Linked open data-based framework for automatic biomedical ontology generation

Abstract

BackgroundFulfilling 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.ResultsOur 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.ConclusionThis 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.

An Automatic Ontology Generation Framework with An Organizational Perspective

“…To summarize, we conclude that most of the approaches used for automatic ontology generation from unstructured text corpus are domain-specific, demonstrating the need for domain independent ontology-generation methods [3,4]. Additionally, few systems used unstructured text from external heterogeneous sources [17,18].…”

Ontology-Based Approach to Semantically Enhanced Question Answering for Closed Domain: A Review

“…Many previous studies have revealed the effectiveness of semantic search engines over conventional keyword search engines when dealing with NLQ in several domains, among which are the biomedical field [46], expert systems [47], transportation [48], and forensics [49]. Meanwhile, reference [46] offered an automatic generation framework for novel ontology, called the linked open data (LOD) method for automatic biomedical ontology generation (LOD-ABOG), which is powered by LOD. The LOD-ABOG was utilized with the aim of extracting concepts utilizing KBs, mainly the medical system of language UMLS and LOD, together with the operation of NLP.…”

Comprehensive structured knowledge base system construction with natural language presentation

“…Recently, several approaches that reuse existing knowledge bases to automate ontology construction from unstructured text have been proposed [24][25][26]. The drawbacks of these approaches include labour costs to construct the dictionary, its domain-specific nature and the limited number of patterns.…”