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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 – 6 of 6 1 MOESM2 of Combining lexical and context features for automatic ontology extension ... Althubaiti, Sara; Åženay Kafkas; Abdelhakim, Marwa. - : figshare, 2020 BASE Show details 2 MOESM1 of Combining lexical and context features for automatic ontology extension ... Althubaiti, Sara; Åženay Kafkas; Abdelhakim, Marwa. - : figshare, 2020 BASE Show details 3 MOESM1 of Combining lexical and context features for automatic ontology extension ... Althubaiti, Sara; Åženay Kafkas; Abdelhakim, Marwa. - : figshare, 2020 BASE Show details 4 MOESM2 of Combining lexical and context features for automatic ontology extension ... Althubaiti, Sara; Åženay Kafkas; Abdelhakim, Marwa. - : figshare, 2020 BASE Show details 5 Combining lexical and context features for automatic ontology extension ... Althubaiti, Sara; Åženay Kafkas; Abdelhakim, Marwa; Hoehndorf, Robert. - : figshare, 2020 Abstract: Abstract Background Ontologies are widely used across biology and biomedicine for the annotation of databases. Ontology development is often a manual, time-consuming, and expensive process. Automatic or semi-automatic identification of classes that can be added to an ontology can make ontology development more efficient. Results We developed a method that uses machine learning and word embeddings to identify words and phrases that are used to refer to an ontology class in biomedical Europe PMC full-text articles. Once labels and synonyms of a class are known, we use machine learning to identify the super-classes of a class. For this purpose, we identify lexical term variants, use word embeddings to capture context information, and rely on automated reasoning over ontologies to generate features, and we use an artificial neural network as classifier. We demonstrate the utility of our approach in identifying terms that refer to diseases in the Human Disease Ontology and to distinguish between different types ... Keyword: 69999 Biological Sciences not elsewhere classified; 80699 Information Systems not elsewhere classified; Cancer; FOS Biological sciences; FOS Computer and information sciences; FOS Sociology; Medicine; Sociology; Space Science URL: https://springernature.figshare.com/collections/Combining_lexical_and_context_features_for_automatic_ontology_extension/4816113 https://dx.doi.org/10.6084/m9.figshare.c.4816113 BASE Hide details 6 Combining lexical and context features for automatic ontology extension ... Althubaiti, Sara; Åženay Kafkas; Abdelhakim, Marwa. - : figshare, 2020 BASE Show details

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