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Data & Knowledge Engineering
Volume 63, Issue 2, November 2007, Pages 200-223
 
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doi:10.1016/j.datak.2007.02.001    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2007 Elsevier B.V. All rights reserved.

Reasoning and change management in modular ontologies

Heiner Stuckenschmidta, Corresponding Author Contact Information, E-mail The Corresponding Author and Michel Kleinb

aUniversität Mannheim, Germany bVrije Universiteit Amsterdam, The Netherlands

Received 13 December 2006; 
accepted 5 February 2007. 
Available online 20 February 2007.

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Abstract

The benefits of modular representations are well known from many areas of computer science. While in software engineering modularization is mainly a vehicle for supporting distributed development and re-use, in knowledge representation, the main goal of modularization is efficiency of reasoning. In this paper, we concentrate on the benefits of modularization in the context of ontologies, explicit representations of the terminology used in a domain. We define a formal representation for modular ontologies based on the notion of Distributed Description Logics and introduce an architecture that supports local reasoning by compiling implied axioms. We further address the problem of guaranteeing the correctness and completeness of compiled knowledge in the presence of changes in different modules. We propose a heuristic for analyzing changes and their impact on compiled knowledge and guiding the process of updating compiled information that can often reduce the effort of maintaining a modular ontology by avoiding unnecessary re-compilation.

Keywords: Ontologies; Reasoning; Distributed Knowledge Representation; Change management

Article Outline

1. Motivation
1.1. Why modularization?
1.2. Requirements
1.3. Related work
1.4. Our approach
2. Modular ontologies
2.1. Ontologies and description logics
2.2. The SHIQ description logic
2.3. Distributed description logic
2.4. Modular ontologies
2.4.1. Architecture
2.4.2. Restricting mapping expressiveness
3. Reasoning in modular ontologies
3.1. Reasoning based on DDL
3.2. Compilation and integrity
4. Evolution management
4.1. Determining harmless changes
4.2. Characterizing changes
4.3. Update management
5. Application in a case study
5.1. The WonderWeb case study
5.2. Modularization in the case study
5.2.1. Implied subsumption relations
5.3. Updating the models
5.3.1. Example 1: The employee concept
5.3.2. Example 2: The department-manager relation
5.3.3. Example 3: The department concept
6. Summary
7. Discussion
7.1. Feasibility
7.2. Adequacy
7.3. Generality
References
Vitae



Data & Knowledge Engineering
Volume 63, Issue 2, November 2007, Pages 200-223
 
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