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OWL2Vec: embedding of OWL ontologies

Machine learning, 2021-07, Vol.110 (7), p.1813-1845 [Peer Reviewed Journal]

The Author(s) 2021 ;The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-021-05997-6

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Title:
OWL2Vec: embedding of OWL ontologies
Author: Chen, Jiaoyan ; Hu, Pan ; Jimenez-Ruiz, Ernesto ; Holter, Ole Magnus ; Antonyrajah, Denvar ; Horrocks, Ian
Subjects: 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
Is Part Of: Machine learning, 2021-07, Vol.110 (7), p.1813-1845
Description: 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.
Publisher: New York: Springer US
Language: English
Identifier: ISSN: 0885-6125
EISSN: 1573-0565
DOI: 10.1007/s10994-021-05997-6
Source: SpringerOpen
AUTh Library subscriptions: ProQuest Central

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OWL2Vec: embedding of OWL ontologies

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