Waking from a Dogmatic Slumber A Different View on Knowledge Management for DL’s
Workshop on Semantic Interoperability in e-Science
Martin Doerr
Center for Cultural Informatics
Institute of Computer Science
Foundation for Research and Technology - Hellas
London, UK
March 30, 2006
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Knowledge Management for DLs
Traditional Use Cases
“There are no new research challenges in DL. There are only the ones
from 30 years ago we still have not solved” (anonymous, ECDL2005)
Apologies: I’ll be deliberately provocative and possibly incomplete. Don’t take me
too serious.
What are Digital Libraries (or more generally Digital Memories )?
Information systems preserving and providing access to source material,
scientific and scholarly information, such as libraries of publications,
experimental data collections, scholarly and scientific encyclopedic or
thematic databases or knowledge bases.
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Knowledge Management for DLs
Traditional Use Cases
The traditional library task:
 Collect and preserve documents and provide finding aids
 The job is solved, when the (one, best) document is handed out. “All you want is in
this document”.
Implementing the finding aids:
 Assumption: User knows a topic, characterized by a noun, or knows associations
of the topic uncorrelated to the problem to be solved (e.g. “organic farming” for
“host-parasite studies”.)



Retrieval goal: Aggregation of like items/ nearly equivalent items, best fit.
Means: Metadata, classification, (subject hierarchies and precise match with
categorical and factual KOS/authorities), keyword extraction.
Semantic interoperability is limited to the aggregation task: Metadata are mainly
homogeneous (DC, MARC etc.), challenge is the matching of terminology (KOS).
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Knowledge Management for DLs
Problems
 No support to solve a problem,
 e.g., what species is this object?

No support to learn from the aggregated source, to retrieve by contexts,
 e.g., Which professions had the relatives of van Gogh?
 e.g., Which excavation drawings show the finding of this object?
 e.g., Which resolution had Galileo’s telescope when he observed... (in general how
reliable was a scientific observation, can we correct the values found?).

No support to integrate complementary information in multiple sources into
new insight,
 e.g., Which where the clients of van Gogh’s paintings?

No support for cross-disciplinary search.
 e.g. Ecology, ethnology and biodiversity. Biology and archaeology.
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Knowledge Management for DLs
Grand Challenge
DLs should become integral parts of work environments as sources to
find integrated knowledge and produce new knowledge.
But How ?
Employing “global networks of knowledge”:

Understanding of user questions, scientific discourse
— expressing user questions in terms of core conceptualizations (relationships, core concepts)

Virtual or physical data and metadata integration
— metadata schema integration as complementary elements of a larger global schema, and not as
alternative views of the same content.
— mapping/merging of terminology *
— matching factual data (“data cleaning”)
 Query systems taking into account the knowledge gaps and varying precision or
abstraction levels in the integrated data
— processing of “unknown” values and overlapping concepts in data and queries.
* main focus of the Semantic Web
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Knowledge Management for DLs
Grand Challenge
Is that a dream ?
“Isn’t Digital information and human knowledge is too diverse, fuzzy, case-dependent?”
“Is the Semantic Web much further than AI decades before?”
We regard suitable knowledge management as the key.
We distinguish:
1. Core ontologies for “schema semantics”, such as: “part-of”,”located at”,”used for”, “made
from”. They are small and rich in relationships that structure information and relate content.
2. Ontologies that are used as “categorical data” for reference and agreement on sets of things,
rather than as means of reasoning, such as: “basket ball shoe”, “whiskey tumbler”, “burma
cat”, “terramycine”. They do not structure information. They aggregate, more than integrate.
3. Factual background knowledge for reference and agreement as objects of discourse, such as
particular persons, places, material and immaterial objects, events, periods, names.
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Knowledge Management for DLs
Preconceptions and Solutions
“Libraries should not depend on domain specific needs. Domains are too many
and too diverse. DLs need a generic approach.”


This seduces us to only employ intuitive top-down approaches for generic
metadata schemata. As a result, when the fantasy is exhausted, research stops.
We need deep knowledge engineering, generalizing in a bottom-up manner from
real, specific cases to find the true generic structures across multiple domains. We
need interdisciplinary work on real research scenarios.
“Ontologies are huge, messy, idiosyncratic and domain dependent. Mapping is
the only generic thing we can do”


We are transfixed with ontologies used as “categorical data” (term lists), for which
this statement is mainly true. We oversee the different character of ontologies
describing “schema semantics”.
The latter may pertain to generic classes of discourse, rather than to domains. They
are the candidates to find generic relationships that integrate both factual and
categorical knowledge in a meaningful way even for specific application. We need
interdisciplinary work.
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Knowledge Management for DLs
Preconceptions and Solutions
“Queries are mainly about classes. The main challenge of information integration
is the integration of classes (terms).”


We believe this is not sufficiently supported by empirical studies. Query
parameters pertain to universals and particulars.
We need to systematically analyze the workflow of research work and the original
research questions in each phase. We need to provide access by factual
relationships (Amit Sheth), such as “georeferencing”.
“Manual work is not scalable or affordable. Only fully automated methods have a
chance”


This seduces us to discard the quality of manual, intellectual decisions. Yet billions
of people produce content manually. Wikipedia demonstrates, that the above is not
true.
We need to design the interactive processes and the awarding of users to
massively involve Virtual Communities / Organisations in cataloguing, data
cleaning and ontology development. We need semiautomatic, highly distributed
algorithms. We need interdisciplinary work.
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Knowledge Management for DLs
Do we talk about the same thing?
“We need more reasoning!”

This is true. But what sort of reasoning? And before any reasoning can be done,
data must be connected, in a “global network of knowledge”. We must first clarify:
Do we talk about the same thing?
Requisites for a global network of knowledge:
1. A sufficiently generic global model (core ontology with the revelant
relationships).
2.
3.
4.

Methods to populate the network: knowledge extraction / data transformation.
Theories of negotiating identifier equivalence across contexts (databases).
Algorithms for global, massive, distributed, semiautomatic detection of
identifier equivalence (data cleaning ) across contexts and to curate referential
integrity in order to create, maintain and improve the consistency of global
networks of knowledge as a continuous process (not making yet another
database).
And only then we can do advanced reasoning and intelligent query processing ...
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Knowledge Management for DLs
Example: the core ontology ISO21127
The CIDOC Conceptual Reference Model (ISO/FDIS 21127)

is a core ontology describing the underlying semantics of data schemata
and structures from all museum disciplines and archives. Now being
merged with IFLA FRBR concepts.


It is result of long-term interdisciplinary work and agreement.
In essence, it is a generic model of recording of “what has happened” in
human scale, i.e. a class of discourse.

It can generate huge, meaningful networks of knowledge by a simple
abstraction: history as meetings of people, things and information.

It bears surprise: more effective metadata structures, and linking
schemes can be created from it.
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Knowledge Management for DLs
Example: History as Meetings of People..
t
Brutus
Caesar’s
mother
coherence
volume of
Caesar’s death
Caesar
Brutus’
dagger
coherence
volume of
Caesar’s birth
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Knowledge Management for DLs
Example: …Things and Information
t
coherence volume of
second announcement
Victory!!!
coherence volume of
first announcement
other
Soldiers
2nd Athenian
Victory!!!
1st Athenian
runner
coherence volume of
the battle of
Marathon
Marathon
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Athens
S
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Knowledge Management for DLs
Example: Meetings and Metadata
Type:
Title:
Title.Subtitle:
Date:
Creator:
Publisher:
Subject:
Text
Protocol of Proceedings of Crimea Conference
II. Declaration of Liberated Europe
February 11, 1945.
The Premier of the Union of Soviet Socialist Republics
The Prime Minister of the United Kingdom
The President of the United States of America
State Department
Postwar division of Europe and Japan
Metadata
Documents
About…
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“The following declaration has been approved:
The Premier of the Union of Soviet Socialist Republics,
the Prime Minister of the United Kingdom and the President
of the United States of America have consulted with each
other in the common interests of the people of their countries
and those of liberated Europe. They jointly declare their mutual
agreement to concert…
….and to ensure that Germany will never again be able to
disturb the peace of the world…… “
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Knowledge Management for DLs
Example: Meetings and Metadata
Type:
Title:
Date:
Publisher:
Source:
Copyright:
References:
Image
Allied Leaders at Yalta
1945
United Press International (UPI)
The Bettmann Archive
Corbis
Churchill, Roosevelt, Stalin
Photos, Persons
Metadata
About…
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Knowledge Management for DLs
Example: The ISO21127 Solution
E52 Time-Span
E39 Actor
E53 Place
7012124
February 1945
P82 at some
time within
E7 Activity
E39 Actor
“Crimea Conference”
E38 Image
P86 falls within
E65 Creation
Event
E39 Actor
*
P81 ongoing throughout
E52 Time-Span
E31 Document
“Yalta Agreement”
11-2-1945
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Knowledge Management for DLs
CIDOC CRM Top-level Entities
E55 Types
refer to / refine
E39 Actors
E28 Conceptual Objects
E18 Physical Thing
participate in
affect or / refer to
location
E2 Temporal Entities
E52 Time-Spans
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E53 Places
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The CIDOC CRM
A Classification of its Relationships
 Identification of real world items by real world names.
 Classification of real world items.
 Part-decomposition and structural properties of
Conceptual &
Physical Objects, Periods, Actors, Places and Times.
 Participation of persistent items in temporal entities.
— creates a notion of history: “world-lines” meeting in space-time.
 Location of periods in space-time and physical objects in space.
 Influence of objects on activities and products and vice-versa.
 Reference of information objects to any real-world item.
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The CIDOC CRM
Example: The Temporal Entity Hierarchy
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Knowledge Management for DLs
Hypertext is wrong: Documents contain links!
Linking documents
by co-reference
Primary link
corresponding to
one document
CIDOC CRM
Core Ontology
Deductions
Instance of
Integration by
Factual Relations
Donald Johanson Johanson's Expedition
Discovery of
Lucy
real world
nodes (KOS)
Cleveland Museum
of Natural History
AL 288-1
Lucy
Ethiopia
Hadar
Documents in
Digital Libraries
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Knowledge Management for DLs
Identifier Equivalence
Query “Friends of a Friend”
Content
2. query
input: “Κώστας”
output: “George”
Source 2
Content
Read output:
find “Kostas”,
guess
“Κώστας”
1. query
input: “Martin”
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Source 1
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Knowledge Management for DLs
Curating Identifier Equivalence
Join across sources by transitivity
of identifier equivalence
local ids
Content
find equivalence
.
.
.
.
output: “George”
Source 2
Join
Dyn amic
li nk
find
id
equivaL
lence
i
n
k
local ids
Content
query
.
.
.
.
input: “Martin”
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Source 1
match
t
a
b
l
match
e
“Κώστας” /
“Kostas”
.
.
.
.
Authority service
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Knowledge Management for DLs
Conclusions
It is feasible to create effective, sustainable, large-scale networks of knowledge:
 The CRM and its extensions seems to have the power to integrate historical knowledge in
Archives Libraries and Museums.
 The CRM can be applied in surprisingly simple forms (CRM Core).
Is the CIDOC CRM more generally applicable to e-science?
 Humanities collect factual knowledge. The CRM is a model of factual relationships at first.
 Sciences collect categorical knowledge. But we oversee the record of experimental data, which
justifies this knowledge and is by far larger than the resulting categorical knowledge.
 Descriptive sciences already produce both categorical and factual knowledge.
Thesis:
 the evaluation, access to and reuse of the record of experimental data of the sciences has a
historical dimension: who measured what, when, where under which circumstances, with which
methods, which knowledge, which care?
 We overestimate the relevance of domain categories, and completely oversee the relevance of
the historical relationships in our scientific reasoning (I am physicist).
 Try the CRM, as a starting model for e-science, and see what else is needed.
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Knowledge Management for DLs
Conclusions
If we rethink old positions, we will find surprising new answers to
“..an information model for digital libraries that intentionally moves 'beyond search
and access’, without ignoring these functions, and facilitates the creation of
collaborative and contextual knowledge environments.”
(C.Lagoze, D-Lib Magazine 2005)
But:
 We need a massive investment in understanding and generalizing the intellectual


processes and original research questions in interdisciplinary work.
We have to do research in dynamic collaborative knowledge organization forms,
formal processes and algorithms that converge to higher stages of knowledge
integration.
The large networks of integrated knowledge to come will need continuous
maintenance with new, specific social organisation forms and GRID-like resource
access, and they may look very different from our current systems…
(This is again a 30 years old challenge, are we closer now?)
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