OWL2Vec: embedding of OWL ontologies

Semantic embedding of knowledge graphs has been widely studied and used for prediction and statistical analysis tasks across various domains such as Natural Language Processing and the Semantic Web. However, less attention has been paid to developing robust methods for embedding OWL (Web Ontology La...

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Vydáno v:Machine learning Ročník 110; číslo 7; s. 1813 - 1845
Hlavní autoři: Chen, Jiaoyan, Hu, Pan, Jimenez-Ruiz, Ernesto, Holter, Ole Magnus, Antonyrajah, Denvar, Horrocks, Ian
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.07.2021
Springer Nature B.V
Témata:
Artificial Intelligence
Bioinformatics
Computer Science
Control
Domains
Embedding
Empirical analysis
Graphs
Knowledge representation
Machine Learning
Mechatronics
Natural language processing
Natural Language Processing (NLP)
Ontology
Programming languages
Random walk
Robotics
Semantic web
Semantics
Simulation and Modeling
Special issue on Learning and Reasoning
Statistical analysis
Web Ontology Language-OWL
Word embedding
Web ontology language
Ontology
Ontology completion
Ontology embedding
OWL2Vec
ISSN:0885-6125, 1573-0565
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Popis
Shrnutí:Semantic embedding of knowledge graphs has been widely studied and used for prediction and statistical analysis tasks across various domains such as Natural Language Processing and the Semantic Web. However, less attention has been paid to developing robust methods for embedding OWL (Web Ontology Language) ontologies, which contain richer semantic information than plain knowledge graphs, and have been widely adopted in domains such as bioinformatics. In this paper, we propose a random walk and word embedding based ontology embedding method named OWL2Vec*, which encodes the semantics of an OWL ontology by taking into account its graph structure, lexical information and logical constructors. Our empirical evaluation with three real world datasets suggests that OWL2Vec* benefits from these three different aspects of an ontology in class membership prediction and class subsumption prediction tasks. Furthermore, OWL2Vec* often significantly outperforms the state-of-the-art methods in our experiments.
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ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-021-05997-6