Ontology Learning for the Semantic Web

Ontology Learning for the Semantic Web (2001)

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by Er Maedche , Steffen Staab

Venue:	IEEE Intelligent Systems
Citations:	285 - 15 self

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Datum	Value	Source
TITLE	Ontology Learning for the Semantic Web	SVM HeaderParse 0.2
AUTHOR NAME	Er Maedche	SVM HeaderParse 0.2
AUTHOR ADDR	Institute AIFB, D-76128 Karlsruhe, Germany	SVM HeaderParse 0.2
AUTHOR NAME	Steffen Staab	SVM HeaderParse 0.2
AUTHOR ADDR	Institute AIFB, D-76128 Karlsruhe, Germany	SVM HeaderParse 0.2
ABSTRACT	The Semantic Web relies heavily on the formal ontologies that structure underlying data for the purpose of comprehensive and transportable machine understanding. Therefore, 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 acquisition bottleneck. Ontology Learning greatly facilitates the construction of ontologies by the ontology engineer. The vision of ontology learning that we propose here includes a number of complementary disciplines that feed on different types of unstructured, 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, refinement, and evaluation giving the ontology engineer a wealth of coordinated tools for ontology modeling. Besides of the general framework and architecture, we show in this paper some exemplary techniques in the ontology learning cycle that we have implemented in our ontology learning environment, Text-To-Onto, such as ontology learning from free text, from dictionaries, or from legacy ontologies, and refer to some others that need to complement the complete architecture, such as reverse engineering	SVM HeaderParse 0.2
YEAR	2001	INFERENCE
VENUE	IEEE Intelligent Systems	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	72--79	INFERENCE
VOLUME	16	INFERENCE
CITATIONS	12 found	ParsCit 1.0