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Automated ontology generation framework powered by linked biomedical ontologies for disease-drug domain

Automated ontology generation framework powered by linked biomedical ontologies for disease-drug domain

Authors :
Maqbool Hussain
Mazen Alobaidi
Khalid Mahmood Malik
Source :
Computer Methods and Programs in Biomedicine. 165:117-128
Publication Year :
2018
Publisher :
Elsevier BV, 2018.

Abstract

Objective and background: The exponential growth of the unstructured data available in biomedical literature, and Electronic Health Record (EHR), requires powerful novel technologies and architectures to unlock the information hidden in the unstructured data. The success of smart healthcare applications such as clinical decision support systems, disease diagnosis systems, and healthcare management systems depends on knowledge that is understandable by machines to interpret and infer new knowledge from it. In this regard, ontological data models are expected to play a vital role to organize, integrate, and make informative inferences with the knowledge implicit in that unstructured data and represent the resultant knowledge in a form that machines can understand. However, constructing such models is challenging because they demand intensive labor, domain experts, and ontology engineers. Such requirements impose a limit on the scale or scope of ontological data models. We present a framework that will allow mitigating the time-intensity to build ontologies and achieve machine interoperability. Methods: Empowered by linked biomedical ontologies, our proposed novel Automated Ontology Generation Framework consists of five major modules: a) Text Processing using compute on demand approach. b) Medical Semantic Annotation using N-Gram, ontology linking and classification algorithms, c) Relation Extraction using graph method and Syntactic Patterns, d), Semantic Enrichment using RDF mining, e) Domain Inference Engine to build the formal ontology. Results: Quantitative evaluations show 84.78% recall, 53.35% precision, and 67.70% F-measure in terms of disease-drug concepts identification; 85.51% recall, 69.61% precision, and F-measure 76.74% with respect to taxonomic relation extraction; and 77.20% recall, 40.10% precision, and F-measure 52.78% with respect to biomedical non-taxonomic relation extraction. Conclusion: We present an automated ontology generation framework that is empowered by Linked Biomedical Ontologies. This framework integrates various natural language processing, semantic enrichment, syntactic pattern, and graph algorithm based techniques. Moreover, it shows that using Linked Biomedical Ontologies enables a promising solution to the problem of automating the process of disease-drug ontology generation.

Details

ISSN :
01692607
Volume :
165
Database :
OpenAIRE
Journal :
Computer Methods and Programs in Biomedicine
Accession number :
edsair.doi.dedup.....7715799a2ed316e54cd6b91342e4cb97
Full Text :
https://doi.org/10.1016/j.cmpb.2018.08.010