Building Integrated Ontological Knowledge Structures with Efficient Approximation Algorithms

The integration of ontologies builds knowledge structures which brings new understanding on existingterminologies and their associations. With the steady increase in the number of ontologies, automaticintegration of ontologies is preferable over manual solutions in many applications. However, availa...

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Published in:BioMed research international Vol. 2015; no. 2015; pp. 1 - 14
Main Authors: Xiang, Yang, Janga, Sarath Chandra
Format: Journal Article
Language:English
Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2015
John Wiley & Sons, Inc
Subjects:
Algorithms
Biological Ontologies
Computer science
Controlled vocabularies
Database Management Systems
Databases, Factual
Datasets
Informatics
Information Storage and Retrieval - methods
Knowledge discovery
Life sciences
Machine Learning
Medical sciences
Methods
Natural Language Processing
Ontology
Pattern Recognition, Automated - methods
Semantic web
Studies
Systems Integration
Terminology
Terminology as Topic
Web Ontology Language-OWL
ISSN:2314-6133, 2314-6141, 2314-6141
Online Access:Get full text
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Summary:The integration of ontologies builds knowledge structures which brings new understanding on existingterminologies and their associations. With the steady increase in the number of ontologies, automaticintegration of ontologies is preferable over manual solutions in many applications. However, availableworks on ontology integration are largely heuristic without guarantees on the quality of the integrationresults. In this work, we focus on the integration of ontologies with hierarchical structures. We identifiedoptimal structures in this problem and proposed optimal and efficient approximation algorithms forintegrating a pair of ontologies. Furthermore, we extend the basic problem to address the integrationof a large number of ontologies, and correspondingly we proposed an efficient approximation algorithmfor integrating multiple ontologies. The empirical study on both real ontologies and synthetic datademonstrates the effectiveness of our proposed approaches. In addition, the results of integration betweengene ontology and National Drug File Reference Terminology suggest that our method provides a novelway to perform association studies between biomedical terms.
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Academic Editor: Dongchun Liang
ISSN:2314-6133
2314-6141
2314-6141
DOI:10.1155/2015/501528