Machine Learning

EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs

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    EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs

    In order to answer natural language questions over knowledge graphs, most processing pipelines involve entity and relation linking. Traditionally, entity linking and relation linking has been performed either as dependent sequential tasks or independent parallel tasks. In this paper, we propose a framework, calledEARL, which performs entity linking and relation linking as a joint single task. EARL is modeled on an optimised variation of GeneralisedTravelling Salesperson Problem. The system determines the best semantic connection between all keywords of the question by referring to the knowledge graph. This is achieved by exploiting the connection density between entity candidates and relation candidates. We have empirically evaluated the framework on a dataset with 3000 complex questions. Our system surpasses state-of-the-art scores for entity linking task by reporting an accuracy of 0.67against 0.40 from the next best entity linker

    EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs
    by Mohnish Dubey, Debayan Banerjee, Debanjan Chaudhuri, Jens Lehmann
    https://arxiv.org/pdf/1801.03825v1.pdf

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