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  • A DBpedia-based Benchmark for Ontology-mediated Query Answering

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    Author(s)
    Ma, S
    Wang, Z
    Wang, K
    Griffith University Author(s)
    Wang, Zhe
    Wang, Kewen
    Year published
    2021
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    Abstract
    Ontology-mediated query answering (OMQA) is a frame-work for querying data with a background ontology. Detailed evaluation of OMQA systems remains a challenge due to limitations in existing benchmarks. In this paper, we propose a new benchmark for OMQA based on natural language questions over DBpedia. In particular, the data are sampled from DBpedia with adjustable volumes and can easily reach a scale that is diffcult for existing OMQA systems to handle. Log-ical rules are automatically extracted from DBpedia using a rule learner, and the queries come from real-life natural language questions over DB-pedia. We evaluated two ...
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    Ontology-mediated query answering (OMQA) is a frame-work for querying data with a background ontology. Detailed evaluation of OMQA systems remains a challenge due to limitations in existing benchmarks. In this paper, we propose a new benchmark for OMQA based on natural language questions over DBpedia. In particular, the data are sampled from DBpedia with adjustable volumes and can easily reach a scale that is diffcult for existing OMQA systems to handle. Log-ical rules are automatically extracted from DBpedia using a rule learner, and the queries come from real-life natural language questions over DB-pedia. We evaluated two state-of-The-Art systems under various settings, to demonstrate the potential of our benchmark in benchmarking and analyzing the behavior of OMQA systems.
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    Conference Title
    CEUR Workshop Proceedings
    Volume
    2980
    Publisher URI
    http://ceur-ws.org/Vol-2980/
    Copyright Statement
    © The Author(s) 2021. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
    Subject
    Artificial intelligence
    Publication URI
    http://hdl.handle.net/10072/410281
    Collection
    • Conference outputs

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