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Generation of Alginate Microspheres for Biomedical Applications

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Published on: August 12, 2012

Linked open data-based framework for automatic biomedical ontology generation.

Mazen Alobaidi^1,2, Khalid Mahmood Malik³, Susan Sabra¹

¹Computer Science and Engineering Department, Oakland University, 2200 N. Squirrel Rd, Rochester, MI, 48309, USA.

BMC Bioinformatics

|September 12, 2018

Summary

This summary is machine-generated.

This study introduces LOD-ABOG, an automated framework for generating biomedical ontologies using Linked Open Data (LOD). It significantly improves concept and relation extraction, reducing the need for manual expert input in ontology development.

Keywords:

Linked open data Ontology generation Semantic enrichment Semantic web

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Area of Science:

Biomedical Informatics
Semantic Web Technologies
Knowledge Representation

Background:

Ontologies are crucial for Semantic Web, enabling knowledge organization and domain understanding in clinical information and biomedical engineering.
Manual ontology construction is time-consuming and requires significant domain expert involvement.
Automated methods are needed to streamline ontology generation and reduce reliance on experts.

Purpose of the Study:

To present a novel automated ontology generation framework, Linked Open Data approach for Automatic Biomedical Ontology Generation (LOD-ABOG).
To leverage Linked Open Data (LOD) and Natural Language Processing (NLP) for efficient concept and relation extraction.
To reduce the labor-intensive nature of ontology development and minimize the need for domain experts.

Main Methods:

Concept extraction using UMLS, LOD, and NLP techniques.
Relation extraction employing LOD, Breadth-First Search (BFS) graph methods, and Freepal repository patterns.
Framework evaluation using CDR and SemMedDB datasets, and comparison with a manually constructed Alzheimer ontology and the OntoGain framework.

Main Results:

LOD-ABOG demonstrated improved performance in concept and relation extraction tasks compared to existing frameworks.
Achieved F-measures of 58.12% (CDR) and 81.68% (SemMedDB) for concept extraction.
Achieved F-measures of 65.26% (CDR) and 77.44% (SemMedDB) for taxonomic relation extraction, and 52.78% (CDR) and 58.12% (SemMedDB) for non-taxonomic relation extraction.
Outperformed the OntoGain framework by 14.76% in relation extraction.

Conclusions:

The LOD-ABOG framework effectively automates biomedical ontology generation using LOD sources and technologies.
The approach significantly enhances the extraction of relations and concepts, requiring expert involvement primarily for final refinements.
LOD-ABOG presents a promising solution for scalable and efficient biomedical ontology development.