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
• DOI: 10.1186/s12859-018-2339-3
• ISSN: 1471-2105

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 engineering, such as word sense disambiguation, semantic similarity, question answering, ontology alignment, etc. Manual construction of ontology is labor intensive and requires domain experts and ontology engineers. To downsize the labor-intensive nature of ontology generation and minimize the need for domain experts, we present a novel automated ontology generation framework, Linked Open Data approach for Automatic Biomedical Ontology Generation (LOD-ABOG), which is empowered by Linked Open Data (LOD). LOD-ABOG performs concept extraction using knowledge base mainly UMLS and LOD, along with Natural Language Processing (NLP) operations; and applies relation extraction using LOD, Breadth first Search (BSF) graph method, and Freepal repository patterns. Our evaluation shows improved results in most of the tasks of ontology generation compared to those obtained by existing frameworks. We evaluated the performance of individual tasks (modules) of proposed framework using CDR and SemMedDB datasets. For concept extraction, evaluation shows an average F-measure of 58.12% for CDR corpus and 81.68% for SemMedDB; F-measure of 65.26% and 77.44% for biomedical taxonomic relation extraction using datasets of CDR and SemMedDB, respectively; and F-measure of 52.78% and 58.12% for biomedical non-taxonomic relation extraction using CDR corpus and SemMedDB, respectively. Additionally, the comparison with manually constructed baseline Alzheimer ontology shows F-measure of 72.48% in terms of concepts detection, 76.27% in relation extraction, and 83.28% in property extraction. Also, we compared our proposed framework with ontology-learning framework called "OntoGain" which shows that LOD-ABOG performs 14.76% better in terms of relation extraction. This paper has presented LOD-ABOG framework which shows that current LOD sources and technologies are a promising solution to automate the process of biomedical ontology generation and extract relations to a greater extent. In addition, unlike existing frameworks which require domain experts in ontology development process, the proposed approach requires involvement of them only for improvement purpose at the end of ontology life cycle.