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Learning Ontologies for the Semantic Web

Learning Ontologies for the Semantic Web

Published: 2001

Buchtitel: Semantic Web 2001 (at WWW10), May 1, 2001, Hongkong, China

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The Semantic Web relies heavily on the formal ontologies that structure underlying data for the purpose of compre- hensive and transportable machine understanding. There- fore, the success of the Semantic Web depends strongly on the proliferation of ontologies, which requires fast and easy engineering of ontologies and avoidance of a knowledge ac- quisition bottleneck. Ontology Learning greatly facilitates the construction of ontologies by theontology engineer. The vision of ontology learning that we propose here includes a number of comple- mentary disciplines that feed on dierent types of unstruc- tured, semi-structured and fully structured data in order to support a semi-automatic, cooperative ontology engineering process. Our ontology learning framework proceeds through ontology import, extraction, pruning, renement, and eval- uation giving the ontology engineer a wealth of coordinated tools for ontology modeling. Besides of the general frame- work and architecture, we show in this paper some exem- plary techniques in the ontology learning cycle that we have implemented in our ontology learning environment, Text- To-Onto, such asontology learning from free text, from dic- tionaries, or from legacy ontologies, and refer to some others that need to complement the complete architecture, such as reverse engineering of ontologies from database schemata or learning from XML documents.

Download: Media:2001_488_Maedche_Learning_Ontolo_1.pdf