An embedding-based approach to constructing OWL ontologies

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Author(s)
Zhang, L
Zhang, X
Zhao, L
Tian, J
Chen, S
Wu, H
Wang, K
Feng, Z
Griffith University Author(s)
Year published
2018
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This paper presents a novel system OWLearner for automatically extracting axioms for OWL ontologies from RDF data using embedding models. In this system, ontology construction is transformed to the classification problem in machine learning and thus off-the-shelf tools can be employed to learn axioms in OWL. There are mainly three modules, namely, embedding, sampling, and training & learning. Large ontologies DBpedia and YAGO are used to validate the proposed approach. The experimental results show that OWLearner is able to learn high-quality expressive OWL axioms automatically and efficiently.This paper presents a novel system OWLearner for automatically extracting axioms for OWL ontologies from RDF data using embedding models. In this system, ontology construction is transformed to the classification problem in machine learning and thus off-the-shelf tools can be employed to learn axioms in OWL. There are mainly three modules, namely, embedding, sampling, and training & learning. Large ontologies DBpedia and YAGO are used to validate the proposed approach. The experimental results show that OWLearner is able to learn high-quality expressive OWL axioms automatically and efficiently.
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Conference Title
CEUR Workshop Proceedings
Volume
2180
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Copyright Statement
2018. This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.