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Leveraging Semantic Parsing for Relation Linking over Knowledge Bases

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The Semantic Web – ISWC 2020 (ISWC 2020)

Abstract

Knowledge base question answering systems are heavily dependent on relation extraction and linking modules. However, the task of extracting and linking relations from text to knowledge bases faces two primary challenges; the ambiguity of natural language and lack of training data. To overcome these challenges, we present SLING, a relation linking framework which leverages semantic parsing using Abstract Meaning Representation (AMR) and distant supervision. SLING integrates multiple approaches that capture complementary signals such as linguistic cues, rich semantic representation, and information from the knowledge base. The experiments on relation linking using three KBQA datasets, QALD-7, QALD-9, and LC-QuAD 1.0 demonstrate that the proposed approach achieves state-of-the-art performance on all benchmarks.

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Notes

  1. 1.

    https://github.com/IBM/kbqa-relation-linking.

  2. 2.

    http://verbs.colorado.edu/propbank/framesets-english-aliases/produce.html.

  3. 3.

    https://amr.isi.edu/doc/ne-types.html.

  4. 4.

    http://verbs.colorado.edu/propbank/framesets-english-aliases/bear.html.

  5. 5.

    Ideally, we can do the hard filtering with the relation connections. However as REL is a component of a whole KBQA pipeline. To mitigate potential error propagation from entity linking, most works adopt a soft approach  [6, 16].

  6. 6.

    We exclude rdf:type, and rdfs:label to follow same setting in [16].

  7. 7.

    Falcon numbers on QALD-7 and LC-QuAD 1.0 are taken from their paper.

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Correspondence to Nandana Mihindukulasooriya .

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Mihindukulasooriya, N. et al. (2020). Leveraging Semantic Parsing for Relation Linking over Knowledge Bases. In: Pan, J.Z., et al. The Semantic Web – ISWC 2020. ISWC 2020. Lecture Notes in Computer Science(), vol 12506. Springer, Cham. https://doi.org/10.1007/978-3-030-62419-4_23

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  • DOI: https://doi.org/10.1007/978-3-030-62419-4_23

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