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...

Cijeli opis

Bibliografski detalji
Glavni autori: Chen, J, Hu, P, Jimenez-Ruiz, E, Holter, OM, Antonyrajah, D, Horrocks, I
Format: Journal article
Jezik:English
Izdano: Springer 2021
_version_ 1826310147522166784
author Chen, J
Hu, P
Jimenez-Ruiz, E
Holter, OM
Antonyrajah, D
Horrocks, I
author_facet Chen, J
Hu, P
Jimenez-Ruiz, E
Holter, OM
Antonyrajah, D
Horrocks, I
author_sort Chen, J
collection OXFORD
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.
first_indexed 2024-03-07T07:46:15Z
format Journal article
id oxford-uuid:f993a116-69c3-4cca-8f82-3f8c006d3c9c
institution University of Oxford
language English
last_indexed 2024-03-07T07:46:15Z
publishDate 2021
publisher Springer
record_format dspace
spelling oxford-uuid:f993a116-69c3-4cca-8f82-3f8c006d3c9c2023-06-08T06:26:32ZOWL2Vec*: embedding of OWL ontologiesJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:f993a116-69c3-4cca-8f82-3f8c006d3c9cEnglishSymplectic ElementsSpringer2021Chen, JHu, PJimenez-Ruiz, EHolter, OMAntonyrajah, DHorrocks, ISemantic 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.
spellingShingle Chen, J
Hu, P
Jimenez-Ruiz, E
Holter, OM
Antonyrajah, D
Horrocks, I
OWL2Vec*: embedding of OWL ontologies
title OWL2Vec*: embedding of OWL ontologies
title_full OWL2Vec*: embedding of OWL ontologies
title_fullStr OWL2Vec*: embedding of OWL ontologies
title_full_unstemmed OWL2Vec*: embedding of OWL ontologies
title_short OWL2Vec*: embedding of OWL ontologies
title_sort owl2vec embedding of owl ontologies
work_keys_str_mv AT chenj owl2vecembeddingofowlontologies
AT hup owl2vecembeddingofowlontologies
AT jimenezruize owl2vecembeddingofowlontologies
AT holterom owl2vecembeddingofowlontologies
AT antonyrajahd owl2vecembeddingofowlontologies
AT horrocksi owl2vecembeddingofowlontologies