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Predicting RDF triples in incomplete knowledge bases with tensor factorization

Published:26 March 2012Publication History

ABSTRACT

On RDF datasets, the truth values of triples are known when they are either explicitly stated or can be inferred using logical entailment. Due to the open world semantics of RDF, nothing can be said about the truth values of triples that are neither in the dataset nor can be logically inferred. By estimating the truth values of such triples, one could discover new information from the database thus enabling to broaden the scope of queries to an RDF base that can be answered, support knowledge engineers in maintaining such knowledge bases or recommend users resources worth looking into for instance. In this paper, we present a new approach to predict the truth values of any RDF triple. Our approach uses a 3-dimensional tensor representation of the RDF knowledge base and applies tensor factorization techniques that take open world semantics into account to predict new true triples given already observed ones. We report results of experiments on real world datasets comparing different tensor factorization models. Our empirical results indicate that our approach is highly successful in estimating triple truth values on incomplete RDF datasets.

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  • Published in

    cover image ACM Conferences
    SAC '12: Proceedings of the 27th Annual ACM Symposium on Applied Computing
    March 2012
    2179 pages
    ISBN:9781450308571
    DOI:10.1145/2245276
    • Conference Chairs:
    • Sascha Ossowski,
    • Paola Lecca

    Copyright © 2012 ACM

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 26 March 2012

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    SAC '12 Paper Acceptance Rate270of1,056submissions,26%Overall Acceptance Rate1,650of6,669submissions,25%

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