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  • Publication . Part of book or chapter of book . 2018
    Closed Access
    Authors: 
    Johannes Fähndrich; Sabine Weber; Hannes Kanthak;
    Publisher: Springer International Publishing
    Project: EC | NeMo (713794)

    This paper approaches a solution of Winograd Schemas with a marker passing algorithm which operates on an automatically generated semantic graph. The semantic graph contains common sense facts from data sources form the semantic web like domain ontologies e.g. from Linked Open Data (LOD), WordNet, Wikidata, and ConceptNet. Out of those facts, a semantic decomposition algorithm selects relevant facts for the concepts used in the Winograd Schema and adds them to the semantic graph. Markers are propagated through the graph and used to identify an answer to the Winograd Schema. Depending on the encoded knowledge in the graph (connectionist view of world knowledge) and the information encoded on the marker (for symbolic reasoning) our approach selects the answers. With this selection, the marker passing approach is able to beat the state-of-the-art approach by about 12%.

Include:
1 Research products, page 1 of 1
  • Publication . Part of book or chapter of book . 2018
    Closed Access
    Authors: 
    Johannes Fähndrich; Sabine Weber; Hannes Kanthak;
    Publisher: Springer International Publishing
    Project: EC | NeMo (713794)

    This paper approaches a solution of Winograd Schemas with a marker passing algorithm which operates on an automatically generated semantic graph. The semantic graph contains common sense facts from data sources form the semantic web like domain ontologies e.g. from Linked Open Data (LOD), WordNet, Wikidata, and ConceptNet. Out of those facts, a semantic decomposition algorithm selects relevant facts for the concepts used in the Winograd Schema and adds them to the semantic graph. Markers are propagated through the graph and used to identify an answer to the Winograd Schema. Depending on the encoded knowledge in the graph (connectionist view of world knowledge) and the information encoded on the marker (for symbolic reasoning) our approach selects the answers. With this selection, the marker passing approach is able to beat the state-of-the-art approach by about 12%.

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