Agent-based Ontology Integration for Ontology-based Applications

Li Li Baolin Wu Yun Yang

Faculty of Information and Communication TechnologiesSwinburne University of Technology,

PO Box 218, Hawthorn, Melbourne, Australia 3122,Email {lli,bwu,yyang}@it.swin.edu.au

Abstract

In this paper, a novel agent-based ontology integra-tion framework is developed for agents which consumeontologies in ontology-based applications as well asengage in tasks of ontology integration. The cor-responding ontology integration mechanism is dis-cussed. Derived ontologies can be reused in the sys-tem. A prototype is built by using the JADE agentplatform for evaluation.

Keywords: Agent, ontology integration, consistencychecking.

1 Introduction and Motivation

An ontology is defined as an explicit specification ofa conceptualisation (Gruber 1993). It is a formal de-scription of a domain of discourse, intended for shar-ing among different applications, and expressed in alanguage that can be used for reasoning (Noy 2003).Ontologies facilitate the interoperability between het-erogeneous systems involved in commonly interesteddomain applications by providing a shared under-standing of domain problems and a formalisation thatmakes ontologies machine-processable. Furthermore,ontologies are seen as key enablers for the emergingSemantic Web. It is known that any information sys-tem uses its own ontology, either implicitly or explic-itly. Proliferation of Internet technology and glob-alisation of business environments has given rise tothe advent of a variety of ontologies which are farbeyond expectations. Thus it is unlikely that every-one conforms to a single ontology because of technicaland non-technical reasons. On the other hand, it isa trend that different systems combine to achieve agoal by taking advantage of existing resources or in-tegrating available systems to avoid error-prone andcostly reinvention. All these may take place at un-predictable times, for unpredictable reasons, betweenunpredictable organisations. Agents in multi-agentsystems (MAS) operate flexibly and rationally in en-vironments which are dynamic and heterogeneous,given that agents have abilities to perceive changesof environments and respond promptly. A MAS per-spective is thus suitable for tackling ontology inte-gration within and across the boundaries of organi-sations. The aim of this paper is to develop a novelagent-based framework to conduct ontology integra-tion based on a certain business scenario and priorwork (Li, Yang & Wu 2005b).

Copyright c©2005, Australian Computer Society, Inc. This pa-per appeared at the Australasian Ontology Workshop (AOW2005), Sydney, Australia. Conferences in Research and Prac-tice in Information Technology (CRPIT), Vol. 58. T. Meyer,M. Orgun, Eds. Reproduction for academic, not-for profit pur-poses permitted provided this text is included.

Two major architectures for ontology integrationhave been investigated (Noy 2003). The first one is ageneral upper ontology agreed upon by users of dif-ferent applications, while the second one is a methodcomprising heuristics-based or machine learning tech-niques that use various characteristics of ontologies.These two approaches focus on ontology manage-ment and have been taken by many researchers ininformation integration. However, ontology inte-gration, as part of the ontology development pro-cess (Pinto & Martins 2004), is far more complexthan expected. Considerable effort is needed in on-tology reuse (Uschold, Healy, Williamson, Clark &Woods 1998). We refer the reader to an excellent andthorough review (Kalfoglou& Schorlemmer 2003) fora detailed discussion in this field. In terms of contri-butions from database community, we refer the readerto a survey (Rahm & Bernstein 2001). Also otherresearchers (Calvanese, Giacomo & Lenzerini 2002,Klein & Noy 2003, Noy 2004, Wache, Vogele, Visser,Stuckenschmidt, Schuster & Neumann 2001) provideoverviews of ontology integration. Some of the spe-cific challenges in ontology integration that must beaddressed in the near future are listed in (Noy 2003).Although the problem of specifying the architectureis the core of ontology integration, it has not beenthoroughly investigated yet. It is a great challenge toperform ontology integration as flexibly as possibleas required on the Web. In terms of flexibility, agenttechnology fits well in developing applications in adynamic and distributed environment which requiressubstantial support for change. A MAS approach isthus ideally suited in handling ontology integration inan open environment such as Web.

To this end, an agent-based framework is devel-oped. By following the framework, agents’ behavioursin interaction diagrams are presented. A key featureof our framework is its flexibility and extendibility inontology integration by allowing ontology reuse in thesystem.

This paper is organised as follows. Section 2 in-troduces the terminology of ontologies and presentsan ontology integration scenario. Section 3 presentsan agent-based integration framework and mecha-nisms. Section 4 describes the prototype and eval-uation. Section 5 contains the related work and dis-cussion, and finally, Section 6 addresses conclusionsand future work.

2 Ontology Definition and Scenario

Agents act on different ontologies from differentsources. These agents in a certain business scenariomight contact each other to work together to solve theproblems that are beyond the individual capabilitiesor knowledge. It is not surprising that there are someterms used inconsistently when people talk aboutontology integration and even ontology itself. We

introduce ontology definition and terminology usedthroughout this paper next. Under the ontology spec-ification, example ontologies will be presented.

2.1 Ontology Definition and Terminology

It is most likely that different organisations opt tochoose terminology according to their understandingsand requirements. Hence, terms such as mapping andintegration may have been used in differing ways. Be-cause a consensus about the meanings of the terms inthis field is unlikely, we need to specify terms that weuse throughout this paper. The following definitionand terminology will specify terms used in this paper.

In this paper, we follow Gruber’s best known on-tology definition (Gruber 1993). Under this defini-tion, we define an ontology O with a specific domainmodel, T . Thus a conceptualisation Σ is a pair of< C,R >, where C represents a set of concepts, andR stands for a set of relations over these concepts. Aspecification is a pair of < Σ,Ψ > to describe that Σsatisfies the axioms Ψ derived from the domain model.In the following, notation C(O) is used to annotateconcepts C of the ontology O.

There are many representations and languagesavailable for encoding an ontology, however to es-tablish the notation of ontology used in a MASinternally for the task of ontology integration, anEntity-Relation (E-R) data model is considered toencode an ontology, where concepts are regarded asclasses. A typical concept class will have an identi-fier that distinguishes from others, and a set of at-tributes that describes the properties of the conceptclass. Then it is feasible to compare two conceptsby looking at the identifier as well as the attributes.Below are three kinds of mutually exclusive semanticrelations between existing concept classes from twodifferent ontologies. We assume that Oi and Oj arein the same domain (i, j ∈ N, where N: natural num-bers). ci where ci ∈ Ci(Oi) and cj where cj ∈ Cj(Oj)are two different concepts.Definition (Equivalent): Two concepts are seman-tically equivalent, if ∃ci, cj , s.t. ci ∼ cj . Namely,these two concepts: (1) have the same denotationnames (e.g. labels); (2) are synonyms; or (3) theirattributes are the same.Definition (Inclusive): Two concepts are semanti-cally inclusive, if ∃ci, cj , s.t. ci ≤ cj (e.g. ci is a kindof cj) or ci ≥ cj (e.g. cj is a kind of ci). Namely,the attributes of one concept are also the attributesof the other.Definition (Disjoint): Two concepts are disjoint, if∃ci, cj , s.t. ci ∩ cj = Φ. Namely, there is no commonattribute between them.

For the purpose of ontology integration, we needto consider the consistency issue of an integrated on-tology. It is obvious that the E-R date model of anontology and the defined semantic relations betweenconcepts allow us to check the consistency of a newlyderived ontology. The ontology consistency is definedas follows.Definition (Consistent): An ontology is consis-tent, if ∀ck

i , cni , cm

i (cki , cn

i , cmi ∈ Ci(Oi), and ck

i 6=cni 6= cm

i (k, n,m ∈N), cki ≤ cn

i and cni ∩ cm

i = Φ, s.t.cki 6≤ cm

i . Namely no sub-concepts of a particular con-cept is also a sub-concept of another concept wherethese two concepts are disjoint.Definition (Ontology Mapping): An mapping ℜbetween two ontologies Oi and Oj exists, if ∃ci, cj ,s.t. ℜ(ci, cj) ∈ {∼, ≤, ≥}. In terms of integration,we will use the following definition throughout thispaper.

Definition (Ontology Integration): Reusing avail-able source ontologies within a range to build a newontology which serves at a higher level in the appli-cation than that of various ontologies in ontology li-braries. It is associated with semantic integration.Different levels of integration can be distinguished.

2.2 Scenario

The running example ontologies come from thedomain of beer and concern the types of beer.One is from the DAML ontology library (http://www.daml.org/ontologies/66). The second oneis built on the definition of the term “beer” fromthe WordNet (http://wordnet.princeton.edu/). Thethird one is based on basic types of beer provided bythe website http://www.dma.be/p/bier/1 2 uk.htm#.The fourth one is Australian beer types ontologybased on information from the following websites:(1)http://www.australianbeers.com/beers/beer types/beer types.htm; and (2) http://www.fosters.com.au/beer/about/beertypes/beer types.asp.

Our main interest is on term “beer” and the corre-sponding hyponym relationship. The scenario such asdifferent wine retailers or even brewers using differentbeer ontologies is not unusual. In a business, achiev-ing some goals frequently requires more than individ-ual capabilities and knowledge. A general view of avariety of existing ontologies at an abstract level isnecessary for achieving such goals.

The above four ontologies are about types of“beer”, but from different points of view. We willcome back to them in Section 4.

3 Agent-based Ontology Integration

Agent technology presents an exciting prospect ina field where high dynamics are requested. It hasthe potential to significantly extend the range of ap-plications that can be feasibly tackled (Jennings &Wooldridge 2001). We have discussed the rationale ofan agent-based perspective in (Li, Yang & Wu 2005b)and behaviours of mapping agent (MA) in (Li, Yang& Wu 2005a). Next, we first present an agent-basedintegration architecture. We incorporate ontologyreuse in the integration. Then we describe the be-haviours of integration agent (InA) and its inter-actions within the integration process. After that, wediscuss the integration process. And finally, we detailthe integration mechanism.

3.1 Incorporate Ontology Reuse in Integra-tion

Some ontologies in the same area exist with differentaspects and overlapping information. Ontologies in-dependently created by individual organisations mayneed to be integrated later on. Moreover, integrationserves ontology reuse in applications. So we shouldtake it into consideration in ontology integration de-sign. Ontology reuse in this paper has two meanings.On one hand, existing ontologies can be used to gener-ate new ontologies (e.g. extending or combining); onthe other hand, newly generated ontologies are readyfor reuse in the system whenever needed. Ontologyintegration embedded with ontology reuse is shown inFigure 1.

Figure 1 points out this paper’s focus. Under theproposed architecture, some of the important aspectsare highlighted: (1) integration can be done gradu-ally; and (2) derived ontology can be reused instantlyby taking advantage of the presence of ontologyagent (OA). In other words, whenever a new ontol-ogy is derived (based on existing ontologies), an OA

RDF

OWL

Frame-

based

InA (integration module)

MA (mapping module)

mapping.txt . . .

integrated

IA UA

OA

OA

OA

OA

NA

NA

NA

NA

. . . . . .

legend: UA- user agent IA- interface agent InA- integration agent MA- mapping agent OA- ontology agent NA- negotiation agent

Figure 1: Ontology integration architecture

will be created on the fly to be responsible for it. On-tology integration (module) is based on the mappingresults discussed in (Li, Yang & Wu 2005a). Inte-grated ontology is depicted at the most right top inFigure 1. It can be reused in the system (associatedwith the integration module in dotted line). Refer to(Li, Yang & Wu 2005b) for details of definitions ofother agents in Figure 1.

3.2 Agent Interaction Diagram

At an abstract level, agents achieve a goal by work-ing with other agents to solve problems that are be-yond individual capabilities and knowledge. Agentswork together based on interactions. An interactionprocess (e.g. diagram) is needed as interactions be-tween agents may take place concurrently. After in-vestigating the existing tools, we have chosen AUML(http://www.auml.org/) to describe the artifacts ofagent interactions.

Figure 2 displays interactions in the integrationmodule. InA consults OAs involved in integrationprocess and counts the number of occurrences. Thenit records concepts that meet the requirements. Cer-tainly, a visualisation module of the user agent(UA) can present a graphic view of the result at theend.

3.3 Integration Process

In this paper, the integration module starts from theroot of the specified ontology and then traverses allsub-concepts of the ontologies. Briefly speaking, InAcounts the appearance of each concept of existingontologies, and then filters unexpected concepts witha given threshold. By saying this, we do not meanthat we attempt to change the conceptual modellingof the ontology. Instead, ontology integration isbased on a specified ontology. The process is asfollows:

(1) obtain ontology related information (e.g. map-ping results) via OAs ;

(2) keep the numbers of occurrences of each conceptin the specified ontology;Three cases may take place according to the map-ping results. They are:

- Case 1 semantic equivalence for the currenttwo concepts (for example, “beer” is thesame as “suds”):

In this case, increase the number of occur-rences of the concept by 1 for each equiva-lence;

- Case 2 inclusive relation for the current twoconcepts (for example, “stout” is a kind of“ale”):In this case, insert sub-concepts of the coun-terpart into the specified ontology structurebut keep the original relations;

- Case 3 no semantic equivalence for the cur-rent two concepts but their correspondingdirect ancestors are semantically equivalent:In this case, insert the counterpart into thespecified ontology but without conflict withexisting sub-concepts of the same ancestor;

(3) filter unexpected concepts by a given threshold.

Following the above approach, we derive a newlyintegrated ontology based on the mapping results.Moreover, as the OA is used in the proposed frame-work to be in charge of ontology related tasks, theintegrated ontology can be reused.

3.4 Integration Mechanism

Integration module operates over available mappingresults. The module uses the following functions ordata structures to execute relevant operations. Thepseudocode for the integration algorithm is shown inTable 1.

• initialise: initialise the integration process;

• next-c: request a particular OA the next con-cept of a specified ontology and returns the con-cept if it exists;

• search: search for relations in mapping resultsand returns existing relations if it exists or Nullotherwise;

• insert-sup: insert a specified concept as asuper-node of a given concept in a particular on-tology structure;

• insert-sub: insert a specified concept as a sub-node of a given concept in a particular ontologystructure;

• get-threshold: contact the UA via IA to obtaina threshold. It returns the threshold;

• filter: filter unexpected items by given thresh-old from a given ontology and returns a filteredontology.

4 Prototyping and Evaluation

We have developed an agent-based ontology in-tegration prototype using the JADE platform(http://jade.tilab.com/). The application back-ground of the prototype is ontology integration in acertain scenario where agents involved work togetherto achieve a goal. In that case, an abstract confor-mance view is needed. After that, the evaluation ofthe framework and the work we have done is pre-sented.

4.1 Prototyping

Following the analysis of the proposed architecture,we worked out the details of the prototype. Itconsists of the following agents: one user agent(UA), one interface agent (IA), one mappingagent (MA), one integration agent (InA), one

InA

request start_node

traverse the ontology structure to get the current nodes

inform current_node

search mapping.txt, if they are the same,

then increase the counter by 1

UA

request subs

inform subs

request is_sub

inform [Yes|No] search mapping.txt, if they are the same,

then increase the counter by 1

request threshold

inform threshold

request insert new ontology

create a new ontology by inserting concepts

visualisation

inform insert new ontology

OA new OA OA i OA j

Figure 2: Interactions between agents in integration module

consistency checking agent (CA), four ontologyagents (OAs), four negotiation agents (NAs).

The prototype runs as follows:(1)Import existing ontologies;(2)Develop corresponding OAs for each available

ontology;(3)Execute mapping module;(4)Execute integration module;The process may take the following steps if re-

quired.(5)Visualise the integrated ontology;(6)Export the integrated ontology in a specified

format (e.g. RDF);(7)Check the consistency of the integrated ontol-

ogy.

Figure 3: Screen shot of ontology integration

Suppose four similar organisations are seeking toachieve a goal that is beyond their individual capabil-ities and knowledge. They have their own ontologiesas presented in Section 2.2. In order to have a generalview of the variety of ontologies, a higher abstract on-tology is necessary. In the example, we assume thatexisting four ontologies are integrated based on theWordNet “beer” definition.

Figure 3 is a screen shot of the ontology integra-tion results (see Section 2.2 for existing running ex-amples). The upper part is the overall prototype, thelower right part is the integrated ontology in a hier-archical structure.

Consistency checking is conducted by applying acertain description logic (DL) based reasoning tool.DL-based inference engines, which use a tableaubased algorithms (Baader & Sattler 2001), are decid-able and support complete consistency checking. Inthis paper, ontology consistency checking is done withRACER (http://www.racer-systems.com/) in Protege(http://protege.stanford.edu).

4.2 Evaluation

Ontologies and ontology-based applications performin the environment of dynamics, distribution and het-erogeneity. The agent-based framework proposed inthis paper is suitable for tasks such as ontology in-tegration in a certain business scenario. By adopt-ing a MAS’s perspective, interactions among multipleagents, which work together to achieve the goal be-yond individual capabilities and knowledge, are high-lighted. In other words, MASs are able to take avariety of environmental circumstances into consider-ation rather than treating the environment mainly asbeing static. The evaluation work takes the followingcharacteristics into account:

• Flexibility: In the framework, the already setup agent communication channel facilitates mes-

Table 1: Integration algorithm/* assume the mapping module starting from a given start point;Os, Ot: two different ontologies;Od: the derived ontology;cs, ct: concepts from ontologies Os and Ot, respectively;ncs

, nct: the numbers of occurrences of concepts cs and ct, respectively;

m: the number of available ontologies;relation: relations between two given concepts from different ontologies;threshold: the threshold given by the user to filter unexpected items from the generatedontology.*/Function integration {initialise;for(i = 1; i < m; i + +) {

while ((next-c(Os)!=Null) && (next-c(Ot)!=Null)) {cs=next-c(Os);ct=next-c(Ot);relation=search(cs, ct);switch (relation) {

case “=”: ncs+ +; break;

case “≤”: insert-sup(ct, cs); nct= 1; break;

case “≥”: insert-sub(ct, cs); nct= 1; break;

default: ncs= 1;

} //end switch} //end while

} //end forthreshold=get-threshold;filter(Od, threshold);} //end function

sage delivery. Moreover, the presence of the OAsallows flexible system organisation. The systemallows freely adding/deleting OAs and all definedagents for a particular tasks to/from the system;

• Interactivity: Agents are highly interactive inthe framework. Interactions take place not onlybetween OAs, but also between other agents if aparticular task needs to deploy the functionalitiesof others;

• Interoperability: The framework enablesinteroperability between agents of differentagent platforms. In terms of syntacticand semantic heterogeneity of ontologies, ameta-ontology (Li, Yang & Wu 2005b) is de-veloped to resolve semantic heterogeneities;

• Scalability: In the framework, different classesare developed. They include concept class, ontol-ogy class and agent class. Moreover, all ontologyrelated operations are encapsulated and isolatedfrom other agents’s view (e.g. only being visibleto OAs). By extending corresponding classes, theagents can be created easily.

• Reusability: In the framework, whenever a newontology is generated based on existing ontolo-gies, an OA is developed correspondingly. It en-ables the general view of a particular applicationdomain to be reused in the system.

• Reliability: It depends on agents performingrationally in the framework. As every agentof the system is required to register and adver-tise its capabilities to the IA, any other agentsare able to reach all available capabilities in thesystem whenever needed. Moreover, the upperbound on the number of iterations (the integra-tion algorithms) required to reach a fixed pointis the number of concepts in an ontology. It isknown that the number of concepts in an ontol-ogy is finite.

Agents generated for the running examples workproperly at this stage. They all exhibit their be-haviours correctly as specified.

To sum up, the proposed framework provides aflexible and effective modelling approach to tackle on-tology integration over a variety of ontologies.

5 Related Work and Discussion

Independently developed ontologies may need to beintegrated or composited later on if required. It isnatural to treat it from a process perspective. Pintoet al. (Pinto & Martins 2001) define activities of theprocess. A methodology is presented to support andguide the process. Even though each stage of theintegration process still needs to be addressed in thefuture, we can find some existing tools which areclaimed to support it in some way. Some of these sys-tems are: PROMPT (Noy & Musen 2003), Chimaera(http://www.ksl.stanford.edu/software/chimaera),SHOE (http://www.cs.umd.edu/projects/plus/SHOE),and OntoEdit (http://www.ontoknowledge.org/tools/ontoedit.shtml). The PROMPT suite consists ofa set of tools to assist merging, alignment andversioning of ontologies. These tools support someof the tasks in the context of multiple ontologymanagement. Chimaera (McGuinness, Fikes, Rice& Wilder 2000) is an environment for merging andtesting large ontologies. SHOE (Heflin 2001) allowsWeb page authors to annotate their web documentswith machine-readable knowledge. OntoEdit (Sure,Erdmann, Angele, Staab, Studer & Wenke 2002) isan ontology editor that integrates numerous aspectsof ontology engineering. Moreover, there are someother tools listed in (Duineveld, Stoter, Weiden,Kenepa & Benjamins 2000) that support ontologyintegration to some extent.

Besides those tools, some researchers, such asGruninger et al. (Gruninger & Kopena 2003),Kalfoglou et al. (Kalfoglou& Schorlemmer 2003),

Gomez-Perez et al. (Gomez-Perez & and RichardBenjamins 1999), have investigated ontology integra-tion from different technical aspects. Other relatedworks, such as GLUE (Doan, Madhavan, Domingos& Halevy 2003) and Hovy (Hovy 1998) are more con-cerned with mapping techniques than the overall ar-chitecture.

Another closely related work is ProcessSpecification Language (PSL) (http://www.mel.nist.gov/psl/ontology.html). Its aim is to create aprocess interchange language that is common to allmanufacturing applications, generic enough to bedecoupled from any given applications and robustenough to be able to represent the necessary processinformation for any given applications. It is moreappropriate to refer to it as an ontology or a datamodel than a language.

Our work is inspired by the approaches in infor-mation integration and PSL as well. We argue thatdifferent techniques and methodologies are comple-mentary and thus must be used in combination ratherthan exclusively. Bearing these in mind, we adopt aMAS perspective to model the variety of ontologiesin ontology-based applications. We believe that theagent technology is ideally suited in a dynamic anddistributed environment such as ontology integrationon the Web.

An agent-based approach aims to provide a flexibleand robust way to automate the process of ontologyintegration as much as possible to alleviate the heavyburden of building ontologies from scratch, which isbelieved to be error-prone and costly. Moreover, littleprior knowledge is needed to start the system becauseagents, which are autonomous and engaged in flexibleinteractions, can perceive changes in the environmentand adopt corresponding actions to achieve the goalsin a timely fashion. Clearly, communications betweenagents play very important roles in agent interactions,which are based on some kinds of ontologies.

6 Conclusions and Future Work

Ontologies are becoming more and more important inthe context of the emerging Semantic Web. Becauseof this trend, the need for integration and reuse ofontologies increases as well. In this paper, we havepresented an novel agent-based framework to achieveontology integration in the environment of similar on-tologies existing in a distributed and heterogeneousway. With the presence of ontology agents, newlygenerated ontologies can be reused, which is in accor-dance with the intuition of reuse of existing ontologiesno matter whether they are original or newly built.

Although the approach proposed in this paper ispromising for ontology integration, some issues needto be further addressed. We attempt to use a processalgebra to support agent interactions at a high levelof abstraction. We hope that this perspective willallow the proposed framework to deal with ontologyintegration in an abstract but more flexible way.

Acknowledgements

Work reported here is partly supported by SwinburneVC’s Strategic Research Initiative Grant 2002-2004for project “Internet-based e-business ventures”. Theauthors are grateful for Shane Grund’s prototypingwork.

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