A DBpedia-based Benchmark for Ontology-mediated Query Answering

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Author(s)
Ma, S
Wang, Z
Wang, K
Year published
2021
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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 ...
View more >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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View more >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
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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