Ontology research and development. Part 2 - a review of ontology mapping and evolving

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<ul><li><p> http://jis.sagepub.com/Journal of Information Science</p><p> http://jis.sagepub.com/content/28/5/375The online version of this article can be found at:</p><p> DOI: 10.1177/016555150202800503</p><p> 2002 28: 375Journal of Information ScienceYing Ding and Schubert Foo</p><p>Ontology research and development. Part 2 - a review of ontology mapping and evolving </p><p>Published by:</p><p> http://www.sagepublications.com</p><p>On behalf of: </p><p> Chartered Institute of Library and Information Professionals</p><p> can be found at:Journal of Information ScienceAdditional services and information for </p><p> http://jis.sagepub.com/cgi/alertsEmail Alerts: </p><p> http://jis.sagepub.com/subscriptionsSubscriptions: </p><p> http://www.sagepub.com/journalsReprints.navReprints: </p><p> http://www.sagepub.com/journalsPermissions.navPermissions: </p><p> http://jis.sagepub.com/content/28/5/375.refs.htmlCitations: </p><p> What is This? </p><p>- Oct 1, 2002Version of Record &gt;&gt; </p><p> at GEORGIAN COURT UNIV on November 6, 2014jis.sagepub.comDownloaded from at GEORGIAN COURT UNIV on November 6, 2014jis.sagepub.comDownloaded from </p><p>http://jis.sagepub.com/http://jis.sagepub.com/content/28/5/375http://www.sagepublications.comhttp://www.cilip.org.uk/http://jis.sagepub.com/cgi/alertshttp://jis.sagepub.com/subscriptionshttp://www.sagepub.com/journalsReprints.navhttp://www.sagepub.com/journalsPermissions.navhttp://jis.sagepub.com/content/28/5/375.refs.htmlhttp://jis.sagepub.com/content/28/5/375.full.pdfhttp://online.sagepub.com/site/sphelp/vorhelp.xhtmlhttp://jis.sagepub.com/http://jis.sagepub.com/</p></li><li><p>Ying Ding</p><p>Vrije Universiteit, Amsterdam, The Netherlands</p><p>Schubert Foo</p><p>Nanyang Technological University, Singapore</p><p>Received 29 November 2001Revised 2 April 2002</p><p>Abstract.</p><p>This is the second of a two-part paper to review ontologyresearch and development, in particular, ontology mappingand evolving. Ontology is defined as a formal explicit speci-fication of a shared conceptualization. Ontology itself is nota static model so that it must have the potential to capturechanges of meanings and relations. As such, mapping andevolving ontologies is part of an essential task of ontologylearning and development. Ontology mapping is concernedwith reusing existing ontologies, expanding and combiningthem by some means and enabling a larger pool of informa-tion and knowledge in different domains to be integrated tosupport new communication and use. Ontology evolving,likewise, is concerned with maintaining existing ontologiesand extending them as appropriate when new informationor knowledge is acquired. It is apparent from the reviewsthat current research into semi-automatic or automaticontology research in all the three aspects of generation,mapping and evolving have so far achieved limited success.</p><p>Expert human input is essential in almost all cases.Achievements have been made largely in the form of toolsand aids to assist the human expert. Many research chal-lenges remain in this field and many of such challenges needto be overcome if the next generation of the Semantic Web isto be realized.</p><p>1. Introduction</p><p>Ontology is defined as a formal explicit specification ofa shared conceptualization [1]. It provides a shared andcommon understanding of a domain that can becommunicated across people and application systems.Ontology itself is not a static model. It must have thepotential to capture the changes of meaning [2].</p><p>Ontologies are developed to provide the commonsemantics for agent communication. When two agentsneed to communicate or exchange information, the pre-requisite is that a common consensus has to formbetween them. This leads to the need to map twoontologies. For example, in business to business (B2B)e-commerce applications, the mapping among differentclassification standards (such as UNSPSC (http://eccma.org/unspsc/) and ecl@ss (www.eclass.de/)) turnsout to be not trivial.</p><p>This paper reports the second part of the survey onontology research and development. The first partintroduced the subject of ontology and focused on thestate-of-the-art techniques and work done on semi-automatic and automatic ontology generation, as wellas the problems facing this research [3]. This secondpart focuses on the current status of research intoontology mapping and ontology evolving.</p><p>123456789</p><p>1110123456789</p><p>2012</p><p>113456789</p><p>30123456789</p><p>40123456789</p><p>5012</p><p>Journal of Information Science, 28 (5) 2002, pp. 375388 375</p><p>The effect of postings information on searching behaviour</p><p>Ontology research anddevelopment. Part 2 a review ofontology mapping and evolving</p><p>Correspondence to: Ying Ding, Division of Mathematics andComputer Science, Vrije Universiteit, Amsterdam, TheNetherlands. E-mail: ying@cs.vu.nl</p><p> at GEORGIAN COURT UNIV on November 6, 2014jis.sagepub.comDownloaded from </p><p>http://jis.sagepub.com/</p></li><li><p>An introduction to ontology mapping is firstpresented, followed by a review of a number ofdifferent ontology mapping projects to illustrate theapproaches, techniques, resulting mapping and prob-lems associated with the ontologies produced. This isfollowed by a review of related work on ontologiesevolving using a similar framework of presentation.</p><p>2. Ontology mapping</p><p>Effective use or reuse of knowledge is essential. This isespecially so now because of the overwhelming amountof information that is being continually generated,which in turn has forced organizations, businesses andpeople to manage their knowledge more effectively andefficiently. Simply combining knowledge from distinctdomains creates several problems, for instance differentknowledge representation formats, semantic inconsis-tencies, and so on. The same applies to the area ofontology engineering.</p><p>With ontologies generation, ontology engineerssubsequently face the problem of how to reuse theseexisting ontologies, and how to map various differentontologies in order to enable a common interface andunderstanding to emerge for the support of communi-cation between existing and new domains. As such,ontology mapping has turned out to be another impor-tant research area for ontology learning.</p><p>Sofia Pinto et al. [4] provided a framework and clar-ified the meaning of the term ontology integration toinclude that of ontology reuse, ontology merging andontology alignment along with tools, methodologiesand applications, as shown in Table 1.</p><p>Noy and Musen [5] clarified the difference betweenontology alignment and ontology merging and noted</p><p>that in ontology merging, a single ontology is createdwhich is a merged version of the original ontologies,while in ontology alignment, the two original ontolo-gies exist, with links established between them. Thereare several ways to carry out ontology mapping, as canbe seen in the ways in which the resulting mapping arerepresented. Mappings can be represented as condi-tional rules [6], functions [7], logic [8], or a set of tablesand procedures [9].</p><p>Ontology mapping has been addressed by researchersusing different approaches: One-to-one approach, where for each ontology a set</p><p>of translating functions is provided to allowcommunication with the other ontologies withoutan intermediate ontology. The problem with thisapproach is one of computing complexity (e.g.OBSERVER [10]).</p><p> Single-shared ontology. The drawbacks of dealingwith a single shared ontology are similar to those ofany standards [11].</p><p> Ontology clustering, where resources are clusteredtogether on the basis of similarities. Additionally,ontology clusters can be organized in a hierarchicalfashion [12].</p><p>Figure 1 shows a very simple example of ontologymapping in which the process of mapping an Employeeontology and a Personnel ontology from differentdepartments of the same company is illustrated. Adifferent UnitOfMeasure exists in these two ontologiesso that the mapping rule of UnitConversion is neededto secure the right mapping.</p><p>3. Ontology mapping projects</p><p>Many existing ontology mapping projects have beencarried out and reported in the literature. The following</p><p>11123456789101234567892012345678930123456789401234567895012</p><p>Ontology research and development. Part 2</p><p>376 Journal of Information Science, 28 (5) 2002, pp. 375388</p><p>Table 1Examples of ontology integration techniques and applications</p><p>Tools Ontologies built/applications Methodology</p><p>1. Integration of ontologies by building a new ontology and reusing other available ontologies (ontology reuse)Ontolingua server PhySys, Mereology ontology, KACTUS, Integration of the building blocks and </p><p>Standard-Units ontology, etc. foundational theories</p><p>2. Integration of ontologies by merging different ontologies into a single one that unifies all of them (includes OntologyMerging and Ontology Alignment)ONIONS SENSUS, Agreed-Upon-Ontology Manually, brainstorming, ONIONS</p><p>3. Integration of ontologies into applicationsKACTUS CYC, GUM, PIF, UMLS, EngMath, PhySys, Manual method, brainstorming, ONIONS</p><p>Enterprise Ontology, Reference ontology</p><p>Note: the various tools, ontologies and methodologies presented in the table will be discussed in subsequent sections.</p><p> at GEORGIAN COURT UNIV on November 6, 2014jis.sagepub.comDownloaded from </p><p>http://jis.sagepub.com/</p></li><li><p>sections provide an update of the state of developmentof ontology mapping in these projects. Informationabout each project along with the important featuresassociated with it are provided and highlighted.</p><p>3.1. InfoSleuths reference ontology</p><p>InfoSleuth [13] can support construction of complexontologies from smaller component ontologies so thattools tailored for one component ontology can be usedin many application domains. Examples of reusedontologies include units of measure, chemistry know-ledge, geographic metadata, etc. Mapping is explicitlyspecified among these ontologies as relationshipsbetween terms in one ontology and related terms inother ontologies.</p><p>All mappings between ontologies are made by a spe-cial class of agents known as resource agents. Aresource agent encapsulates a set of information aboutthe ontology mapping rules, and presents that informa-tion to the agent-based system in terms of one or moreontologies (called reference ontologies). All mapping isencapsulated within the resource agent. All ontologiesare represented in OKBC (Open Knowledge Base Con-nectivity) and stored in an OKBC server by a specialclass of agents called ontology agents, which can pro-vide ontology specifications to users (for request form-ulation) and to resource agents (for mapping).</p><p>3.2. Stanfords ontology algebra</p><p>In this application, the mapping between ontologieshas been executed by ontology algebra [14], consistingof three operations, namely, intersection, union anddifference. The objective of ontology algebra is toprovide the capability for interrogating many largelysemantically disjoint knowledge resources. Here, artic-ulations (the rules that provide links across domains)can be established to enable the knowledge interoper-</p><p>ability. Contexts, the abstract mathematical entitieswith some properties, were defined to be the unit ofencapsulation for well-structured ontologies [15],which could provide guarantees about the knowledgethey export, and contain feasible inferences aboutthem. The ontology resulting from the mappingsbetween two source ontologies is assumed to be consis-tent only within its own context, known as an articula-tion context [16]. Similar work can be found inMcCarthys research and the CYC (Cycorps Cyc know-ledge base; www.cyc.com/) project. For instance,McCarthy [15] defined context as simple mathematicalentities used for the situations in which particularassertions are valid. He proposed using the liftingaxioms to state that a proposition or assertion in thecontext of one knowledge base is valid in another. TheCYC [8] use of microtheories bears some resemblanceto this definition of context. Every microtheory withinCYC is a context that makes some simplifying assump-tions about the world. Microtheories in CYC are orga-nized in an inheritance hierarchy whereby everythingasserted in the super-microtheory is also true in thelower level microtheory.</p><p>Mitra et al. [17] used ontology algebra to enable inter-operation between ontologies via articulation ontology.The input to the algebra is provided by ontology graphs.The operators in the algebra include unitary operatorslike filter and extract, and binary operators that includeunion, intersection and difference (as in normal setoperators): The union operator generates a unified ontology</p><p>graph comprising two original ontology graphsconnected by the articulation. The union presentsa coherent, connected and semantically soundunified ontology.</p><p> The intersection operator produces the articulationontology graph, which consists of the nodes and theedges added to the articulation generator using the</p><p>11123456789</p><p>10123456789</p><p>1120123456789</p><p>1130123456789</p><p>1140123456789</p><p>501</p><p>112</p><p>YING DING AND S. FOO</p><p>Journal of Information Science, 28 (5) 2002, pp. 375388 377</p><p>Employee</p><p>name: textweight: number kgnationality: country</p><p>Unit Conversion</p><p>Factor = 1000SourceProperty = weightTargetProperty = gram</p><p>Personnel</p><p>name: textweight: number gramnationality: country</p><p>Source Target</p><p>Fig. 1. Simple example of ontology mapping.</p><p> at GEORGIAN COURT UNIV on November 6, 2014jis.sagepub.comDownloaded from </p><p>http://jis.sagepub.com/</p></li><li><p>articulation rules between two ontologies. Theintersection determines the portions of knowledgebases that deal with similar concepts.</p><p> The difference operator, distinguishing betweentwo ontologies (O1O2) is defined as the terms andrelationships of the first ontology that have not beendetermined to exist in the second. This operationallows a local ontology maintainer to determine theextent of one ontology that remains independent ofthe articulation with other domain ontologies, sothat it can be independently manipulated withouthaving to update any articulation.</p><p>The researchers also built up a system known asONION (Ontology compositION), which is an architec-ture based on a sound formalism to support a scalableframework for ontology integration. The special featureof this system is that it separates the logical inferenceengine from the representation model of the ontologiesas much as possible. This allows the accommodation ofdifferent inference engines. This system contains thefollowing components: data layer which manages the ontology representa-</p><p>tions, the articulations and the rule sets involvedand the rules required for query processing;</p><p> viewer (which is basically a graphical user inter-face);</p><p> query system; articulation agent that is responsible for creating</p><p>the articulation ontology and the semantic bridgesbetween it and the source ontologies. (The genera-tion of the articulation in this system is semi-automatic.)</p><p>The ontology in ONION is represented by the concep-tual graph, and the ontology mapping is based on thegraph mapping. At the same time, domain experts candefine a variety of fuzzy matching. The main innova-tion of ONION is that it uses articulations of ontologiesto interoperate among ontologies and it also representsontologies graphically which could help in separatingthe data layer from the inference engine.</p><p>Mitra et...</p></li></ul>