Sesame: A Generic Architecture for Storing and Querying RDF and RDF Schema
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
Experiments with Clustering as a Software Remodularization Method
WCRE '99 Proceedings of the Sixth Working Conference on Reverse Engineering
ICSM '01 Proceedings of the IEEE International Conference on Software Maintenance (ICSM'01)
Ontology selection for the real semantic web: how to cover the queen's birthday dinner?
EKAW'06 Proceedings of the 15th international conference on Managing Knowledge in a World of Networks
Ontology modularization for knowledge selection: experiments and evaluations
DEXA'07 Proceedings of the 18th international conference on Database and Expert Systems Applications
Component models for semantic web languages
Semantic techniques for the web
Ontology modularization to improve semantic medical image annotation
Journal of Biomedical Informatics
KR4HC'09 Proceedings of the 2009 AIME international conference on Knowledge Representation for Health-Care: data, Processes and Guidelines
An information content based partitioning method for the anatomical ontology matching task
Proceedings of the Third Symposium on Information and Communication Technology
Enhancing LOD Complex Query Building with Context
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Model-theoretic inseparability and modularity of description logic ontologies
Artificial Intelligence
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In this chapter we describe a method for structure-based ontology partitioning and its implementation that is practically applicable to very large ontologies. We show that a modularization based on structural properties of the ontology only already results in modules that intuitively make sense. The method was used for creating an overview graph for ontologies and for extracting key topics from an ontology that correspond to topics selected by human experts. Because the optimal modularization of an ontology greatly depends on the application it is used for, we implemented the partitioning algorithm in a way that allows for adaption to different requirements. Furthermore this adaption can be performed automatically by specifying requirements of the application.