2000 DiscoverConceptualRelFromText

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  • Ontologies have become an important means for structuring information and information systems and, hence, important in knowledge as well as in software engineering. However, there remains the problem of engineering large and adequate ontologies within short time frames in order to keep costs low. For this purpose, efforts have been made to facilitate the ontology engineering process, in particular the acquisition of ontologies from domain texts. We broaden these investigations with regard to two dimensions. First, we present a general architecture for discovering ontological concepts and relations. This architecture is general enough to subsume current approaches in this direction. Second, we propose a new approach to extend current approaches, who mostly focus on the semi-automatic acquisition of taxonomies, by the discovery of non-taxonomic conceptual relations. We use a generalized association rule algorithm that does not only detect relations between concepts, but also determines the appropriate level of abstraction at which to define relations. This is crucial for an appropriate ontology definition in order that it be succinct and conceptually adequate and, hence, easy to understand, maintain, and extend. In order to prove the validity of our proposal we evaluate the success of our learning approach against a manually engineered ontology. For this objective, we present a new paradigm suited to evaluate the degree to which relations that are learned match relations in a manually engineered ontology.



 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2000 DiscoverConceptualRelFromTextAlexander Maedche
Steffen Staab
Discovering Conceptual Relations from TextProceedings of the European Conference in Artificial Intelligencehttp://www.aifb.kit.edu/images/2/20/2000 252 Maedche Discovering Con 1.pdf2000