Multilingual enrichment of disease biomedical ontologies
Translating biomedical ontologies is an important challenge, but doing it manually requires much time and money. We study the possibility to use open-source knowledge bases to translate biomedical ontologies. We focus on two aspects: coverage and quality. We look at the coverage of two biomedical ontologies focusing on diseases with respect to Wikidata for 9 European languages (Czech, Dutch, English, French, German, Italian, Polish, Portuguese and Spanish) for both ontologies, plus Arabic, Chinese and Russian for the second one. We first use direct links between Wikidata and the studied ontologies and then use second-order links by going through other intermediate ontologies. We then compare the quality of the translations obtained thanks to Wikidata with a commercial machine translation tool, here Google Cloud Translation.
Code (2)
Tasks
Machine TranslationTranslationSimilar Papers 제목 키워드 기반
Enrichment of French Biomedical Ontologies with UMLS Concepts and Semantic Types for Biomedical Named Entity Recognition Though Ontological Semantic Annotation
BiOnt: Deep Learning using Multiple Biomedical Ontologies for Relation Extraction
Successful biomedical relation extraction can provide evidence to researchers and clinicians about possible unknown associations between biomedical entities, advancing the current knowledge we have about those entities a…
Deep LearningRelationRelation ExtractionOPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction
Motivation: Ontologies are widely used in biology for data annotation, integration, and analysis. In addition to formally structured axioms, ontologies contain meta-data in the form of annotation axioms which provide val…
Semantic SimilaritySemantic Textual SimilarityA Fourfold Pathogen Reference Ontology Suite
Infectious diseases remain a critical global health challenge, and the integration of standardized ontologies plays a vital role in managing related data. The Infectious Disease Ontology (IDO) and its extensions, such as…
Ontology Enrichment from Texts: A Biomedical Dataset for Concept Discovery and Placement
Mentions of new concepts appear regularly in texts and require automated approaches to harvest and place them into Knowledge Bases (KB), e.g., ontologies and taxonomies. Existing datasets suffer from three issues, (i) mo…
Language ModelingLanguage ModellingLarge Language Model