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

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  • http://jis.sagepub.com/Journal of Information Science

    http://jis.sagepub.com/content/28/5/375The online version of this article can be found at:

    DOI: 10.1177/016555150202800503

    2002 28: 375Journal of Information ScienceYing Ding and Schubert Foo

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

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  • Ying Ding

    Vrije Universiteit, Amsterdam, The Netherlands

    Schubert Foo

    Nanyang Technological University, Singapore

    Received 29 November 2001Revised 2 April 2002

    Abstract.

    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.

    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.

    1. Introduction

    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].

    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.

    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.

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    Journal of Information Science, 28 (5) 2002, pp. 375388 375

    The effect of postings information on searching behaviour

    Ontology research anddevelopment. Part 2 a review ofontology mapping and evolving

    Correspondence to: Ying Ding, Division of Mathematics andComputer Science, Vrije Universiteit, Amsterdam, TheNetherlands. E-mail: ying@cs.vu.nl

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  • 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.

    2. Ontology mapping

    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.

    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.

    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.

    Noy and Musen [5] clarified the difference betweenontology alignment and ontology merging and noted

    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].

    Ontology mapping has been addressed by researchersusing different approaches: One-to-one approach, where for each ontology a set

    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]).

    Single-shared ontology. The drawbacks of dealingwith a single shared ontology are similar to those ofany standards [11].

    Ontology clustering, where resources are clusteredtogether on the basis of similarities. Additionally,ontology clusters can be organized in a hierarchicalfashion [12].

    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.

    3. Ontology mapping projects

    Many existing ontology mapping projects have beencarried out and reported in the literature. The following

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    Ontology research and development. Part 2

    376 Journal of Information Science, 28 (5) 2002, pp. 375388

    Table 1Examples of ontology integration techniques and applications

    Tools Ontologies built/applications Methodology

    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

    Standard-Units ontology, etc. foundational theories

    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

    3. Integration of ontologies into applicationsKACTUS CYC, GUM, PIF, UMLS, EngMath, PhySys, Manual method, brainstorming, ONIONS

    Enterprise Ontology, Reference ontology

    Note: the various tools, ontologies and methodologies presented in the table will be discussed in subsequent sections.

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  • 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.

    3.1. InfoSleuths reference ontology

    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-

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