Linked open data-based framework for automatic biomedical ontology generation

• Published in: BMC bioinformatics Vol. 19; no. 1; pp. 319 - 13
• Main Authors: Alobaidi, Mazen, Malik, Khalid Mahmood, Sabra, Susan
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 10.09.2018; BioMed Central; BMC
• Subjects: Artificial intelligence; Automation; Bioinformatics; Biological Ontologies; Biomedical data; Biomedical engineering; Biomedical Research; Controlled vocabularies; Datasets; Dictionaries; Disease; Explicit knowledge; Health informatics; Humans; International conferences; Knowledge Bases; Knowledge bases (artificial intelligence); Labor; Language; Life cycle engineering; Life cycles; Linguistics; Linked Data; Linked open data; Machine learning; Medical research; Medical terminology; Methodology; Natural Language Processing; Ontology; Ontology generation; Open data; Resource Description Framework-RDF; Semantic enrichment; Semantic networks; Semantic web; Semantics; Subject specialists; Taxonomy; Teaching methods; Web Ontology Language-OWL; Word sense disambiguation; Semantic web; Semantic enrichment; Linked open data; Ontology generation

Fulfilling the vision of Semantic Web requires an accurate data model for organizing knowledge and sharing common understanding of the domain. Fitting this description, ontologies are the cornerstones of Semantic Web and can be used to solve many problems of clinical information and biomedical engin...