AN EXTENDABLE JAVA FRAMEWORK FOR INSTANCE

SIMILARITIES IN ONTOLOGIES

Mark Hefke, Valentin Zacharias, Andreas Abecker, Qingli Wang

FZI Research Center for Information Technologies at the University of Karlsruhe, 76131 Karlsruhe, Germany

Ernst Biesalski, Marco Breiter

DaimlerChrysler AG, Plant Wörth, Daimlerstrasse 1, D-76742 Wörth, Germany

Keywords: Ontologies, Similarity, Knowledge Management, Case-Based Reasoning.

Abstract: We present the conceptual basis and a prototypical implementation of a software framework for syntactical

and semantical similarities between ontology instances. Our focus comprises both the implementation of

specific, ontology-based similarity measures and their flexible, efficient, and extensible combination.

1 INTRODUCTION

The importance of ontologies increased significantly

in the recent years with usage scenarios such as the

representation of complex knowledge in application

domains like manufacturing, transportation, or life-

sciences

For instance, the DAML Ontology Library

( http://www.daml.org/ontologies/ ) contains 282 on-

tologies in different domains. Ontologies often serve

as a common model for the semantic integration of

heterogeneous information sources

Here, one has to

deal with identifying similar entities (concepts, attri-

butes, relations, instances or even instance sets)

within and between ontologies, in order to be able to

build semantic bridges between distributed ontolo-

gies (ontology mapping), or to aggregate comple-

mentary ontologies to a common one (ontology mer-

ging). Due to the complex structure of ontologies

(concepts, concept hierarchies, inverse, symmetric

or transitive relations between concepts, etc.), tradi-

tional syntactical similarity measures like, e.g.,

string comparison or distance-based similarity for

numerical attribute values alone are not able to re-

turn reasonable results for the identification of simi-

lar entities. Therefore we have developed an exten-

sible framework for the flexible combination and pa-

rameterization of syntactical and semantical similari-

ties, in order to compute the similarity between

(sets) of ontology instances. The framework is

designed such that it can easily be integrated in any

ontology-based system.

The paper is organized as follows: In Section 2,

we introduce basic notions for defining similarity

measures and then construct some ontology-related

similarity measures. In Section 3, we show how such

approaches can be implemented in a modular and

extensible software framework. After discussing

related work in Section 4, we conclude in Section 5.

2 ONTOLOGY-BASED

SIMILARITY MEASURES

2.1 Ontologies

In Computer Science, ontologies are formal models

of a domain which support the communication bet-

ween human beings and/or machines. On a socio-

cultural level, ontologies demand a shared under-

standing of concepts and relationships. Technically,

let us use the following definition – which comprises

both schema and instance data in the ontology.

Definition 1 (Ontology).

(,,,,):= , ,OCHCRCHRIRIA

An ontology O is a tuple consisting of: concepts C

of the schema that are arranged in a subsumption

hierarchy HC. Between single concepts exist rela-

263

Hefke M., Zacharias V., Abecker A., Wang Q., Biesalski E. and Breiter M. (2006).

AN EXTENDABLE JAVA FRAMEWORK FOR INSTANCE SIMILARITIES IN ONTOLOGIES.

In Proceedings of the Eighth International Conference on Enterprise Information Systems - AIDSS, pages 263-269

DOI: 10.5220/0002464302630269

 SciTePress

tions (properties) RC. Relations can also be arranged

in a hierarchy HR. Instances I of a specific concept

are interconnected by property instances RI. Addi-

tionally, one can define axioms A which can be used

to infer new knowledge or to formulate integrity

constraints.

Common languages to represent ontologies are

RDF(S)

( http://www.w3.org/RDF/ ) or OWL

(http://www.w3.org/TR/owl-features/ ).

2.2 Similarity Measures

A similarity measure is a function for quantitatively

computing the similarity between things:

Definition 2 (Similarity Measure). A similarity

measure is a real-valued function sim(x, y): S

Æ [0,

1] on a set S measuring the degree of similarity

between x and y.

If we regard two complex objects in the real world

which can be formally described as sets of interrela-

ted ontology instances, the similarity of such two

complex entities Ea and Eb can be assessed follo-

wing the local-global principle which is based on

the assumption that complex objects are built up

from smaller units which can be characterized by a

number of attributes and their respective values. To

compute local – i.e. attribute-specific – similarities,

appropriate local similarity measures are used which

have to be defined accordingly. For the computation

of the global similarity between Ea and Eb, we

combine the results of local similarities, i.e.:

Definition 3 (Similarity Measure for Complex

Objects). A similarity measure for two complex

objects

E and

E consisting of smaller units

a,1

a,2

,...,e

a,n

) and (e

b,1

b,2

,...,e

b,n

) is a function

)),(),...,((:),(

,,1,1,1 nbnanbaba

eesimeesimAEESim =

where ),(

,, xbxax

eesim is a similarity function defi-

ned on smaller units of

E and

E . Please note that

those subunits may represent simple attributes as

well as more complex entities themselves.

is an

aggregation function (e.g., a weighted sum).

2.3 Semantic Similarity Measures

A semantic similarity measure takes into account not

just the parts of complex objects, but the content of

an ontology as well; it uses background knowledge

to better calculate the similarity of two elements. In

the context of this paper, we restrict ourselves to

only look at the similarity between two instances.

Definition 4 (Semantic Instance Similarity

Measure). A Semantic Instance Similarity Measure

is a function calculating the similarity of two

entities IEE

∈

, with respect to an Ontology

O=(C,HC,RC,HR,I,RI,A):

)),,(),...,,((:),,(

OEEsimOEEsimAOEESim

banbaba

where ),,( OEEsim

bax

is a similarity function

that compares

E and

E with regard to a certain

aspect.

Examples for aspects of

E are certain attributes,

relations of some type, or the position of

E in a ta-

xonomy. Let us briefly explain two exemplary kinds

of similarity measures that take into account the con-

tent of the ontology. The instance aspects that these

similarity measures work on are taxonomic relations

and general (non-taxonomic) relations, respectively.

2.3.1 Taxonomic Similarity

The taxonomic similarity of two instances is calcula-

ted by looking at the relative taxonomic position of

the concepts of the regarded instances. One way to

formalize the notion of “relative position” is by

looking at the “Semantic Cotopy” – the set of all

super concepts – of an instance:

Definition 5 (Taxonomic Similarity). Be SC(i)

(“Semantic Cotopy“) a function that returns the set

of all super concepts of an instance, then the taxono-

mic similarity between instances Iii ∈

, is defined

as:

)()(

:),(

iSCiSC

iisim

taxonomic

∪

∩

2.3.2 Set Similarity

The set similarity is not directly used to compare

two instances, but other instance similarity measures

(like the relational similarity described below) de-

pend on it. Set similarity compares sets of instances;

the method described here is just one example how

to calculate this. The set similarity we describe here

is based on ideas from multi-dimensional scaling.

Given a similarity measure that can compare two in-

stances, the set similarity first represents each in-

ICEIS 2006 - ARTIFICIAL INTELLIGENCE AND DECISION SUPPORT SYSTEMS

264

stance by a vector with the similarity to all instances

in one of the compared sets. Then each of the two

sets is represented by the average of these vectors,

and the similarity between the sets is given by the

cosine of the vectors representing the sets.

Definition 6 (Set Similarity).

⎟

⎠

⎞

⎜

⎝

⎛

∑

∈

sim

set

,cos:

where E is a complex instance ...},{

eeE = ,

),...),(),,(),...,,(),,((

2121

fesimfesimeesimeesime =

F and

are defined analogously.

2.3.3 Relational Similarity

Relational similarity looks at the outgoing and in-

coming relations, thus realizing the idea that in-

stances can be considered more similar if they have

relations to similar entities:

Definition 7 (Relational Similarity).

JJsim

iisim

kkset

relation

∑

...1

),(

:),(

where J

is the set of instances linked to i

via a

relationship, and J

analogous. Through the index k,

all relationships in RC are enumerated.

3 SIMILARITY FRAMEWORK

On top of the KAON ontology management system

(http://kaon.semanticweb.org/), we built an exten-

dable Java framework for calculating instance simi-

larities in RDF(S) ontologies. It comes with a set of

semantic similarity measures, including those descri-

bed above. For a concrete application use case, such

as calculating the match between job offers and skill

profiles described in an ontology (see Section 4, or

(Biesalski et al., 2005)), an expert can create a custo-

mized similarity measure by writing an XML file

that describes how semantic similarity measures are

used to compare the different aspects of the complex

instances. New semantic similarity measures can be

added without changing the framework.

3.1 Similarity Measures

As stated already, complex similarity measures can

be defined by an XML file that describes how to

combine the atomic measures. A simple example:

<similarity name='Example'

concept='fzi.de/kmir#Profile'>

<instanceRelationSimilarity weight='3'

relationType='fzi.de/kmir#hasProblem'>

<syntacticSimilarity

language='de'

attributeURI='labelValue'/>

</similarity>

</instanceRelationSimilarity>

</similarity>

The first <similarity> tag states that this measure

has the name “Example” and that it is used ex-

clusively to calculate the similarity between instan-

ces of the concept fzi.de#Profile. This outermost

tag creates a new complex similarity measure; its

content describes how it is calculated. The measure

“Example” only considers one aspect of instances

when comparing them: their relation

fzi.de/kmir#hasProblem.

The similarity of two profile instances only depends

on the similarity of the problems they are associated

with. The <instanceRelationSimilarity> tag gives in-

formation about this part of the similarity measure.

The attribute relationType states which relation we

are talking about; the weight attribute defines the

relative weight of this aspect compared to other

aspects of a complex similarity measure (here we

only have one and could have omitted this attribute).

We have now defined that the similarity between

“#Profile” instances only depends on the similarity

of the instances they have #hasProblem relations to;

what we haven’t said yet, is how these problem in-

stances are compared. As a last resort the system has

a “defaultSimilarity.xml” that defines a similarity

method that would be used if no other can be found,

but this is a very rudimentary. Another possibility is

to include a similarity measure for #Problem instan-

ces in the same XML file; this similarity measure

would be preferred to the default one. The third pos-

sibility has been used in the example: the description

of the similarity measure used for the related instan-

ces is nested inside the <instanceRelationSimilarity>

tag. In the example, the similarity measure used to

compare the problem instances again only considers

one aspect: the syntactic similarity of the (German)

labels – the similarity of two problem instances

depends on the Levenshtein distance of their labels.

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265

3.2 Filters

Two different kinds of filters allow to specify which

instances are considered in the calculation and to

precisely define what results are returned. They can

be individually combined from atomic filters.

Pre-filters allow a precise definition of the instances

that should be considered for similarity computation.

These filters are important because similarity calcu-

lations can be computationally very expensive.

<inclusiveConceptFilter

concept="fzi.de#Human"/>

<inclusiveConceptFilter

concept="fzi.de#chimp"/>

</preFilter

The example defines a filter that consists of two

atomic filters. Both state that instances of a certain

concept should be included – here instances of the

concepts #Human and #Chimp.

It is possible to use filters based on KAON Queries

(cf. “Developers Guide for KAON”) in the XML-

based definition of filters in order to further reduce

the number of considered instances. The KAON

conceptual query language allows easy and efficient

locating of elements in KAON OI-models. For

instance, a filter with the query

[#Profile] AND

SOME(<#hasOrganisationType>,!#SME!)

restricts the instances that are considered to those of

type #Profile and with an organisation type #SME

(small and medium enterprises)

Post filters determine how many instances are retur-

ned after similarity computation. There are two filter

types, minSimilarityFilter and maxCountofIn-

stancesFilter. The first defines the minimum si-

milarity to the query instance for an instance to be

included in the result. The second defines a cut-off

for the number of results returned.

<maxCountOfInstancesFilter

maximum="10"/>

</postFilter>

Here, a filter is defined that restricts the number of

results to 10 and returns only instances with a

similarity of at least 0.3 to the query instance.

3.3 Adding Similarity Measures

For use cases where the atomic similarities included

in the system are not sufficient, it is possible to add

new similarity measures.

A new similarity measure must implement the inter-

face Similarity or InstanceRelationSimilarity: The

second one is for similarity measures that will need

information about the similarity of instances related

to the currently compared ones (like the relational si-

milarity described earlier). Once the new similarity

measure has been implemented, the framework is

alerted about its existence through a change in the

tag library “similarity.tld”:

<tag>

<name>taxonomicSimilarity</name>

de.fzi.wim.similarity.TaxSim

</tagclass>

</tag>

This example adds a tag <taxonomicSimilarity>

and defines which class implements it. This tag can

then be used alongside the other similarity measures

in XML-files.

4 USE CASES

The strength of our framework is that is easily adap-

table to different domains and use cases. We shortly

sketch two successful use cases.

4.1 Case-Based Reasoning

The KMIR (Knowledge Management Implementa-

tion and Recommendation) Framework (Hefke,

2004) supports organizations in the implementation

of Knowledge Management (KM) by providing re-

commendations wrt. technological, organizational,

and human aspects. These recommendations are ba-

sed on best practice cases (BPCs) for successful KM

introduction. BPCs are structured and stored in an

ontology. A typical KM introduction scenario is de-

scribed as follows: An organization intends to intro-

duce KM. With a web-based self-description compo-

nent, it describes its profile in consideration of orga-

nizational structure (business size, sector, legal form,

processes, etc.) and infrastructure (used tools and

technologies), financial ratios (e.g., turnover, profit)

as well as economic aspects for KM introduction

(e.g., planned implementation time, amortization

time and target costs). Moreover, the organization

can define knowledge problems and requirements, as

well as normative, strategic and operative (know-

ledge) goals wrt. KM. Finally, the organization as-

signs weights to all described aspects in order to

attach more or less importance to them. After that,

the “profile” is stored as a set of instances/ relations

in the ontology representing in this scenario the case

base. In a next step, a matching component identifies

the most similar case(s) in the case base which con-

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266

sider the same “problem situation” concerning the

introduction of KM by matching the organization

profile against already existing BPCs.

In order to retrieve BPCs that are most similar to a

newly created profile achieved from the self-descrip-

tion process, a matching component matches the

profile against already existing BPCs from the case

base. This is done by combining syntactical similari-

ty measures (distance-based similarity, syntactical

similarity and equality) with semantical similarity

measures (relation similarity, taxonomic similarity

and set similarity). Finally, the most similar BPC(s)

from the case base is/are presented to the requesting

organization including solutions and methods for

solving the similar problem situation.

Distance-based Similarity is used to compare values

of numeric data types (e.g. turnover, profit, number

of KM workers, etc.) from the organization profile

with those of existing BPCs. Syntactical Similarity

and Equality are used for string comparisons in

order to compare problem or goal descriptions, the

name of specific tools or technologies. Relation si-

milarity is used for, e.g., comparing instantiations of

the concept “problem” that are linked to further in-

stantiations of the concept “Core process” using the

relation “(problem) addresses core process”. Set si-

milarity compares each instance or set of instances

in an organisation profile with those of the BPC(s).

Taxonomic Similarity identifies similar profile in-

stances based on their position in the taxonomy. For

instance, an organization is searching for an exten-

sion of its existing groupware system G

in order to

achieve better search results. The matching compo-

nent identifies a similar groupware system G

in the

case base (which has been extended with a semantic

search functionality) by regarding all instances of

the corresponding concept “groupware” resp. of

more general/specific ones and recommends the as-

signed solution to the requesting organization.

Finally, a weighted average determines the global si-

milarity of all computed local similarities between

the organisation profile, and each of the BPCs, and

presents a ranked list of the best matching results.

4.2 Case-Based Reasoning

A further application domain of the similarity frame-

work has been introduced at DaimlerChrysler AG,

Wörth. Efficient skill management is a key factor

wrt. human resources. Here, matching skill profiles

with position requirements is an essential, yet com-

plex task that is performed in order to staff positions

or project teams, to provide strategically optimized

training recommendations, or to perform succession

planning. Current approaches often lack comprehen-

sive means to compare skill profiles considering in-

terrelations of skills, synonyms, varying skill metrics

of different data sources and application domains.

Our approach (Biesalski et al., 2005) allows to over-

come these challenges with an integrated, ontology-

based skill catalogue for storing and managing

individual skills as well as profiles.

On the basis of the presented similarity framework,

we were able to provide decision makers with fle-

xible similarity measures based on different com-

pound similarity measures:

• Direct skill comparison: we require an exact

match of as-is and to-be. So we can specify

K.O. criteria for the central requirements, espe-

cially in strategically important jobs.

• Proportional similarity: we identify also par-

tially fulfilled requirements. This is also impor-

tant if we can plan for additional teaching and

qualification, or for “training on the project”.

• Compensatory similarity: we identify not only

partially fulfilled requirements, but also over-

qualifications; so, additional expertise on one

hand may compensate deficiencies on the other

hand. If several employees fulfil the K.O. crite-

ria, this can be used to find the most suited one.

• Taxonomic similarity: the taxonomic structure

of the skill ontology is taken into account to

find “close matches” in the case that no em-

ployee has exactly the required qualifications.

Also usable for deciding between several candi-

dates, and for refining profile specifications.

Yet since decision makers need to be able to put a

different emphasis on individual skills when staf-

fing, specification of different weights for certain

skill requirements or definition of subsets as manda-

tory elements had to be allowed. In order to support

skill matching with these additional constraints, we

had to introduce customized set similarity, instance

relation and compound similarity measures, which

dynamically compute weights that are specified in

the ontology rather than statically given in the simi-

larity configuration file. This was accomplished by

definition of dedicated weight properties of instan-

ces and provision of references to this weight setting

in the customized configuration format:

<instanceRelationSimilarity weight="#has-

weight" relationType="#has-skill"

depth="3">

</instanceRelationSimilarity>

Another challenge in skill management is to adjust

and optimize efficiency and effectiveness conside-

ring the average number of skills within a profile,

the size of the skill catalogue, or the granularity of

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267

weight metrics. Our approach simplifies the defini-

tion and ongoing adjustment of the similarity

measures within the life cycle of the skill manage-

ment solution. This was accomplished by provision

of adaptable configuration files which can be custo-

mized according to the demands of the ever-

changing environment.

Our approach allows us to successfully provide exe-

cutives with context-dependent similarity measures

of skill profiles and position requirements.

5 RELATED WORK

Related work can be coarsely separated into two ca-

tegories: (i) the ontology mapping / alignment

approaches, and (ii) the object similarity approaches.

Ontology alignment and mapping approaches are

concerned with finding corresponding concepts and

instances in different ontologies. They are mostly

motivated by information integration problems

where different ontologies for the same domain need

to be joined or aligned. Approaches comprise the

FOAM Framework for Ontology Alignment and

Mapping (Ehrig & Sure, 2005), AnchorPROMPT

(Noy & Musen, 2003), or GLUE (Doan et al., 2002).

These systems often exploit anchor-based

approaches to conclude semantic similarity from

similar connection structures in the graph constituted

by the relationships between ontology instances.

Object similarity approaches are more similar to the

work presented here and compute the similarity of

objects (instances or concepts) within the same onto-

logy.

Bernstein and colleagues created the SimPack

framework (Bernstein et al., 2004) that uses a set of

similarity measures to calculate the similarity of dif-

ferent concepts. The major difference to our simila-

rity framework is that they are trying to build a ge-

neric similarity measure that works for all domains,

although they do acknowledge the need for what

they call “personalised similarity measures”.

SemMF (Oldakowski & Bizer, 2005) is a simple

customizable framework for instance similarities in

ontologies; it finds the most similar objects for a

query instance in a set of resource objects. The pro-

perties of the query instance need not exactly corres-

pond to properties of the resource objects (a map-

ping between them can be specified in an RDF file),

but the concept taxonomy needs to be the same.

Compared to our approach, SemMF lacks support

for the recursive and set like characteristics of rela-

tional similarity – SemMF cannot compute the simi-

larity of objects based on their relations to other

objects; relations can only be treated like attributes.

Culmone and colleagues (Culmone et al., 2002) de-

scribe a simple mechanism to calculate the similarity

between concepts based on the number of relations

to identical concepts.

(Diaz-Agudo & Gonzalez-Calero, 2001) describe the

architecture of a CBR framework that encompasses

similarity of cases described in an ontology. jColibri

(Bello-Tomás et al., 2004) implements a CBR

framework for the entire CBR lifecycle (retrieve,

reuse, revise, remember). Similarity of cases with

respect to background knowledge is not a part of its

core, but this functionality can be added through

Problem-Solving Methods (PSMs).

6 CONCLUSION

We have presented a flexible and extensible similari-

ty framework for instance-similarity computation in

ontologies. In order to later guarantee the usefulness

of the framework in different application domains,

we started with a collection, analysis and selection

of commonly used traditional similarity measures, as

well as of ontology-related similarity measures.

Here, we rely among others on the theoretical results

of (Ehrig et al., 2005) which provide “a comprehen-

sive framework for measuring similarity within and

between ontologies as a basis for the interoperabili-

ty across various application fields”. Further, we

applied our framework in two different application

domains (Knowledge and Skill Management).

The framework is implemented in Java 1.4 on top of

KAON and uses Xerxes for parsing XML configura-

tion files ( http://xml.apache.org/xerces2-j/ ).

For the future, we plan to technically enhance the

framework, which currently supports RDF(s) and the

more expressive, proprietary KAON language. The-

refore, we will also support the web ontology

language OWL

(http://www.w3.org/TR/owl-ref/).

OWL, for instance, provides different data types,

further special property characteristics, and property

restrictions, which are not supported in KAON, but

promise added value for similarity computation. We

also plan to consider OWL DL’s complex classes

(e.g., by regarding the set operators “intersection-

Of”, “unionOf” and “complementOf”), which enab-

les new ways of filtering similarity sets by directly

using the ontology.

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ACKNOWLEDGMENTS

The work presented has been supported by the

European Commission under grant OntoGov (IST-

507237) and DIP (IST-507483), and by the German

National Ministry for Education and Research

bmb+f under grant Modale (FKZ 01ISC28H). Most

of the work presented was implemented in the

Diploma theses of Mrs. Qingli Wang and Mr. Marco

Breiter.

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Figure 1: Major Components of the Similarity Framework.

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