Authors:
Victoria Nebot
and
Rafael Berlanga
Affiliation:
Universitat Jaume I, Spain
Keyword(s):
Ontology generation, Ontology indexing, Knowledge repositories.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Data Engineering
;
Enterprise Information Systems
;
Health Information Systems
;
Informatics in Control, Automation and Robotics
;
Information Systems Analysis and Specification
;
Intelligent Control Systems and Optimization
;
Knowledge Engineering
;
Knowledge Engineering and Ontology Development
;
Knowledge Management
;
Knowledge-Based Systems
;
Knowledge-Based Systems Applications
;
Ontologies and the Semantic Web
;
Ontology Engineering
;
Society, e-Business and e-Government
;
Software Engineering
;
Symbolic Systems
;
Web Information Systems and Technologies
Abstract:
Nowadays very large domain knowledge resources are being developed in domains like Biomedicine. Users and applications can benefit enormously from these repositories in very different tasks, such as visualization, vocabulary homogenizing and classification. However, due to their large size and lack of formal semantics, they cannot be properly managed and exploited. Instead, it is necessary to provide small and useful logic-based ontologies from these large knowledge resource so that they become manageable and the user can take benefit from the semantics encoded. In this work we present a novel framework for efficiently indexing and generating ontologies according to the user requirements. Moreover, the generated ontologies are encoded using OWL logic-based axioms so that ontologies are provided with reasoning capabilities. Such a framework relies on an interval labeling scheme that efficiently manages the transitive relationships present in the domain knowledge resources. We have eva
luated the proposed framework over the Unified Medical Language System (UMLS). Results show very good performance and scalability, demonstrating the applicability of the proposed framework in real scenarios.
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