Back to Search
Start Over
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
- 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.
- Subjects :
- 0301 basic medicine
Computer science
Knowledge Bases
Interoperability
Health Informatics
02 engineering and technology
Ontology (information science)
Clinical decision support system
Data modeling
Machine Learning
Open Biomedical Ontologies
03 medical and health sciences
Drug Therapy
0202 electrical engineering, electronic engineering, information engineering
Data Mining
Humans
Disease
RDF
Inference engine
Semantic Web
Information retrieval
Unstructured data
computer.file_format
Decision Support Systems, Clinical
Relationship extraction
Semantics
Computer Science Applications
030104 developmental biology
Formal ontology
Biological Ontologies
Ontology
020201 artificial intelligence & image processing
computer
Algorithms
Software
Subjects
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