Structuring Linked Data Search Results Using Probabilistic Soft Logic: ISWC 2016: The Semantic Web – ISWC 2016

Dhahi Al Shekaili, Alvaro A A Fernandes, Norman Paton

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    On-the-y generation of integrated representations of Linked
    Data (LD) search results is challenging because it requires successfully
    automating a number of complex subtasks, such as structure inference
    and matching of both instances and concepts, each of which gives rise to
    uncertain outcomes. Such uncertainty is unavoidable given the semantically
    heterogeneous nature of web sources, including LD ones. This paper
    approaches the problem of structuring LD search results as an evidencebased
    one. In particular, the paper shows how one formalism (viz., probabilistic
    soft logic (PSL)) can be exploited to assimilate different sources
    of evidence in a principled way and to beneficial effect for users. The
    paper considers syntactic evidence derived from matching algorithms,
    semantic evidence derived from LD vocabularies, and user evidence, in
    the form of feedback. The main contributions are: sets of PSL rules that
    model the uniform assimilation of diverse kinds of evidence, an empirical
    evaluation of how the resulting PSL programs perform in terms of their
    ability to infer structure for integrating LD search results, and, finally,
    a concrete example of how populating such inferred structures for presentation
    to the end user is benefcial, besides enabling the collection of
    feedback whose assimilation further improves search result presentation.
    Original languageEnglish
    Title of host publicationISWC 2016: The Semantic Web – ISWC 2016
    Publication statusPublished - 2016
    EventISWC2016 The 15th International Semantic Web Conference - Kobe, Japan
    Duration: 17 Oct 201621 Oct 2016

    Publication series

    NameLecture Notes in Computer Science
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    ConferenceISWC2016 The 15th International Semantic Web Conference


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