A Representation Scheme for Managing Complex Knowledge
Abstract. A representation scheme called CKR-1 is introduced to deal with the challenges of
representing complex knowledge. Complex knowledge is defined as deep knowledge concerning a
complex object, event, situation or process. The nature of complex knowledge is identified from samples
of knowledge drawn from the insurance industry. These examples are used to highlight the efficacy of
the representation scheme as well as its limitations. CKR-1 is a type of semantic network that supports
class/sub-class and several types of relationships. It derives its expressivity from ability to support
abstractions, elaborations and alternative points of view. Despite this, the scheme suffers from
limitations stemming from the difficulty in expressing abstract concepts.
Keywords: knowledge representation, complex knowledge representation, domain engineering,
knowledge engineering, knowledge management, insurance industry.
1
Introduction
The basis for intelligent behavior in knowledge-based systems lies in the explicit representation of knowledge.
But despite decades of research, knowledge representation remains a standing challenge in the field. Initially,
researchers in the ‘70’s and ‘80’s developed schemes such as frames, rules, logic and semantic networks. These
were subsequently developed and refined in the context of domains such as natural language, qualitative
physics, medicine and software development [1]. The idiosyncracies of the domain dictated the type of
representation scheme employed. For e.g. Schank developed and used scripts for processing natural language.
He introduced a specialized language to describe actions in scripts such as “P TRANS P to POST OFFICE,” “P
MBUILDS LINE POSITION.” Since the objective of the representation is to achieve intelligent behavior in a
specific domain, issues in reasoning associated with these problems were also explored. In many cases,
inferencing dominated the research rather than the representation per se. Researchers attempted to address
problems such as selective inheritance (or subsumption), non-monotonic reasoning, belief revision, probabilistic
reasoning and temporal reasoning [ibid]. In recent times emphasis of researchers has shifted in many cases
completely into procedural approaches with emphasis on neural networks [2], case-based reasoning [3] and into
multi-paradigm approaches utilizing perhaps rules, frames and objects [4]. The semantics of the representation
have not been fully addressed.
Ontologies are also a recent development and define terminologies and
relationships in a specific domain. Their main purpose is to facilitate knowledge sharing and interoperability of
information systems [5] rather than deep reasoning. While numerous intelligent applications have been
developed, the emphasis has been on applying existing techniques in new areas (see for example [6]). The
conceptual and epistemic levels in Brachman’s terminology [7], i.e. the primitives and their relationships have
not been addressed adequately for the business domain. Knowledge engineering is still a challenge in this
respect and especially more so for complex types of knowledge. We will define complex knowledge (CK) as
deep knowledge associated with a complex object, event, situation, idea or process.
Examples of such
knowledge include ‘design knowledge,’ ‘decision knowledge,’ ‘legal knowledge’ etc. In this research, we will
focus on how to represent such knowledge. The knowledge should be of sufficient depth such that an intelligent
agent is able to answer questions about the domain from the resulting knowledge base.
2
Relevant Literature
Seminal work in knowledge representation was carried out in the ‘70’s and ‘80’s. Current literature in this area,
especially as it pertains to CK is sparse for reasons already mentioned. There seem to be two approaches to
knowledge engineering which can be thought of as “top-down” and “ bottom up.” The first is to identify models
of the domain and to implement them using a well known representation scheme [8]. This is the method
primarily used for Expert Systems [9]. ‘Pictorial Knowledge Representation’ for example, uses a domain model
to represent geometric figures such as ‘triangles’ and ‘polygons’. The figures are represented as ‘nodes’ in a
classification network with separate ‘property nodes’ describing properties such as “the sum of interior angles of
a triangle must be 1800” [10] . The scheme supports image recognition of geometric shapes.
In the second approach, the representation itself is part of an application or an environment. KL-One is the most
well-known of these [11]. KL-One arranges “concepts” in a classification hierarchy from the most general
(“thing”) to the most specific (e.g. “Red Ferrari” ). Classes and instances are referred as “general concept” and
“individual concept.” The individual concept needs to be linked to a general concept to be valid. Properties of
concepts are modeled with “roles” (e.g ‘color’ of a car) that are inherited from the parent concept (“vehicle”).
Concepts can be formed from more general concepts by restricting roles. Allowable fillers (“Role sets”) are
defined by the “Value Restriction” property of a role.
For e.g. ‘part’ of a ‘car’ could be the role set
‘door’,‘engine’, ‘trunk’. Thus an enhanced frame-based representation is employed to describe the structure of
concepts. Relationships between roles bind instances to specific roles e.g. ‘John’ as a ‘driver’ of the ‘red
Ferrari’. The most interesting feature from the point of view of CK are “Description Wires,” used to make
assertions of the type “John drove the Red Ferrari at the Grand Prix.” The assertions utilize sets of linkages
between concepts for example, between ‘Ferrari’ and ‘car’ and between ‘race’ and ‘grand prix’ and are stored
outside the representation. Despite its power, the linkages between concepts and roles render it difficult to
partition the resulting network and to comprehend the knowledge in the knowledge base. Not surprisingly,
recent work has shifted to the linguistic level, in the form of ontologies. KL-One has inspired other major and
minor schemes including Telos [12] and KRS [13]. An interesting feature of Telos is the representation of
instances of an object and its properties as predicates e.g. [Car] [Ferrari, red, john]. Recently, an extensive
attempt has been made with multi-layered semantic nets to represent natural language [14]. Multi-nets address
the conceptual and epistemic levels by including an extensive set of objects, events and relationships as well as
constructs for representing quantification. The network is also noteworthy in separating the intensional from the
extensional representation. Despite its power, the representation is unwieldy for business realms. In a different
domain, Zarri proposed a representation language NKRL, whose basic constructs are ‘events’ and ‘concepts’.
‘Concepts’ are objects that can occur in ‘events.’ ‘Events’ and ‘concepts’ are placed in separate classification
hierarchies. Events have a case-frame structure (e.g. ‘SUBJ’, ‘OBJ’, ‘TIME’) and are manually entered from
news stories.
Some relationships between events are defined such as Event 1 causes Event 2.
The
representation is utilized in information retrieval [15]. A similar scheme is proposed by Gomez who uses event
relationships (“sim” – simulataneous, “sprec” – strong precedence) to model complex processes [16]. Yet
another approach, AEI-3 has been proposed recently. AEI-3 uses semantic networks and makes use of two node
types (“class,” “instance”) and two link types (“structural,” “descriptive.”) to represent large volumes of
administrative knowledge (for example, “Manugistics is a client of BSS” or “the van leaves BSS at 11:00 am.”)
[17]. It overcomes some of the traditional limitations of semantic nets such as tractability, but is a minimalist
design owing to the relative simplicity of administrative knowledge. The basic ability echoed in all schemes is
to model concepts and relationships with substantial variations in the way either are dealt with. A few methods
include propositions as well.
Except for Multi-nets the conceptual and epistemic levels are not developed
adequately.
3
The Nature of Complex Knowledge
There is a paucity of literature concerning the nature of complex knowledge. Lacking empirical evidence, we
will hypothesize some characteristics based on samples from a text book by [18]. In their foreword, they state
“The American Institute for Chartered Property Casualty Underwriters and the Insurance Institute of America
are committed to expanding the knowledge of professionals in risk management, insurance, financial services,
and related fields through education and research.” Thus their comments establish the rationale for using the
text as an example of complex knowledge. A few representative samples are illustrated in Table 1.
Table 1. Samples of Complex Knowledge [18]
Item#
1.
2.
3.
4.
5.
6.
7.
Example
Property includes real property and personal property. Real property is land, buildings and other property
attached to it. §1.6.
A liability loss exposure is any condition or situation that presents the possibility of a claim alleging legal
responsibility of a person or business for injury or damage suffered by another party. §1.6.
Types of insurers include stock insurers, mutual insurers and reciprocal exchanges. §1.11.
To be insurable, a loss should have a definite time and place of occurrence… § 1.8
Underwriting expenses include acquisition expenses, general expenses, premium taxes and licenses § 3.8
Depreciation is allowance for physical wear and tear or technological or economic obsolescence §6.14.
A contract of good faith is an obligation to act in an honest manner and to disclose all relevant facts §7.7.
Note: “§” refers to section numbers, there are no page numbers in the cited reference.
Individual instances of CK in the insurance domain appear to exhibit one or more of the following
characteristics:
a)
They describe objects, events, actions, situations, concepts, objectives or policies (item #1, 3).
b) Objects are generally concrete such as “automobile,” “property,” and “underwriter.” Concepts are
abstract such as “loss,” “depreciation” and “indemnify.”
c)
They elaborate or define a concept as in item#2 and 5.
d) The elaboration imposes additional conditions or restrictions (item #4).
e)
Concepts involve other concepts or objects that may or may not be explicitly defined (item#3).
f)
Objects and concepts are related to other concepts/objects through structural, axiomatic, mathematical
or logical relationships (item#5).
The major challenge is to represent abstract concepts since they are complex, amorphous and often defined in
terms of other concepts/objects that are challenging to represent in a meaningful manner. Consider, “The board
of directors consists of elected officials.” The ‘board of directors’ (BOD) and ‘elected officials’ are complex
concepts. that are defined easily, but what about ‘elected officials’?.
4
Knowledge Engineering for Complex Knowledge
The objective of the representation is to serve as a foundation for knowledge-based systems and knowledgemanagement systems [17]. Since visual representations facilitate this task, we are committed to one that has a
graphical notation. Visual representations will be user friendly provided they are intuitive and not overly
complex. This places certain restrictions on the epistemology. Secondly, the representation ought to provide
sufficient representational mechanisms or representational adequacy so that knowledge may be stored and
queries, answered [19]. Here, we will focus mainly only on intensional knowledge (definition) although it will
be seen that the knowledge can be readily used in inferencing and hence satisfying inferential adequacy [ibid].
We also assume that syntactic elements can be enforced at the implementational level, so the current focus is on
the logical and epistemic levels. In view of the nature of CK we will impose further requirements on the
representation scheme.
Complex concepts such as premium are defined in terms of other concepts such as ‘insurance coverage’ which
may themselves be complex. Therefore it is convenient to refer to high level concepts without having to redefine
the underlying concepts, leading to the requirement of supporting abstractions. The ability to demarcate
concepts, known traditionally as network partitioning [20], will ensure modularity. Another issue that arises is
the multiplicity of definitions. A single concept has alternative viewpoints making this a requirement as well.
For example, insurance could be described as a business or as an obligation to provide coverage. Relationships
among concepts can be simple (concrete) or complex (abstract). A class-subclass relationship is an example of a
simple relationship. Abstract relationships are complex because they are qualitative, involve multiple concepts
and involve complex conditions. Thus another requirement is to model both simple and complex relationships.
5
A SCHEME FOR COMPLEX KNOWLEDGE REPRESENTATION - CKR-1
We will refer to our scheme as CKR-1 in honor of KL-One [11] and in recognition of the fact that no solution is
likely to be complete or elegant. The scheme is in development. Following discussion from the previous
section, the scheme will be graphical and will have constructs to model objects, events, activities, situations,
concepts and associations.
Physical objects and concepts are not distinguished in CKR-1 – both are treated uniformly and are modeled by
rectangles with name of the concept/object as the label. Rectangles could also denote variables and these are
represented with the name of the variable starting with a capital letter as in “Value,” “Time.” We will use the
generic term ‘object’ to refer to objects and concepts, ‘event’ to refer to events, actions and situations and
‘concept’ to refer to objects and events. Events are represented by rectangles with a vertical bar towards the left
and marked as “E,” “A” or “S” to denote “events,” “actions,” or “situations” respectively. Because of the nature
of CK, attributes of objects are not given importance, so there are no extensive semantics here. Our philosophy
is that there can be a separate and independent mechanism at the implementation level supporting storage and
retrieval of routine properties of objects. ‘Interesting’ properties of objects are modeled by property links
described subsequently. Objects, concepts and events, situations and activities can be atomic or complex. A
‘disaster’ for e.g. is a complex concept since it could be caused by fire, earthquake etc. and could result in
property loss. On the other hand, ‘fire’ and ‘earthquake’ are simple concepts from the point of view of the
domain. A special construct that is provided is a “named” object or event which is a user-defined concept that is
useful in concept definition as well as in modeling abstractions. For example, loss could be defined as a
situation where an object loses value. The same construct also serves as a mechanism to partition the network
as well as to express propositions.
There are two relationship types, ‘structural’ and ‘descriptive’. The structural relationships are “class-subclass,”
“part-subpart” and “has-a” (‘is-a,’ ‘p-sp’, ‘has-a’). Descriptive relationships are modeled with “rel: <name>”
label on the link where <name> is the name of the relationship. This allows for any arbitrary relationship
without semantic baggage. There are also standard relationship types such as logical relationships (‘GT’, ‘LT’..),
Causal (‘CL’), Temporal (‘TE+’ and ‘TE-‘), probabilistic (‘PR’), business relationships (example ‘payer payee’
– ‘PP’, ‘legal obligation’ – ‘OB’) and case relationships (‘Subj’, ‘obj’..) that are part of descriptive relationships.
Structural relationships are distinguished from descriptive only for the benefit of the user. Additionally, from
the point of view of inferencing, generic reasoning mechanisms would suffice for standardized relationships, but
interpreters will need to provide individual reasoning for non-standard relationships. Properties are modeled
with a “p:” link. An asset could have a property, “value” modeled with a “p: value” between “asset” and a
variable denoting value (e.g. “Value”). A required property is modeled with “rp:” link. Consider, the board of
directors consists of elected officials.
Here the required property is that officers must be elected.
To
accommodate required properties, we will use the “rp:” link type. Since a concept could be related to more than
one concept, we use an oval to represent non-significant relationships between two objects and a double oval for
conjunctive relationships. A dotted double oval would mean the same thing for disjunctive relationships. Thus
some logical propositions could be made with the scheme. There is a special link “elaboration” or “e:”, that
uses the same standard relationship types, used primarily for elaborating on properties of a relationship. It also
serves as a crude mechanism for conditions and quantification.
s:is_a
mortgaged
asset
rp: owned
:
by
mortgagee
bank
s : is_a
individual
business
Fig 1. Modeling simple concepts with CKR-1
asset
We will illustrate CKR-1 with examples of knowledge. For example a mortgaged asset could be defined as an
asset for which some percentage is owned by a bank i.e. rest is owned by the mortgage. As shown in Figure 1,
mortgaged asset is connected by “s:is_a” structural link to asset. It has the required condition that it should be
partly owned by a bank. There is an “rp: owned by” link between the mortgaged asset and the owners which in
this case are both the bank and mortgagee. Note the use of ovals for multiple arguments.
A more abstract concept such as insurance coverage is difficult to represent. Insurance coverage is the legal
obligation of an underwriter to compensate the insured in the event of a loss. The concept is challenging
because it involves a number of abstract concepts such as “legal obligation,” “compensate,” and “loss.” It needs
to be represented as three assertions:
‘Compensate-1’: Insured suffers loss. It is understood that the loss amount is quantified.
‘Compensate-2’: Underwriter compensates insured for the loss amount.
Coverage: Compensate1 and Compensate2.
The first two propositions are shown in Figure 2. ‘Compensate-1’ is represented as a user-defined ‘event type’.
The insured has a ‘relationship’ with ‘loss’ i.e. the ‘subject’ of the ‘loss’. The ‘loss amount’ is a required
property for this concept; it refers to a variable ‘Loss amount’. ‘Compensate-2’ is similar. ‘Underwriter’ and
‘Insured’ share a ‘Payer-payee’ relationship with an ‘elaboration’ link (‘e:’) on the relationship to show the
compensated amount. The reader must note that ‘loss’, ‘insured’ and ‘underwriter’ are complex concepts.
‘Loss’ will be represented as a degradation of ‘state’ of an ‘entity’ - which could be a person, object, animal etc.
This method while awkward, neatly partitions concepts and keeps relationships manageable; it can be used for
more complex concepts such as ‘salvage rights.’ However, concepts such as ‘liability insurance’ defined as the
obligation to pay on behalf of the insured for damage or injury to others’ property for which the insured is
legally responsible will challenge the representation.
6
Conclusions
CKR-1 is an initial attempt to model complex knowledge in business domains. It derives its power from the
ability to define arbitrary concepts in terms of other concepts using the ‘object’ types, relationship types and the
concept definition facility. Given this power, it may be easy to define contradictory knowledge. Knowledge
consistency is therefore an issue. There was an explicit concern for simplicity to ensure that the scheme does
not suffer from the weight of its own features. It is for this reason that record type structures for objects and
relationships among these were not included. Properties of interest could be modeled with the “p:” and “rp:”
links. Also we started out with a limited set of constructs that could be subsequently expanded. For instance,
there is no construct for a conditional assertion. Being completely graphical it is expected that a system based
on CKR-1 would be easy to comprehend and at the same time easy to implement.
We have allowed
object/event properties to be concepts. At the implementation level, the property of an object when modeled as
a concept should be treated as an ‘alias’ or ‘derivation’ of the actual property. One could also conceive of an
‘extensional layer’ for the representation that has assertions about instances of the definitions in CKR-1. All of
these are obviously future directions for the research.
COMPENSATE-1
rel: subj
E
insured
loss
rp: loss
amount
Loss amt.
COMPENSATE-2
rel: pp
E
insured
underwriter
E: payment
amount
Loss amt.
Fig 2. Modeling complex concepts with CKR-1
References
1. Brachman, R.J. The Future of Knowledge Representation. Proceedings of AAAI90 1082-1092 (1990).
2. Kurfess, F.J. Neural Networks and Structured Knowledge: Knowledge Representation and Reasoning.
Information Sciences 13, 5-13 (1999).
3. Zeng, Y., Wang, R., Zeleznikow, J. & Kemp, E. A Knowledge Representation Model for the Intelligent
Retrieval of Legal Cases. International Journal Of Law And Information Technology 15, 299-319 (2006).
4. Shaalan, K., Rafea, M., Rafea.: A, KROL: a Knowledge Representation Object Language on Top of Prolog.
Expert Systems With Applications, 15, 33-46, (1998).
5. Welty, C.: Ontology Research. AI Magazine 24, 11-12, (2003).
6. Vranes, S., Stanojevic, M.: Design Knowledge Representation in Prolog/Rex, Engineering Applications of
Artificial Intelligence 12, 221-228, (1999).
7. Brachman, R.J. On the Epistemological Status of Semantic Networks. Associative Networks Representation
and Use of Knowledge by Computers, 3-50, (1979)
8. Morik, K. Acquiring domain models. International Journal of Man-Machine Studies, 26(1), Jan 1987, 93104.
9. Hayward, S. A., Weilinga, B. J., and Breuker, J. A.: Structured analysis of knowledge, Int. J. Man-Machine
Studies 26, , 487-498, (1987).
10. Lee, E.T.: Pictorial Knowledge Representation using Pictorial Semantic Networks, 6, 155-160, (1988).
11. Brachman, R.J., Schmolze, J. G.: An Overview of the KL-ONE Knowledge Representation System,
Cognitive Science 9, 171-216 (1985).
12. Mylopoulos, J., Borgida, A., Jarke, M., Koubarakis, M.: Telos: Representing Knowledge About Information
Systems, ACM Transactions on Information Systems, 8, 325-362 (1990).
13. Marcke, K. V., Jonckers V., Daelemans, W.. Representation aspects of knowledge based office systems. In
Esprit 87, Achievements and Impacts. New York: North Holland, 1226-1238. (1987)
14. Helbig, H.: Knowledge Representation and the Semantics of Natural Language. Springer-Verlag (2005).
15. Zarri, G. P.: A Knowledge Engineering Approach to Deal with Narrative Multimedia Documents. Lecture
Notes in Computer Science, 2244, 363 (2001).
16. Gomez, F.,. “A representation of complex events and processes for the acquisition of knowledge from
texts.” Knowledge Based Systems, 10, 237-251 (1998).
17. Amaravadi, C. S.: Knowledge Management for Administrative Knowledge, Expert Systems, 25 (2), 53-61,
May (2005).
18. Luthardt, C. M., Wiening, E. A. and Fraolli, M. J.: Property and Liability Insurance Principles, American
Institute for Chartered Property Casualty Underwriters / Insurance Institute of America Malvern, PA, fourth
edition (2005).
19. Davis, R., Shrobe, H., and Szolovits, P.: What is a Knowledge Representation? AI Magazine, 14(1), 17-33
(1993).
20. Hendrix, G. G. (1979). “Encoding knowledge in partitioned networks,” in Findler, Nicholas, Associative
Networks: Representation and Use of Knowledge by Computers, Academic Press: New York, 51-92, (1979).