Publicação
Evaluating Pre-trained Word Embeddings in domain specific Ontology Matching
| datacite.subject.fos | Departamento de Informática | pt_PT |
| dc.contributor.advisor | Pesquita, Cátia, 1980- | |
| dc.contributor.author | Amorim, Sofia Pessoa de | |
| dc.date.accessioned | 2022-07-22T08:59:36Z | |
| dc.date.available | 2022-07-22T08:59:36Z | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2021 | |
| dc.description | Tese de mestrado, Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2022 | pt_PT |
| dc.description.abstract | The ontology matching process focuses on discovering mappings between two concepts from distinct ontologies, a source and a target. It is a fundamental step when trying to integrate heterogeneous data sources that are described in ontologies. This data represents an even more challenging problem since we are working with complex data as biomedical data. Thus, derived from the necessity of keeping on improving ontology matching techniques, this dissertation focused on implementing a new approach to the AML pipeline to calculate similarities between entities from two distinct ontologies. For the implementation of this dissertation, we used some of the OAEI tracks, such as Anatomy and LargeBio, to apply a new algorithm and evaluate if it improves AML’s results against a refer ence alignment. This new approach consisted of using pre-trained word embeddings of five different types, BioWordVec Extrinsic, BioWordVec Intrinsic, PubMed+PC, PubMed+PC+Wikipedia and English Wikipedia. These pre-trained word embeddings use a machine learning technique, Word2Vec, and were used in this work since it allows to carry the semantic meaning inherent to the words represented with the corresponding vector. Word embeddings allowed that each concept of each ontology was represented with a corresponding vector to see if, with that information, it was possible to improve how relations between concepts were determined in the AML system. The similarity between concepts was calculated through the cosine distance and the evaluation of the new alignment used the metrics precision recall and F-measure. Although we could not prove that word embeddings improve AML current results, this implementation could be refined, and the technique can be still an option to consider in future work if applied in some other way. | pt_PT |
| dc.identifier.tid | 203205685 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10451/53906 | |
| dc.language.iso | eng | pt_PT |
| dc.subject | Embeddings de Palavras | pt_PT |
| dc.subject | Alinhamento de Ontologias | pt_PT |
| dc.subject | Ontologias Biomédicas | pt_PT |
| dc.subject | Teses de mestrado - 2022 | pt_PT |
| dc.title | Evaluating Pre-trained Word Embeddings in domain specific Ontology Matching | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Tese de mestrado em Ciência de Dados | pt_PT |