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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 2 of 2 1 OWL2Vec*: Embedding of OWL Ontologies Chen, J.; Hu, P.; Jimenez-Ruiz, E.. - 2021 BASE Show details 2 OWL2Vec*: Embedding of OWL ontologies Chen, J.; Hu, P.; Jimenez-Ruiz, E.; Holter, O. M.; Antonyrajah, D.; Horrocks, I.. - : Springer Verlag, 2021 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. Keyword: QA75 Electronic computers. Computer science; Z665 Library Science. Information Science URL: https://openaccess.city.ac.uk/id/eprint/25935/ https://openaccess.city.ac.uk/id/eprint/25935/1/OWL2Vec-Star_ML-Journal-Springer.pdf https://doi.org/10.1007/s10994-021-05997-6 BASE Hide details

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