=Paper=
{{Paper
|id=Vol-67/paper-3
|storemode=property
|title=On the Relations between Structural Case-Based Reasoning and Ontology-based Knowledge Management
|pdfUrl=https://ceur-ws.org/Vol-67/gwem2003-BS.pdf
|volume=Vol-67
}}
==On the Relations between Structural Case-Based Reasoning and Ontology-based Knowledge Management==
https://ceur-ws.org/Vol-67/gwem2003-BS.pdf
On the Relations between
Structural Case-Based Reasoning and
Ontology-based Knowledge Management
Ralph Bergmann & Martin Schaaf
University of Hildesheim
Data- and Knowledge Management Group
PO Box 101363
31113 Hildesheim, Germany
{bergmann | schaaf}@dwm.uni-hildesheim.de
www.dwm.uni-hildesheim.de
Abstract: This paper addresses the relations between ontology-based knowledge
management implemented by logic-oriented knowledge representation/retrieval ap-
proaches and knowledge management using case-based reasoning. We argue that
knowledge management with CBR does not only very much resemble but indeed is
a kind of ontology-based knowledge management since it is based on closely related
ideas and a similar development methodology, although the reasoning paradigms are
different. Therefore, we conclude by proposing to merge logic-oriented and case-
based retrieval and also to extend the current view of the semantic web architecture
respectively.
1 Motivation
Structural Case-based Reasoning (SCBR) and ontology-based knowledge management
(OBKM) are widely discussed as technologies for building organizational memory infor-
mation systems (OMIS) to support knowledge management [Al00; Be02; St02; Ab02].
When applying SCBR, the knowledge items (e.g., documents) are described by a charac-
terization constructed from a previously developed domain vocabulary. The collection of
all characterizations of the knowledge items constitutes the case base. In the traditional
CBR view, the characterization can be considered as the problem description with the
knowledge item itself (or a reference to it) as the solution. Queries to the OMIS are formu-
lated in terms of the domain vocabulary and the similarity measure is used during retrieval
to assess the utility [Be01] of knowledge items.
When applying OBKM, a domain ontology is constructed as a conceptual model for
knowledge items described by metadata annotations. The domain ontology is represented
using some logic formalism (e.g. F-Logic [Ki95]) that facilitates the specification of rele-
vant domain relations axiomatically. The metadata annotations of the documents are con-
sidered as facts and build, together with the ontology, a knowledge base that is the foun-
dation of the OMIS. A dedicated inference mechanism is used to answer queries
conforming to the logic formalism and the terms defined in the ontology.
By comparing these two approaches, it becomes obvious that both are based on the same
principle: knowledge items are abstracted to a characterization by metadata descriptions,
which are used for further processing. This characterization is based on some vocabu-
lary/ontology that is a shared conceptualization of the domain among the computer agents
and users of the OMIS. Despite of these similarities, there is currently not much cross-
citation in papers addressing the one or the other approach. Although some of the relations
between both approaches might be implicitly clear, they have never been analyzed sys-
tematically and explicitly stated before. With this paper we want to unveil those relation-
ships and break the borders between both approaches by claiming that KM by SCBR is a
kind of OBKM. The difference lies mainly in the inference mechanism used: logic vs.
utility-based reasoning. With this paper, we also want to contribute to a better understand-
ing within the (recently) merged German SIGs on KM and CBR. This might be a starting
point for further exploring new synergies.
2 Ontology-based Knowledge Management
The notion of Ontology-based Knowledge Management (OBKM) refers to activities con-
cerning the creation, accumulation, sharing, reuse and further development of knowledge
in an organization within the context of explicitly defined conceptual models. The term
ontology stands for the representation of a conceptual model and is the core of OBKM. Its
philosophical origin goes back to Aristotle who is supposed to be the founder of meta-
physics as a separate discipline. According to [BS91], metaphysic and ontology coincide
partially and can be regarded from two different points of view: a) with respect to its ob-
ject, e.g. thing, Ding, being etc., b) in relation to other philosophical and non-
philosophical disciplines. Within this paper we will emphasize only the technical aspects
of OBKM and from this perspective we consider an ontology as formal description of the
entities, relationships, and constraints that make the conceptual model. Depending on the
expressiveness and the degree of formality of the underlying representation language, an
ontology can range from a simple taxonomic hierarchy of classes to a logic program util-
izing first-order predicate logic, modal logic, or even higher order logics with probabili-
ties. In contrast to classical expert systems, ontology-based systems typically distinguish
between multiple levels of knowledge from common sense knowledge to highly specific
domain knowledge.
2.1 Ontological Engineering
As a relatively new sub-discipline of knowledge engineering, ontological engineering
focuses on the systematic development of ontologies in a reusable and modular fashion
and their maintenance. Ontological engineering has probably its origins in the CYC pro-
ject [LG90], which first addressed the issue of reusability and modularity of large knowl-
edge bases, and the development of the knowledge representation language KL-ONE
[BS85], which was the first logical formalization of a frame-based semantic net-
work.Other approaches for developing knowledge-based systems include contexts respec-
tive microtheories, compositional modelling, or knowledge composition and merging
([Gu91], [FF91], [CP97], [NM00]).
KL-ONE inspired an entire new discipline in logical frame-based languages called termi-
nological logics or description logics. It distinguishes between a T-Box, which is a sub-
sumption hierarchy called the axioms or ontology of the knowledge base, and the A-Box
that comprises the instance level knowledge (facts etc.). The T-Box is somewhat similar to
a schema in relational database theory, while the A-Box particularly corresponds to tuples
of a database.
Although engineering principles for ontologies emphasize modularity and reusability, this
is still very difficult to achieve for systems beyond research prototypes. It requires formal
and declarative representation languages that have a standardized syntax, a well founded
semantic, and the sufficient expressiveness for real world applications. Consequently, the
most important advances in ontological engineering currently come from the research and
standardization efforts for representation languages and models for the semantic web,
which are developed on top of XML. A variety of languages compete to be the language
of choice like the XML Ontology Exchange Language (XOL) [Ka99], the Ontology Infer-
ence Layer (OIL) [Fe00], the DARPA Agent Markup Language (DAML) [DA02], the
Resource Description Framework (RDF) [LS99] and the corresponding RDF Schema
Specification [Ha02], or XML Topic Maps (XMT) [PM01]. In the following we will
briefly show two approaches, RDF(S) and DAML/OIL, that already have reached a cer-
tain level of maturity.
2.2 RDF(S)
The Resource Description Framework (RDF) [LS99] is a W3C recommendation for en-
coding, exchange, and reuse of structured metadata and uses XML as underlying lan-
guage. The RDF Data Model is based on resources and properties. A resource is every-
thing that can be uniquely identified by a Uniform Resource Identifier (URI). A property
can associate resources with values and can be labeled by a name illustrating the relation-
ship. RDF defines a set of atomic types for property values like strings or integers. Fur-
thermore, a property value may be another property enabling the specification of directly
labeled graphs, which can be interpreted as a semantic network, or a collection of values.
RDF is an easy to use formalism that resembles very much an entity relationship diagram.
Meanwhile, it has become the foundation of higher-level standardizations and many on-
tology-based systems allow using RDF for metadata (A-Box) but keep a proprietary for-
malism for the ontology itself. An approach to close this gap led to the development of
RDF Schema [BG02] that denotes some special associations, for instance a “subClassOf”
relation, and thereby provides mechanisms to define classes of resources, to restrict possi-
ble combinations of classes and relationships, and detect violations of those restrictions.
2.3 DAML+OIL
Although current efforts of the W3C aim to supply a model-theoretic semantic for RDF
and RDF Schema [Ha02b] in order to enable a unique interpretation for automatic reason-
ing, RDF(S) still lacks the necessary expressive power for many applications. Hence, two
extensions to RDF(S), namely the DARPA Agent Markup Language (DAML) [DA02]
and the Ontology Inference Layer (OIL) [Fe00], have been proposed and finally merged
into DAML+OIL [Ha02a] because of their similarity. DAML+OIL is based on description
logics encoded in RDF. In addition to RDF(S), DAML provides the ability to express the
equivalence or disjointness of classes, additional restrictions like cardinality, or to build
new classes as intersections or complements of other classes. Furthermore, DAML has
been integrated with XML Schema providing a rich set of data types, which are still miss-
ing in RDF(S). The further evolution of DAML+OIL is the Ontology Web Language
(OWL) [De02] for what a first language reference as a W3C working draft is available,
now.
2.4 On the Usage of Ontologies in OBKM
Gruber [Gr93] defines an ontology as “an explicit specification of a conceptualization”
committed by a set of agents “so that they can communicate about a domain of discourse”.
This definition proposes ontologies as a formal representation of background knowledge
in a multi-agent environment enabling, for instance, distributed reasoning across multiple
knowledge bases. By assuming any problem or task specific knowledge being imple-
mented by the agents, it implies also an important design principle for ontology-based
systems with respect to modularity and reusability.
A more focused use for ontologies, especially for OBKM purposes, is the systematic crea-
tion and storage of knowledge assets based on the characterization of knowledge items
[Fe98]. Here, ontology and characterization are the key for content-based access (filter,
retrieve, render, etc.) to knowledge items [Gu99]. Furthermore, the ontology itself can
serve as a communication base about the products and processes e.g. for generating expla-
nations to users. In the following we will illustrate these very basic ideas by a simple ex-
ample. Table 1 shows an excerpt of an F-Logic1 [Ki95] specification how it might be used
in the FLORID [FL02] system as ontology for a document management approach main-
taining publications submitted to a particular event (e.g. a workshop).
publication[ workshop : event.
title => string ; event[
authors =>> author ; title => string ;
location => URI ; cfp => URI ;
submittedTo => event; appDay @( string ) => date;
topics =>> topic; topics =>> topic;
].
].
knowledgeManagement :: topic.
experienceManagement :: knowledgeManagement.
knowledgeRepresentation :: knowledgeManagement.
∀ X : publication[topics− >> T ]
X ,T ,E
← X [submittedTo− > E] ∧ E[topics− >> T ] (*)
Table 1: Excerpt of an Ontology in F-Logic
The specification contains only F-Logic signatures that correspond to the T-Box in KL-
ONE. They indicate, for example, that each publication has authors, a location given by a
Uniform Resource Identifier (URI), and the link to the particular event. An event, e.g. a
workshop, has some dates associated (e.g. submission deadline) as well as a set of topics.
1
F-Logic is a descendant of KL-ONE, which does not distinguish between A-Box and T-Box knowledge.
Furthermore, rule (*) formalizes the simple relationship between the associated topics of
the document and the topics of the event.
The necessary link to a knowledge item, e.g. a PDF-file containing a publication or a web
site announcing a workshop, is now established by a meta-data characterization that
somehow conforms to the ontology in Table 1. Such a characterization does not require an
expressive underlying formalism but must allow identifying the concepts and associated
entities of the ontology in a unique and unambiguous way. For instance Figure 1 illus-
trates the usage of RDF for characterization of a publication. The properties of the RDF
description are labeled as the slots of the corresponding F-Logic frame and assign re-
sources as values.
“On the Relations
between …"
title
rdf:type rdf:bag
BS02
authors rdf:_1 turing
submittedTo
rdf:_2 neumann
location
http://www.uni.de/...
IJCAI03
Figure 1: RDF Example Characterization
From a theoretical point of view, it does not matter if such a characterization is part of the
knowledge itself or provided separately. Furthermore, depending on the ontology-based
system, conformance requirements are more or less strict. Typically, even a simple and
weakly typed entity-relationship representation like RDF is sufficient. Some ontology
representation languages like DAML/OIL extend RDF by own constructs that allow inter-
preting the ontology itself as a kind of schema for the characterization. In addition to tra-
ditional database schemes, ontologies provide an axiomatic base of the stored knowledge
items making it possible to implement retrieval, filtering etc. by derivation. For instance,
retrieval of knowledge items is, beside communication and cooperation, the most frequent
application of ontology-based systems in OBKM and can be facilitated by queries like
∀x ← X : publication[topics− >> {exp erienceManagement}, authors− >> {turing}]
that selects all documents with (co-) author turing related to the topic experienceManage-
ment. An appropriate inference system can answer the query by proving if it is a conse-
quence from the ontology and the set of characterizations (considered as facts) and
thereby finding characterizations as substitutions for the variable X that represent the re-
quested documents. Finally, we would like to mention, that using the ontology as an
axiomatic base for a logic calculus, derivation is, of course, restricted to the deductive
closure of the axioms.
3 Structural CBR for KM
In CBR there are three main approaches that differ in the sources, materials, and knowl-
edge they use [Be99]. The textual CBR approach is similar to traditional information re-
trieval in that it works directly on the text documents.There is no a-priori domain model,
but similarity measures can be introduced between the words occurring in the documents.
Therefore, retrieval is very similar to keyword matching, but considers the similarity for
document scoring.
Conversational CBR captures the knowledge contained in customer/agent conversations.
A case is represented through a list of questions that varies from one case to the other.
There is no domain model and no standardized structure for all the cases.This approach is
very useful for domains where a high volume of simple problems must be solved again
and again.
The structural CBR approach is the third approach and relies on cases that are described
with attributes and values that are pre-defined. In different SCBR systems, attributes may
be organized as flat tables, or as sets of tables with relations, or they may be structured in
an object-oriented manner. The SCBR approach is useful in domains where additional
knowledge, beside cases, must be used in order to produce good results. In the following
we focus on the SCBR approach.
3.1 Knowledge Containers
In the SCBR approach, knowledge is represented in one of the four knowledge containers:
the vocabulary used, the similarity measure, the solution transformation, and the case-base
[Ri95]. In principle, each container is able to carry all the available knowledge, but this
does not mean that this is advisable. The first three containers include compiled knowl-
edge (with “compile time“we mean the development time before actual problem solving,
and “compilation“ is taken in a very general sense including human coding activities),
while the case-base consists of case-specific knowledge that is interpreted at run time, i.e.
during actual problem solving. For compiled knowledge the maintenance task is as diffi-
cult as for knowledge-based systems in general. However, for interpreted knowledge, the
maintenance task is easier because it results in updating the case-base only. In our opin-
ion, a main attractiveness of CBR comes from the flexibility to decide pragmatically
which container includes which knowledge.
When applying SCBR to knowledge management, the characterization of the knowledge
items is stored in the case base. Ideally, the vocabulary used to represent the cases is de-
veloped a-priori for the domain at hand and is considered as stable.
3.2 Vocabulary Representation in CBR
State-of-the-art CBR systems make use of an object-oriented vocabulary representation
[Ma94; AP95]. Object-oriented case representations can be seen as an extension of the
attribute-value representation. They make use of the data modeling approach of the object-
oriented paradigm including is-a and other arbitrary binary relations as well as the inheri-
tance principle. Such representations are particularly suitable for complex domains in
which cases with different structures occur.
The structure of an object is described by an object class that defines the set of attributes
together with a type (set of possible values or sub-objects) for each attribute. Object
classes are arranged in a class hierarchy, that is usually a n-ary tree in which sub-classes
inherit attributes as well as their definition from the parent class. Moreover, we distin-
guish between simple attributes, which have a simple type like Integer or Symbol, and so-
called relational attributes. Relational attributes hold complete objects of some (arbitrary)
class from the class hierarchy. They represent a directed binary relation, e.g., a part-of
relation, between the object that defines the relational attribute and the object to which it
refers. Relational attributes are used to represent complex case structures. The ability to
relate an object to another object of an arbitrary class (or an arbitrary sub-class from a
specified parent class) enables the representation of cases with different structures in an
appropriate way. Different representation languages for the vocabulary have been devel-
oped such as CASUEL [Ma94] and the XML-based Orenge Model Markup language
[ST02] used in the commercial CBR tool orenge from empolis.
When applying CBR for KM, the development of the vocabulary is a crucial issue. The
vocabulary is used for characterizing the knowledge items to be searched. When develop-
ing a vocabulary, the following must be considered:
Utility Distinguishability: The vocabulary must be complete in the following sense: it
must be possible to decide based on the selected classes and attribute values whether it is
possible to make use of the knowledge item in a new situation. If it is not possible to dis-
tinguish two knowledge items that must be distinguished based on the attributes in the
characterization, new attributes or classes must be added to enable the differentiation be-
tween the two. This criterion has been formalized in [Be02].
Common Understanding: There must be a common understanding of the use of the vo-
cabulary items (and the entire representation language) among the persons or agents in
charge of characterizing knowledge items and the users formulating a query with these
items. That is, all people involved should characterize a knowledge item the same way
and should characterize their queries the same way. In many KM project that involve CBR
technology, it has been recognized that the development of such a shared vocabulary is a
very difficult task explicitly addressed in development methodologies for CBR applica-
tions, such as the INRECA methodology [Be99].
Besides these criteria, one usually aims at achieving a vocabulary in which the attributes
are independent from each other (i.e., there is no functional dependency) and the set of
attributes is minimal (i.e., there is no redundant attribute). Although these criteria help in
the engineering of appropriate similarity measures, they are not mandatory and are often
ignored if there is not one single clearly defined task to be supported with the OMIS.
3.3 Characterizations
For applying CBR for KM, the cases to be stored in the cases base usually consist of a
characterization part and a lesson part. The characterization part is represented using the
vocabulary and consists of a collection of objects instances from the classes of the vo-
cabulary. The lesson part just consists of a link to the knowledge item that is character-
ized.
For a given set of knowledge items, these characterizations must be constructed either
manually, i.e. the documents must be annotated with their characterization, or by applying
text-mining approaches. In the latter case, syntactic text analysis rules can be applied to
map certain text patterns to attribute values or object instances of the characterization.
3.4 Similarity, Utility, and Rules
The similarity measures used in CBR are of critical importance during the retrieval of
knowledge items for a new query. Unlike in early CBR approaches, the recent view is that
similarity is usually not just an arbitrary distance measure, but a function that approxi-
mately measures utility. The similarity measure assesses the utility of a knowledge item
only based on the characterization. The knowledge container view made clear that the
similarity measure itself contains (compiled) knowledge. This is knowledge about the
utility of a knowledge item re-applied in a new context [Be01]. Connected with this ob-
servation was the need to model similarity knowledge explicitly for an application do-
main, as it is done with other kinds of knowledge too. Current similarity modeling ap-
proaches are tightly integrated with object-oriented vocabulary representations [Be02].
The similarity measure allows the retrieval of knowledge items that do not exactly match
the query, but which can differ in many ways. The similarity value imposes a partial or-
dering on the knowledge items according to their relevance for the current query, which is
an important feedback to the user of the OMIS.
Beside the use of similarity measures, CBR research also came up with approaches for
integrating rule-based background knowledge [Aa91, Be96, Be02]. For example with
completion rules, it is possible to derive deductive, logical conclusions from the charac-
terization of knowledge items. These conclusions are stored as part of the characterization
for each knowledge item, i.e., for each knowledge item, the deductive closure (which must
of course be guaranteed to be finite) is determined and stored in as part of the case in the
case base. During retrieval the deductive close is also computed for the query and the
similarity is determined between the extended representations. The most common use of
this approach is for determining derived (or also called virtual) attributes that are com-
puted from the given representation for the means of similarity assessment.
4 Relations between Structural CBR and OBKM
From the previous analysis of knowledge management by SCBR and OBKM it should
have become clear that both rely on metadata annotations that serve the purpose of charac-
terizing instead of formalizing knowledge items. In CBR these characterizations are called
cases and, basically, it does not matter where the representation of the characterization is
physically located. It may be stored together with the knowledge item itself (e.g. by using
a structured XML-based format) or, as with CBR, in a case base. A more important rela-
tionship is given by the SCBR vocabulary that very much resembles the ontology in
OBKM. Both are formal models for restricting the possible interpretations of metadata
annotations thereby providing the necessary background knowledge for semantic-based
access to knowledge items. It is obvious that the fundamental types of knowledge of
SCBR and OBKM are strongly related as shown in Figure 2. Hence, from these relation-
ships follows that design principles for SCBR and OBKM are closely related, too. Several
CBR development and maintenance approaches have been researched, for instance the
INRECA methodology [Be99, Ta01], and they are at least partially structured according
to the CBR knowledge containers and do address the vocabulary development as well. For
OBKM, [St02] follows a KADS oriented methodology and presents a meta-process for
systematic ontology development that utilizes SE-tools.
Ontology-based Approach SCBR Approach
Service Service
Utility
oriented
Logic
Search
Inference
Problem
Knowled Vocabulary
Problem
ge Knowledg
e Sim. Model
Ontology
Conceptual Model
Meta Data
Characterization Cases
Annotation
Knowledge Items (e.g. documents, processes)
Figure 2: Ontology vs. SCBR Knowledge Containers
An important difference between both approaches results from the fact that SCBR-
systems are often isolated and closed in the sense that they are not developed with respect
to cooperation with other systems. For that reason, though research of vocabulary repre-
sentation languages led to expressive languages [Ma94; AP95], standardization was not a
big issue in past CBR research. Most SCBR-based systems rely on proprietary, sometimes
even XML compliant, languages for the vocabulary and the cases but do not allow an easy
knowledge exchange. However, current research for distributed CBR [LS02] shows how
CBR can benefit from systems that are able the search across multiple-case bases. Of
course, this is only possible if a standardized, shared knowledge representation language
enables unambiguous interpretation of cases stored in the different case bases.
The coincidence of an SCBR vocabulary and an ontology becomes even more prevalent if
we compare vocabulary representation approaches to ontology representation languages
mentioned earlier in this paper. They provide nearly the same expressiveness by utilizing
object-oriented technology allowing the specification of concept hierarchies, arbitrary
binary relations, types, and rules e.g. like definite clauses in horn logic. Neglecting the
fact that an ontology typically serves many purposes one can say that a SCBR vocabulary
is an ontology of the domain of discourse underlying the SCBR application.
The major difference between the SCBR and OBKM approach results from different rea-
soning strategies. As mentioned before, most ontology-based systems utilize logic-based
inference, while SCBR systems provide a search functionality that makes use of similarity
measures for ranking results according to their utility with respect to a given query. In our
opinion, both reasoning strategies complement each other very well. On the one hand,
logic deduction produces only correct and provable results, which are consequences of the
ontology and metadata. Computer agents normally require this for further processing. On
the other hand, SCBR retrieval suggests results even in the case that no exactly matching
answers can be found. This has been proven as highly efficient in many real-world appli-
cations [Be99]. For realizing the utility-oriented search, SCBR systems introduce an addi-
tional kind of knowledge that is the similarity model. Although the similarity model is
part of the problem knowledge, it is a first-class citizen of each CBR system in the sense
that constructs required for specification are usually part of the vocabulary representation
language. This emphasizes the more problem-oriented approach of SCBR.
5 Conclusions
Within this paper we analyzed knowledge management by SCBR and ontology-based
knowledge management and showed a strong relationship between both approaches with
respect to technological but also to methodological issues. However, we identified several
differences, too, being a potential source for synergies. For example, OBKM comes up
with a variety of standardized knowledge representation languages. Their incorporation
into SCBR-based systems would enable to apply CBR technology to a broader range of
applications. As a consequence, this makes it possible to develop unified modeling tools
for greater flexibility. The decision between the different reasoning strategies supported
by SCBR and OBKM may be postponed to a later phase of the development. Conversely,
ontology engineering could take advantage of experiences with real-world SCBR applica-
tions that are discussed, for example, in [Be99]. Finally, by having a closer look at the
current state of the semantic web, it becomes obvious that, even under the assumption of
standardized knowledge representation languages, ontologies are often highly specific to
their domain of discourse. Hence, interoperability can only be achieved by some kind of
semantic unification. For that purpose, a strict, logic-oriented approach does not seem to
be the ultimate solution, especially when only an approximation of unification is possible.
SCBR, beside arbitrary probabilistic approaches, seems to be a good starting point for
further research because of its strong relationship to OBKM. It introduces the similarity
model as another type of knowledge that recommends itself to become part of future ex-
tensions to knowledge representation standards.
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