2001 OntologyLearningForTheSemanticWeb

(Maedche & Staab, 2001) ⇒ Alexander Maedche, Steffen Staab. (2001). “Ontology Learning for the Semantic Web.” In: IEEE Intelligent Systems, 16(2). doi:10.1109/5254.920602

Subject Headings: Ontology Learning Task, Text-To-Onto.

Notes

Cited By

2007

(Tao et al., 2007) ⇒ Xiaohui Tao, Yuefeng Li, and Richi Nayak. (2007). “Ontology Mining for Semantic Interpretation of Information Needs."
- Much effort has been invested in ontology learning or mining for semantic interpretation. Staab & Studer [13] formally define an ontology as a 4-tuple of a set of concepts, a set of relations, a set of instances and a set of axioms. Maedche & Staab [9] have another slightly different definition which differentiates the relations to hierarchical and plain relations.

Quotes

Abstract

The Semantic Web relies heavily on the formal ontologies that structure its underlying data for comprehensive and transportable machine understanding. Ontology learning greatly facilitates the construction of ontologies. The authors' view of ontology learning includes a number of complementary disciplines that feed on different types of unstructured, semistructured, and fully structured data to support semiautomatic, cooperative ontology engineering. In addition to discussing their general ontology-learning framework and architecture, the authors give examples of the ontology-learning cycle that they have implemented in their ontology-learning environment, Text-To-Onto, such as ontology learning from free text, dictionaries, or legacy ontologies.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2001 OntologyLearningForTheSemanticWeb	Steffen Staab Alexander Maedche			Ontology Learning for the Semantic Web		IEEE Intelligent Systems	http://www.aifb.uni-karlsruhe.de/WBS/sst/Research/Publications/ieee semweb.pdf	10.1109/5254.920602		2001