DOI: 10.5281/zenodo.4882682
title: Embeddings of Gene Ontology (GO) using OWL2Vec*

description: These embeddings are from the Gene Ontology (GO) [1] downloaded on 19 Oct 2020, were created using OWL2Vec* [2], and are in binary format (output using pickle). Some information about the embedding settings and an example for how they were used are found in Ritchie et al.'s paper "Ontology Clustering with OWL2Vec*" [3]. References<br> [1] The Gene Ontology Consortium. The Gene Ontology Resource: 20 years and still GOing strong, Nucleic Acids Research, Volume 47, 2019. Accessed 9th October 2020.<br> [2] Chen J, Hu P, Jimenez-Ruiz E, Holter OM, Antonyrajah D, Horrocks I. OWL2Vec*: Embedding of OWL Ontologies. Machine Learning, Springer, 2021. https://github.com/KRR-Oxford/OWL2Vec-Star<br> [3] Ritchie, A., Chen, J., Castro, L. J., Rebholz-Schuhmann, D. and Jimenez-Ruiz, E. <em>Ontology Clustering with OWL2Vec*</em>. Paper presented at the Deep Learning meets Ontologies and Natural Language Processing (DeepOntoNLP2021), 6-10 Jun 2021, Online.
description: The naming convention of the files are as follows where each symbol is separated by a dash (e.g. 3-L-R-2-50): First Symbol - Document/Embedding Settings 1 - Structure Document, IRI embeddings [D(s), V(iri)] 2 - Lexical Document, IRI embeddings [D(s,l), V(iri)] 3 - Lexical Document, Word embeddings [D(s,l), V(word)] 4 - Lexical Document, IRI & Word embeddings [D(s,l), V(iri, word)] 5 - Combined Document - random, IRI embeddings [D(s,l,rc), V(iri)] 6 - Combined Document - random, word embeddings [D(s,l,rc), V(word)] 7 - Combined Document - random, IRI & Word embeddings [D(s,l,rc), V(iri, word)] 8 - Combined Document - traversal, IRI embeddings [D(s,l,tc), V(iri)] 9 - Combined Document - traversal, word embeddings [D(s,l,tc), V(word)] 10 - Combined Document - traversal, IRI & Word embeddings [D(s,l,tc), V(iri, word)] Second Symbol - Annotation Settings A - All annotations L - Limited annotations (label, hasExactSynonym) Third Symbol - Walker Type R - Random walker WL - Weisfeiler Lehman sub-tree kernel Fourth Symbol - Walk Depth 2 - creates walks similar to (A, subclassof, B) 4 - creates walks similar to (A, subclassof, B, subclassof, C) 6 - creates walks similar to (A, subclassof, B, subclassof, C, subclassof, D) Fifth Symbol - Embedding Size 50 - output vector space will be [44,272, 50] 100 - output vector space will be [44,272, 100] 200 - output vector space will be [44,272, 200] Example 3-L-R-2-50 Will extract lexical information from the ontology and output Word embeddings only [D(s,l), V(word)]. The annotations used are limited (label and hasExactSynonym). It uses a random walker to extract ontology information. The walk depth is 2. The output embedding size is 50 for each class.
description: {"references": ["Ritchie, A.,\u00a0Chen, J.,\u00a0Castro, L. J.,\u00a0Rebholz-Schuhmann, D.\u00a0and Jimenez-Ruiz, E.\u00a0Ontology Clustering with OWL2Vec*. Paper presented at the Deep Learning meets Ontologies and Natural Language Processing (DeepOntoNLP2021), 6-10 Jun 2021, Online.", "Chen J, Hu P, Jimenez-Ruiz E, Holter OM, Antonyrajah D, Horrocks I. OWL2Vec*: Embedding of OWL Ontologies. Machine Learning, Springer, 2021. https://github.com/KRR-Oxford/OWL2Vec-Star", "The Gene Ontology Consortium. The Gene Ontology Resource: 20 years and still GOing strong, Nucleic Acids Research, Volume 47, 2019"]}