidelman
that different approaches to logic may be related to individual differences in
the relative efficiencies of the hemispheric analytical and synthetical
mechanisms. In the sequel a theory relating concept mapping to the cerebral
hemispheres through logic is suggested. According to this theory, top-down
concept-mapping is more related to the right hemisphere relative to
bottom-up mapping. On the other hand, bottom-up mapping is more related
to the left hemisphere relative to top-down mapping.”
They go on to suggest a contrast between the formation of concepts in Bertrand
Russell’s logic which they present as bottom-up, and on the other hand, in Gottlieb
Frege’s logic concepts – where a top-down formation is primary: (Thimor and
Fidelman, 1995, page 50).
This suggests that concept mapping can appeal to different populations who will
tend to use it in various complementary ways, particularly with the emergence of
cloud-based approaches based on collaborative infrastructure.
5.5
Usage profiles
Conceprocity is cloud-based and essentially multi-user. It also provides implicit
support for the notion of usage profiles. A usage profile is a named usage of
Conceprocity.
The various usage profiles require few or no extensions to the Conceprocity basic
notation which is richly expressive.
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**
Knowledge organisation by means of concept process mapping
It is possible and desirable to start with a beginners’ profile “simple concept
mapping for beginners”, in which the only available relationship is Association and
no use is made of principles, and only then to move on to typed relationships and
principles. Particularly in this profile, we place strong emphasis on the use of
sketches, icons and images. We have chosen to give the name CAPRI to this simple
usage profile. CAPRI stands for concepts, actors, procedures, relationships, images.
By way of contrast, we identify the full usage profile as CAPRILOPE, Concept /
Actor / Procedure / Relationship / Image / Logical Operator / Principle / Event.
Table 5 Conceprocity Usage Profiles
Model type
Simple concept
mapping for
beginners
Name
Purpose
Conceprocity CAPRI
Concepts Actors Procedures Relationships Images. Makes use
of a deliberately restricted range of Conceprocity notions. In
particular, the only relationship type supported is Association. In
order to give more expressiveness, this profile permits
Association relationships to be named.
Knowledge
mapping
Conceprocity
CAPRILOPE
Use case
diagrams
Conceprocity Use
Case
Very general with the full range of Conceprocity objects, Concept
/ Actor / Procedure / Relationship / Image / Logical Operator /
Principle / Event. In this profile, relationships should not normally
be named. Instead, the nature of the two notions linked by a
typed relationship should normally provide full context sufficient
to make the meaning of the relationship clear. Where this is not
the case, Conceprocity permits commentary / notes.
Typical uses include: self-observation, research design,
representing knowledge as-is and as-ought, demonstrating
understanding, documenting a body of knowledge and design of
teaching, learning and evaluation. In the context of teaching, it is
sensible to use such knowledge maps as the “advance
organiser” or signposting originally suggested by (Ausubel,
1963). This usage profile is also suitable for the representation of
algorithms and of heuristics.
Conceprocity use case diagrams are generally similar to UML
UCDs but they are extended to show the interaction between an
actor and a use case as a specific interaction element. This is
done because such interactions normally need to be
implemented, sometimes as form and subform hierarchies,
sometimes as webpage hierarchies
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Knowledge organisation by means of concept process mapping
Event-driven
process chains
Conceprocity EPC
Conceprocity event process chain diagrams are generally similar
to ARIS EPC diagrams but they are optionally extended by
incorporating a specific Data swimlane. The data swimlane is
populated by concepts, which may subsequently be implemented
as data tables, data views, specific file-types or by webpages.
The value of the data swimlane is that interactions between it and
other (non-data) swimlanes enable the modelling of the data
flows (dataflows) that would otherwise require specific dataflow
diagrams (DFDs)
E/R Data models
Conceprocity E/R
Conceprocity Entity / Relationship diagrams follow the
conventions established by (Chen 1976) and subsequent work.
However, ordinality, cardinality and multiplicity are shown in the
Conceprocity / UML style because this is more expressive than
Chen’s notation
A notion similar to that of usage profile does exist in G-MOT. One usage profile
that exists in G-MOT which has no direct equivalent in Conceprocity is that of an
ontology builder and the automatic generation of OWL language statements from
the ontology model which is so built.
5.6
How and why Conceprocity differs from G-MOT
So why not simply reuse the existing G-MOT formalism? Table 6 gives a (gentle)
critique of Mot+ and G-MOT and outlines how Conceprocity differs:
Table 6 How Conceprocity differs from G-MOT
G-MOT
Conceprocity
Conceprocity is a little closer to UML
Based on the object oriented (OO)
approach extensively used in software – particularly in the ways in which
concepts are related
engineering, but just as the OO
approach is often vague about its
philosophical and pragmatic
antecedents, so (sometimes) is G-MOT
G-MOT is object-influenced, most
obviously by class diagrams. But it
separates procedures out from
concepts, thus eschewing
encapsulation – which has value in
software engineering but not always in
clarifying meaning and understanding
Conceprocity follows G-MOT.
Inheritance is explicitly supported
between concepts by means of a
specialisation- generalisation
relationship. The effect of
encapsulation can be achieved by deft
use of hierarchy: an apparently atomic
concept is expanded at a lower level in
the modelling hierarchy.
The visual representation used is
sometimes obscure, specifically in the
areas of how the different types of
Conceprocity prefers a
UML-influenced style in which the
type of arrow shows the kind of
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Knowledge organisation by means of concept process mapping
G-MOT
Conceprocity
relationship are displayed; they are
signified by a character label rather
than by a visual device
relationship. This is initially a little
more difficult to teach and learn, but
subsequently makes Conceprocity
models easier to read and to
understand.
The visual representation used is
sometimes unclear, particularly the
visual distinction between classes and
object-instances (although this is
better in G-MOT than in the earlier
Mot+)
Conceprocity is clearer again in this
respect.
The expression is not very visual,
depending too much on textual
elements and not on images and icons:
It does not engage the right brain
Particularly in the simple usage
profile, users are actively encouraged
to make full use of icons, images and
sketches.
It does not permit the clear expression
of algorithms, in particular
conditionality (if… then… else…
endif) and repetition (do while…;
repeat until…)
Whereas in G-MOT conditional
statements are represented as
principles, Conceprocity prefers to
make this visually much clearer by
using logical connectors and the
separate event syntax (here following
the event process chain paradigm
suggested by(Scheer et al., 2005).
The language does not encourage
consideration of object state and/or
events
Conceprocity uses the event notion to
make this much clearer.
Cardinality and ordinality
(multiplicity) is not made explicit in
associations
Conceprocity follows the conventions
of UML class diagrams in this respect,
making multiplicity much more
evident.
Conceprocity is implemented using the
G-MOT is a standalone (“desktop”)
Lucidchart web-based diagramming
application available only for
system, which is SaaS.
Windows. It is therefore not SaaS,
software as a service – which is needed
to make web-based collaboration on
concept maps possible and easy –
especially in the context of the PhD
research which I am currently
undertaking
5.7
G-MOT strengths
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Knowledge organisation by means of concept process mapping
G-MOT has many strengths, some of which are not matched in Conceprocity. Its
visual editor, although restricted to the Windows desktop, enforces grammar rules
much more effectively than does Conceprocity. A notion similar to that of usage
profile does exist in G-MOT. One usage profile that exists in G-MOT which has no
direct equivalent in Conceprocity is that of an ontology builder and the automatic
generation of OWL language statements from the ontology model which is so built.
5.8
Conceprocity conceptual data structures
In any knowledge representation scheme, it will normally be necessary also to
represent data. Table 7 is an attempt at a synthetic view and positioning of
Conceprocity within the spectrum of conceptual data structure CDS, here
following (Völkel and Haller, 2009) :
Table 7 Conceptual data structures and their associated metadata
Technique
Metadata
Expressiveness, precision and recall
Spreadsheets
Pragmatic – the meaning of the data is not
explicit, but is partially expressed in the
natural language semantics of column
and/or row headings; and partially in
relationships expressed as formulae
between cells
Potentially very expressive and
frequently imprecise or even
contradictory. Charting permits
visually-arresting representations of some
of the underlying data.
Relational databases
If the data is normalised (Codd, 1971; Date,
2003), then the column headings name sets
of atomic (non-divisible) data items. This is
deliberately constricting, because
human-readable metadata, in the form of a
natural language description (name) for
each attribute, can be exploited by users as
they enquire from the data, enabling precise
answers to questions they have. These
names can be extended by a data dictionary
(which, however, is often not accessible to
the end-user of the data in the database).
Deliberately very restricted
expressiveness. All data is constrained to
appear as tables to permit generality and
precision of subsequent querying. The
results of queries are themselves virtual
tables constructed from the original input
data.
Outlining and
Outliners
The relative positioning of the items in a
hierarchy groups and classifies data; and
associates meaning with each group and
sub-group. The addition of a grid, as in the
products Ecco and InfoQube – see
(Gregory, 2010) - permits further
structuring and expressiveness.
Hierarchies themselves are cognitively
powerful or not depending on the prior
training of the user.
Mindmaps
The relative positioning of the items in a
diagram groups and classifies data; and
associates meaning with each branch and
sub-branch. An image is (potentially)
associated with each branch or sub-branch
Visually very powerful, the user
perceives both structure and meaning.
Querying is very imprecise or
non-existent.
Concept maps
(Novak and Cañas,
The relative positioning of the items in a
diagram groups and classifies data; and
Visually very powerful, the user
perceives both structure and meaning.
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Knowledge organisation by means of concept process mapping
Technique
Metadata
Expressiveness, precision and recall
2008)
associates meaning with each branch and
sub-branch.
Relationships are distinguished from
concepts. Querying is very imprecise or
non-existent in current implementations.
XML, RDF and
OWL
The meaning of an XML document is
described in an associated Data Type
Definition (DTD) or Schema. The RDF
Schema carries this forward.
XML-based approaches potentially
combine the strengths of outlining and of
relational database. Because XML is both
a language and a meta-language, it is
possible to define specialised languages
such as OPML.
Generalised query languages for XML
data are emerging. RDF makes possible
the expression of simple forms of
knowledge (as opposed simply to
information), and supports processes like:
Conceprocity maps
The relative positioning of the items in a
diagram groups and classifies data; and
associates meaning with each branch and
sub-branch. An image is (potentially)
associated with each branch or sub-branch.
Each object has a type, as does each
relationship (link). Appropriate use of
hierarchy enables encapsulation.

User-specified keyword
classification of information
structured in accordance with
user design

Rule-based auto-classification

Tagging
Visually very powerful, the user
perceives both structure and meaning.
Querying is currently non-existent but
because objects are semantically
classified it would be relatively
straightforward to construct a dictionary
for each Conceprocity map and a lexicon
(index) across multiple maps.
The latter in a cloud context might permit
the emergence of shared ontologies,
especially if the maps are constrained to
conform to RDF and OWL standards. The
latter is not yet proposed for
Conceprocity but has been achieved with
extensions to the similar G-MOT
approach. See (Paquette, 2010: part 3)
First order logic and
Horn clauses
Expressiveness and precision very high;
readability and visual appeal very limited
(although these can be enhanced by the
use of libraries which create
visualisations from Prolog statements).
Querying is very general and strong
logical inferencing capabilities are
offered
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Knowledge organisation by means of concept process mapping
§6
A critical evaluation of Conceprocity and some suggestions for
future work
6.1
The tentative nature of these initial conclusions: further
research proposed
Conceprocity is a semi-formal visual knowledge representation language which
enables and encourages the modeller to be more precise in defining, bounding and
relating conceptual and procedural knowledge. It is in effect a means to constrain
and enhance natural language expression and thereby to increase the precision of
the meaning which the modeller needs to express. To the extent to which two
modellers can agree upon a Conceprocity model, it is also a means to establish and
to verify communication of ideas and concepts.
In this paper in its present form I have sought to respond in a positive manner to the
constructive criticism of David Weir and of Renaud Macgilchrist. In doing so I have
both myself learned and also – I hope – begun to position the next phase of my
research. I look forward to further criticism!
Certainly, Conceprocity is not without its weaknesses. It is arguably an error to
permit so much generality of expression in a single modelling approach. The
counter-argument is that usage profiles permit a more restricted representation and
are therefore less likely to give rise to cognitive overload in users and readers. I
would also point out that in knowledge representation schemes such as UML, it is
necessary to learn a wide range of different – sometimes annoyingly so –
representations. This problem is even starker in the area of conventional structured
analysis, where a simple notion such as process is represented in different,
overlapping and confusing ways – contrast data flow diagrams, event process
chains and use case diagrams.
Because Conceprocity has been built using the Lucidchart diagramming system, it
will be very easy to change the formalism in directions suggested by the readers of
this article. For this and many other reasons I would strongly welcome critique and
suggestions for improvement.
A potential disadvantage of moving away from the simple concept and relationship
approach to concept mapping which most concept maps (and mappers) employ is to
make it slightly more difficult to create metrics. Whereas (Friedman and Smiraglia,
2013) can present concept maps simply as nodes and arcs, Conceprocity maps
include more basic notions – both nodes and arcs have type - and will therefore be
slightly more difficult to analyse programmatically.
In the next few months I intend using Conceprocity in the following ways:
1. I will gradually build a Conceprocity map of my own Personal Work System
PWS and the supporting Personal Information Management System PIMS.
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Knowledge organisation by means of concept process mapping
This will have two forms, an as-is – current map – and an as-ought – what I
would like to achieve. These models will be based on the PhD journal which
I have now maintained for two years.
2. I have already used G-MOT to analyse complex scientific articles and will
in future use Conceprocity to do so.
3. Two of my masters’ research students will use Conceprocity as a small
element of their research projects, due for submission in November.
4. In the first semester of the new academic year I will teach two sessions on
concept mapping within my IS505E Principles of E-Commerce PEC
module. I expect two classes each of about 40 students. The first session will
introduce the basic usage profile of Conceprocity and the second more
advanced usage. One of the two courseworks in this module requires each
student to choose an academic article concerning e-commerce or
information systems and to analyse its content. Students will be required to
use the basic Conceprocity usage profile CAPRI and encouraged to use the
full usage profile CAPRILOPE as part of the article analysis. This action
learning approach will give me valuable feedback on the practical and
conceptual difficulties faced by students as they learn Conceprocity. This
will in turn be fed back into an improved Conceprocity 2.0 early in the new
calendar year.
5. I hope to be able to carry out some quantitative assessment of Conceprocity
models and modelling based on the IS505E PEC experience. This is
dependent on being able to carry out some software-assisted analysis of
Conceprocity models stored in XML format. This is perhaps a student
project or an internship possibility for a software engineering student from a
software engineering school.
Together these experiments will yield data and enable me (and a co-author?) to
create a research-based journal article on concept process mapping early in 2014.
This will represent a useful contribution from the PhD process.
6.2
More fundamental difficulties and objections
We have largely accepted as a given the notions put forward by (Paquette, 2010)
which are themselves based partly on the UML thinking of (Booch et al., 2005).
Paquette’s thinking also derives in large part from cognitive science; this influence
pervades his book and in particular informs chapter 6 on taxonomies of problems
and generic skills.
In section 2.5 above, we suggested that existing visual representation formalisms
have emerged largely from the computer science and software engineering
communities. It is instructive to reconsider the origins of formalisms such as Entity
Relationship models, modern structured systems analysis, conceptual graphs (John
Sowa (Sowa, 2000, 1984) following Charles Pierce), the object modelling
technique and the successor Unified Modelling Language UML. These are all
representation approaches which have been built primarily for the analysis and
architectural design of complex software systems. In Conceprocity as it currently
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Knowledge organisation by means of concept process mapping
stands we have designed and presented a visual representation system which,
following (Paquette, 2010, p.xiv), we wish to be usable by educational specialists
and learners who are not computer scientists. It is at the same time general and
powerful enough to represent the structure of knowledge and learning/working
scenarios.
Paquette goes on to say:
“We present three major steps starting with (1) informal visual modelling
for the educated layperson, to help represent interesting knowledge. We
then (2) move onto semi-formal modelling to help define target
competencies and activity scenarios for knowledge and competency
acquisition by learners and workers. Finally (3) we present the more formal
visual models (Ontologies) that can be used by software agents to ensure
execution of knowledge-based processes on the semantic web.” [(Paquette,
2010, p.xiv) slightly amended for clarity.]
Thus G-MOT supports three dialects, one for general use, one for instruction design
and one for ontology building. Similarly Conceprocity distinguishes usage profiles
(see Table 1) within a single visual representation language.
Recall that notion is the name given in Conceprocity to the modelling
meta-concepts of concepts, procedures, actors, principles, events and relationships.
See sections 2.3 and 2.4. A possible alternative word for notions is meta-concepts,
that is, concepts about concepts. We now wish further to address the issue of
whether Conceprocity has chosen the right notions. In section 2.5 we discussed why
Conceprocity distinguishes concepts, procedures and principles. Here we consider
the nature of concept mapping itself and the relationships permitted in
Conceprocity.
6.2.1 What is concept mapping anyway?
Much of the literature surrounding concept mapping comes from the field of enquiry known
as knowledge organisation which is largely situated within the discipline known as library
information science. (Hjørland, 2009) holds that information science and knowledge
organization cannot avoid relating to theories of concepts. Knowledge organizing systems
(e.g., classification systems, thesauri, and ontologies) should be understood as systems
organizing concepts and their semantic relations. Different theories of concepts have
different implications for how to construe, evaluate, and use such systems. Based on what
he calls “a post-Kuhnian view” of paradigms, Hjørland argues that the best understanding
and classification of theories of concepts is to view and classify them in accordance with
epistemological theories (he emphasises empiricism, rationalism, historicism, and
pragmatism). Different views of concepts are associated with different worldviews and
epistemologies which tend to compete with each other. The historicist and pragmatist
understandings of concepts are in his view the most fruitful views; he outlines the
importance of historicist and pragmatic theories of concepts for information science. For
him, the concept is a socially negotiated construct that should be identified by studying
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Knowledge organisation by means of concept process mapping
discourses (Hjørland, 2009, p.1530). This view of concept theory has been labelled
socio-constructivist.2
(Friedman and Thellefsen, 2011) discuss knowledge organisation systems and the
emergence of concept theory and semiotics in that connection. For them knowledge
organisation as a domain has as its focus the order of concepts, both from a theoretical
perspective and from an applied perspective. It is therefore important to understand the
meaning of a concept found in text and in visual maps. Whatever the epistemological stance
one adopts, it is evident that the meaning of a concept is that which was intended by the
originator of that concept in accordance with their own particular epistemological stance. 3
Thus when (Friedman and Smiraglia, 2013) attempt a synthesis of the existing theory
concerning concepts and concept mapping they do so within the tradition of library
information science and in particular they identify “knowledge organisation systems”,
based on earlier work reported as (Friedman and Thellefsen, 2011).
6.2.2 Relationships in Conceprocity
Some concepts refer to data. The E/R Entity Relationship model of (Chen, 1976) has
informed in particular Conceprocity’s thinking about associations, cardinality, ordinality
and multiplicities. See section 2.6.5
The ideas of aggregation, generalisation and specialisation were introduced by (Smith and
Smith, 1977) and later informed the design of UML and G-MOT. However, it is difficult to
discern a single source of inspiration for the conceptualisations underlying UML.
Specifically, UML does not possess a meta-model; nor does G-MOT. See section 2.6.3
Composition and part-whole relationships are the subject of mereology (which is separate
from the concept of topology). (Guarino and Poli, 1995) give a fuller introduction. See
section 2.6.4.
6.2.3 Conceprocity: Design issues which are not yet fully resolved
In its current guise, Conceprocity recognises various structural relationship types. These
are:
 Association
 Aggregation
 Composition
 Specialisation / Generalisation
 Regulation
 Precedence
2
Kuhn also wrote extensively about the nature of concepts, largely from a cognitive psychology
point of view.
3
I note here that the stance of Hjørland is socio-constructivist and that my own is pragmatist –
a stance which Hjørland also recognises as very important.
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Knowledge organisation by means of concept process mapping
 Input-Product
 Instantiation
Are there other structural relationship types beyond those already recognised in
Conceprocity?
Is there (for example) a relationship type is-a-model-of in the relationship concept-A
is-a-model-of concept-B?
Given that knowledge types share common characteristics with UML classes, but that
knowledge is not at all the same thing as data: What interpretations can we validly give to
the relationship types which Conceprocity recognises? Thus, what is inheritance?
Note here that the semantics of inheritance are debated within the programming
community. See for example: (Cook et al., 1990) who suggest that in typed object-oriented
languages the subtype relation is typically based on the inheritance hierarchy. They
therefore put forward a new typed model of inheritance that introduces polymorphism into
the typing of inheritance. They call for the uniform application of inheritance to objects,
classes and types. The resulting notion of type inheritance allows them to show that the type
of an inherited object is an inherited type but not always a subtype. This more general view
of inheritance is I suggest not only sensible but also what most human users would expect
when reading a Conceprocity model in which one concept is a specialisation of another.
Similarly, is multiple inheritance meaningful for Conceprocity concepts? Certainly, it is
possible to show it in Conceprocity.
How in Conceprocity do we best represent the subsetting and overlapping sets which are
well summarised by Venn diagrams?
The G-MOT editor continues to have strengths not possessed by Conceprocity; these
include XML import and (better) export, explicit support for the OWL Web Ontology
Language, and copy with reference between diagram levels. The latter would ideally be
generalised in Conceprocity…
Conceprocity does not yet possess easy means to produce and maintain a dictionary of the
objects it contains, nor any metrics.
6.3
Towards an ontological evaluation of Conceprocity
(Wand, 1996) holds that despite the availability of a large number of systems
analysis methods and techniques there does not exist a general underlying
foundation for this knowledge domain. The stance which Wand adopts is that an
information system is a representation of another “real-world” system. This
ontological stance borrows from the philosophy of Mario Bunge, and in particular
his ontological formalism as presented in (Bunge, 1977, 1979). Wand sees an
information system as a representation that enables us to obtain knowledge about a
certain domain without having to observe it. Thus where the represented domain
might be termed the real-world system, an information system is an artificial
representation of that real-world system, as perceived by somebody, built to enable
information processing functions. (Wand, 1996) therefore challenges me to
re-engineer Conceprocity starting from a clear ontological stance.
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Knowledge organisation by means of concept process mapping
(Wand and Weber, 2002) set out a framework for research on conceptual modelling
in connection with information systems which has four main components:
Table 8 Conceptual Modelling Framework Elements (based on (Wand and Weber, 2002, p.364))
Element
1. Conceptualmodelling
grammar
2.
Conceptualmodelling
method
3.
Conceptualmodelling
script
Context
4.
Meaning
Provides a set of constructs and
rules that show how to combine
the constructs to model
real-while domains.
Provides procedures by which a
grammar can be used. Such a
method needs to prescribe how
to make observations of a
domain into a model of the
domain.
A script is the product of the
conceptual modelling process.
The context is the setting in
which conceptual modelling
occurs and in which scripts are
subsequently used.
Status in Conceprocity 1.0
Largely complete. We need to give further
consideration in particular to properties,
since the current representation
(sub-concepts) consumes too much space
on the page. Note that we have yet to define
the meta-model (Rosemann and Green,
2002) for Conceprocity.
The method is documented, but for now
only in the form of a PowerPoint
presentation.
A Lucidchart template exists and this forms
the basis of each script.
The initial context of use has been
identified and some scripts have already
been produced. Conceprocity does not yet
possess easy means to produce and
maintain a dictionary of the objects it
contains, nor any metrics (but see section
5.2).
Macgilchrist challenges me to a more formal knowledge representation approach
both for my models and my meta-models; he suggests first order logic in the form of
Horn clauses supported by a Prolog implementation – see
http://www.coli.uni-saarland.de/projects/milca/courses/comsem/html/index.html.
I am rejecting this possibility for the time being because first-order logic is
inaccessible to most of the research volunteers that I might reasonably expect to be
able to work with in the near future. For now, I will take a much more pragmatic
stance. Conceprocity is what it is because I sought for and have built a pragmatic
knowledge representation scheme based on typed concept maps. It is a means to an
end. That end is to complete my Ph.D. in the shortest possible time scales and of
course to the highest possible standard commensurate with those timescales. As
David Weir has put it in conversation: "focus, focus, focus". As I use Conceprocity
over the next few weeks to begin to model my own personal knowledge
management system, as I use it to structure my final literature review, as I work with
my MSc and MAIB students, as I experiment with Conceprocity in the classroom,
but above all as I use Conceprocity to make explicit the existing personal work
systems of research volunteers and to suggest improved personal work systems to
them: I will also gradually be building up requirements for Conceprocity 2.0.
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Knowledge organisation by means of concept process mapping
6.4
Learning by enquiry: some parallels with Checkland’s LUMAS
It is clear that concept maps lack ontological truth value in so far as they are only
ever artificial artefacts that present an observer’s point of view concerning what is.
57 / 67
Knowledge organisation by means of concept process mapping
Figure 24 Illustrative summary of some of our propositions
established
knowledge:
academic literature
IP
learn and
understand
existing state of
the art
IP
C
researcher
IP
IP
R
IP
constructs and their
relationships
C
IP
analyse gaps in
existing
literature
applicable
knowledge
C
P
IP
reflect on
anomalies
IP
IP
research
question
IP
IP
C
problematic
situation requiring
action
IP
IP
structured self
observation;
auto-ethnography
IP
design research to
address identified
research question
C
learn through action
and reflection cycles
C
collect data
IP
data: measurements and
descriptions of the attributes
of objects or events
IP
derive
meaningful
information
C
choose
goal-directed
activity
C
report findings
We would comment that this diagram illustrates an inner learning loop as the
researcher engages with perceived reality in accordance with some research
methodology. She or he learns in a problem-focussed way as (s)he uses methods in
an applied methodology. Just as (Argyris, 2000) describes double loop learning in
organisations, we suggest that there potentially exists also an outer loop by means
58 / 67
Knowledge organisation by means of concept process mapping
of which the researcher may learn at the more profound level described by Peter
Checkland. (Checkland, 2000) presents (inter alia) LUMAS, Learning for a User by
a Methodology-informed Approach to a problem Situation. Taking as his definition
of methodology ‘a body of methods used in a particular activity’, Checkland
suggests that a user knowledgeable about a methodology perceives a problem
situation and uses the methodology to try to improve it. The methodology as a set of
principles is converted by the methodology user into a specific method which the
user feels to be appropriate for this particular situation at this moment in its history:
“The user U, appreciating a methodology M as a coherent set of principles, and perceiving a
problem situation S, asks himself (or herself): What can I do? He or she then tailors from M
a specific approach, A, regarded as appropriate for S, and uses it to improve the situation.
This generates learning L, which may both change U and his or her appreciations of the
methodology: future versions of all the elements [of] LUMAS may be different as a result
of each enactment of the process shown.” (Checkland, 2000)
Figure 25 Checkland's LUMAS model Source: (Checkland, 2000)
Checkland stresses that it is not the methodology which leads to improvement. It is
the user as (s)he benefits from using the guidelines, as (s)he takes the formally
defined methodology M to create or tailor A, the actual, user- and situation-specific
approach adopted to the Real –world problem R that (s)he perceives a concern for.
Asked recently in an interview by Frank Stowell (Stowell, 2013), whether he sees
the “systems approach” as “a scientific methodology”; if so, how does it guide
scientific inquiry, in your opinion? If not, how would you describe the relationship
59 / 67
Knowledge organisation by means of concept process mapping
between a “systems approach” and scientific inquiry?”, Peter Checkland has
replied:
“
As for the phrase ‘a systems approach’ I see it as being the name of any
epistemology which encompasses the idea ‘system’; defined as the name of
the concept of an adaptive whole which can adapt and survive in a changing
environment. It thus has only epistemological, not ontological status. This is
crucially important for the Systems Movement, this difference between
Natural Science and so called ‘Social Science’. Thus, Marx has a theory of
history, and his ideas change history, which is not law governed. On the
other hand Copernicus and Galileo have different theories concerning
whether our local universe is sun-centred or earth-centred; but these ideas
can have no effect whatsoever on what is the case out there in the universe,
which is law governed. What this means for a ‘systems approach’ is that if it
engages with human and social phenomena it can develop only useful
epistemology, not discover laws.
” (Stowell, 2013, p.54)4
Thus we suggest the existence of problem-focussed or situational learning – using
methods in an applied methodology; and higher-level learning – which will
manifest itself in a deepening appreciation of methodology and a concern to
develop it further in action. We also suggest the possibility that the outer loop
corresponds more-or-less directly to the inquiring / learning cycle of Checkland’s
Soft Systems Methodology SSM.
6.5
An application to student learning
Accepting that his approach to concept mapping has as a primary justification
Checkland’s Learning for a User by a Methodology-informed Approach to a
problem Situation, the author has set a group of postgraduate students on a
programme of international communications and digital marketing who are, in a
particular module, studying the principles of e-commerce: the task of individually
choosing an academic article relating to business information systems or to
e-commerce or e-business. The individual assignment is to choose one article from
a leading information systems or e-business or e-commerce journal, to read it very
carefully, to identify and map the concepts within it, to summarise the article, and
then to identify and discuss the practical consequences and usefulness of the theory
in the actual practice of e-business. So each student on the module will create a
4
Checkland might perhaps resist ontological commitment. See
http://en.wikipedia.org/wiki/Ontological_commitment.
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Knowledge organisation by means of concept process mapping
single summary document or presentation and then upload the summary and
implications to cloud-based storage so that all students can see the results of the
work of others. When mapping the concepts students must use the Conceprocity
concept process mapping approach. The results and the learning will both be
evaluated in the near future.
6.6
Complementary approaches to concept mapping as part of a
mixed-methods research approach
The author’s current research is at heart a multi-methodology – cf. (Avison et al.,
1998) - mixed-methods and inherently exploratory approach to a research question
which can be simplified to:
“What is the contribution of personal information management systems
PIMS to the working model and personal work system of knowledge
workers?”
Mixed methods research mainly refers to quantitative and qualitative research in
differing mixes. For an introduction to the issues, see (Ågerfalk 2013). (Goldkuhl
1995) presents a Habermasian view of information and action which is in contrast
both to pragmatism as seen in (Ågerfalk 2010) and critical realism as seen in
(Mingers et al. 2013) and (Zachariadis et al. 2013).
Table 9 indicates how two contrasting forms of concept mapping are used in
complementary experiments already underway or soon to be started. These two
forms of concept mapping (which I believe to be complementary) are:
1. Conceprocity concept-process maps. Conceprocity CAPRI or CAPRILOPE
models are the result of conscious analysis and specific design by
Conceprocity modellers.
2. Leximancer “fuzzy” concept maps.
Concerning Leximancer: (Smith and Humphreys, 2006) report that the Leximancer
system is a relatively new method for transforming lexical co-occurrence
information from natural language into semantic patterns in an unsupervised
manner. It employs two stages of co-occurrence information extraction—semantic
and relational—using a different algorithm for each stage. “The Leximancer system
performs a style of automatic content analysis. The system goes beyond keyword
searching by discovering and extracting thesaurus-based concepts from the text
data, with no requirement for a prior dictionary, although one can be used if desired.
These concepts are then coded into the text, using the thesaurus as a classifier. The
resulting asymmetric concept co-occurrence information is then used to generate a
concept map.” (Smith and Humphreys, 2006, p.262).
Thus what I term Leximancer “fuzzy” concept maps emerge from unsupervised
(or, better in practice, semi-supervised) semantic mapping of natural language text.
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Knowledge organisation by means of concept process mapping
Figure 26 shows the result of a semi-supervised Leximancer analysis of the author's
PhD journal (circa 130,000 words):
Figure 26 Fuzzy concept map of the author's PhD journal produced using Leximancer
Leximancer automatically recognises only single-word concepts. Most of my
research concerns compound concepts; here is the list used when producing Figure
26 5:
•
information system (merge with IS)
•
personal information management system (merge with PIMS)
5
To inform Leximancer of the centrality of such compound terms, it is necessary to
Edit Compound Concepts: to manually compound selected concepts via Boolean
operators to obtain deeper and more meaningful analysis. Clicking ‘Edit’ at the
‘Compound Concepts’ stage enables this. Subsequently, Compound concepts will
automatically appear on the map if they are regular word concepts.
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Knowledge organisation by means of concept process mapping
•
work system
•
personal work system (merge with PWS)
•
action research
•
knowledge management (merge with KM)
•
knowledge representation (merge with KR)
•
personal knowledge management (merge with PKM)
•
Personal Information Management (merge with PIM)
Table 9 also evidences the significance of concept mapping in this research work.
Table 9 Experiments planned or underway in the current research of Mark Gregory
Experiment
1. Analyse my own auto-ethnography using Leximancer
emergent or fuzzy concept maps. This involves using
Leximancer to enquire into my auto-ethnographic PhD
journal (130000 words).
2.
Building various text corpora and then analysing them


Recognised writing concerning personal
information management
Key information systems literature

3.
4.
5.
6.
Key literature concerning the epistemology and
ontology of personal information management and
personal knowledge management
Analyse my own auto-ethnography using
Conceprocity; the outcome will be a directed and
synthetic concept map
Observing the usability and usefulness of Conceprocity
mapping used by postgraduate students as a means of
understanding and elucidating research articles
Encourage recognised PIM researchers to audit their
own personal information management approach in the
form of a PIM Audit; to discuss the resultant
approaches and perhaps to map some of them
Mentored action research with a small number of
research volunteers
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Concept mapping approach
Leximancer. I will learn how to seed Leximancer
with compound concepts (e.g. information system,
personal information management, personal
information management system) and thus to refine
and focus the resultant concept map. An early
attempt at this analysis appears as Figure 26
Leximancer; seeking the emergence of significant
vocabulary as a fuzzy concept map
Seeking evidence of a systems approach in the PIM
literature; expecting the null hypothesis
Seeking evidence of a systems approach in the IS
literature; expecting the hypothesis but at a low
level of significance
Seeking an emergent vocabulary
Conceprocity CAPRILOPE; the outcome expected
to be an initial definition of a Working Model
Conceprocity CAPRI; perhaps some use of
CAPRILOPE; the outcomes expected to be (1) a
better understanding of the extent to which these
two usage profiles are used and useful to students
and probably (2) refinements to both usage profiles
Limited use of Conceprocity CAPRILOPE
Evaluation will make limited use of Conceprocity
CAPRILOPE
Knowledge organisation by means of concept process mapping
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