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Names Are Not Just Sound and Smoke: Word Embeddings for Axiom Selection

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Automated Deduction – CADE 27 (CADE 2019)

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Abstract

First-order theorem proving with large knowledge bases makes it necessary to select those parts of the knowledge base, that are necessary to prove the theorem at hand. We extend syntactic axiom selection procedures like SInE to use semantics of symbol names. For this, not only occurrences of symbol names but also semantically similar names are taken into account. We use a similarity measure based on word embeddings. An evaluation of this similarity based SInE is given using problems from TPTP’s CSR problem class and Adimen-SUMO. This evaluation is done with two very different systems, namely the Hyper tableau prover and the saturation based system E.

Work supported by DFG grant CoRg – Cognitive Reasoning. Author names are given in alphabetical order.

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Notes

  1. 1.

    WordNet mappings usually also contain information about subclass or instance of relations. Since these relations are not relevant for this paper, they are omitted.

  2. 2.

    Implementation (git hash ‘eeee0fc0b46c688ec25e08806d39ec8cea93cbc0’) available at https://gitlab.uni-koblenz.de/corg/similaritysine.

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Authors and Affiliations

  1. Institute for Computer Science, University of Koblenz-Landau, Koblenz, Germany

    Ulrich Furbach & Teresa Krämer

  2. Institute for Web Science and Technologies, University of Koblenz-Landau, Koblenz, Germany

    Claudia Schon

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  1. Ulrich Furbach
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  2. Teresa Krämer
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  3. Claudia Schon
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Correspondence to Claudia Schon .

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  1. University of Lorraine, Villers-lès-Nancy, France

    Pascal Fontaine

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Furbach, U., Krämer, T., Schon, C. (2019). Names Are Not Just Sound and Smoke: Word Embeddings for Axiom Selection. In: Fontaine, P. (eds) Automated Deduction – CADE 27. CADE 2019. Lecture Notes in Computer Science(), vol 11716. Springer, Cham. https://doi.org/10.1007/978-3-030-29436-6_15

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  • DOI: https://doi.org/10.1007/978-3-030-29436-6_15

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