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    Discovering Concept Mappings by Similarity Propagation among Substructures

    Access Status
    Fulltext not available
    Authors
    Pan, Qi
    Hadzic, Fedja
    Dillon, Tharam S.
    Date
    2010
    Type
    Book Chapter
    
    Metadata
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    Citation
    Pan, Qi H. and Hadzic, Fedja and Dillon, Tharam S. 2010. Discovering Concept Mappings by Similarity Propagation among Substructures, in Fyfe, C. and Tino, P. and Charles, D. and Garcia-Osorio, C. and Yin, H. (ed), Lecture Notes in Computer Science, Volume 6283: Intelligent Data Engineering and Automated Learning – IDEAL 2010. pp. 324-333. Germany: Springer.
    Source Title
    Lecture notes in computer science, volume 6283: intelligent data engineering and automated learning (IDEAL 2010)
    ISBN
    9783642153808
    School
    Centre for Extended Enterprises and Business Intelligence
    URI
    http://hdl.handle.net/20.500.11937/6865
    Collection
    • Curtin Research Publications
    Abstract

    Concept matching is important when heterogeneous data sources are to be merged for the purpose of knowledge sharing. It has many useful applications in areas such as schema matching, ontology matching, scientific knowledge management, e-commerce, enterprise application integration, etc. With the desire of knowledge sharing and reuse in these fields, merging commonly occurs among different organizations where the knowledge describing the same domain is to be matched. Due to the different naming conventions, granularity and the use of concepts in different contexts, a semantic approach to this problem is preferred in comparison to syntactic approach that performs matches based upon the labels only. We propose a concept matching method that initially does not consider labels when forming candidate matches, but rather utilizes structural information to take the context into account and detect complex matches. Real world knowledge representations (schemas) are used to evaluate the method.

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