City Research Online

OWL2Vec*: Embedding of OWL ontologies

Chen, J., Hu, P., Jimenez-Ruiz, E. ORCID: 0000-0002-9083-4599, Holter, O. M., Antonyrajah, D. and Horrocks, I. (2021). OWL2Vec*: Embedding of OWL ontologies. Machine Learning, doi: 10.1007/s10994-021-05997-6

Abstract

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.

Publication Type: Article
Additional Information: This is a post-peer-review, pre-copyedit version of an article published in Machine Learning. The final authenticated version is available online at: https://doi.org/10.1007/s10994-021-05997-6
Publisher Keywords: Ontology, Ontology Embedding, Word Embedding, Web Ontology Language, OWL2Vec∗, Ontology Completion
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Departments: School of Mathematics, Computer Science & Engineering > Computer Science
Date available in CRO: 21 Apr 2021 07:34
Date deposited: 21 April 2021
Date of acceptance: 9 April 2021
Date of first online publication: 16 June 2021
URI: https://openaccess.city.ac.uk/id/eprint/25935
[img]
Preview
Text - Accepted Version
Available under License Creative Commons Attribution.

Download (634kB) | Preview

Export

Downloads

Downloads per month over past year

View more statistics

Actions (login required)

Admin Login Admin Login