LREC 2006 - Proceedings sorted by papers

Title	Finding the Appropriate Generalization Level for Binary Ontological Relations Extracted from the Genia Corpus
Authors	P. Cimiano, M. Hartung, E. Ratsch
Abstract	Recent work has aimed at discovering ontological relations from text corpora. Most approaches are based on the assumption that verbs typically indicate semantic relations between concepts. However, the problem of finding the appropriate generalization level for the verb's arguments with respect to a given taxonomy has not received much attention in the ontology learning community. In this paper, we address the issue of determining the appropriate level of abstraction for binary relations extracted from a corpus with respect to a given concept hierarchy. For this purpose, we reuse techniques from the subcategorization and selectional restrictions acquisition communities. The contribution of our work lies in the systematic analysis of three different measures. We conduct our experiments on the Genia corpus and the Genia ontology and evaluate the different measures by comparing the results of our approach with a gold standard provided by one of the authors, a biologist.
Keywords	ontology learning, selectional restrictions, Bio-NLP
Full paper	Finding the Appropriate Generalization Level for Binary Ontological Relations Extracted from the Genia Corpus

Title

Finding the Appropriate Generalization Level for Binary Ontological Relations Extracted from the Genia Corpus

Authors

P. Cimiano, M. Hartung, E. Ratsch

Abstract

Recent work has aimed at discovering ontological relations from text corpora. Most approaches are based on the assumption that verbs typically indicate semantic relations between concepts. However, the problem of finding the appropriate generalization level for the verb's arguments with respect to a given taxonomy has not received much attention in the ontology learning community. In this paper, we address the issue of determining the appropriate level of abstraction for binary relations extracted from a corpus with respect to a given concept hierarchy. For this purpose, we reuse techniques from the subcategorization and selectional restrictions acquisition communities. The contribution of our work lies in the systematic analysis of three different measures. We conduct our experiments on the Genia corpus and the Genia ontology and evaluate the different measures by comparing the results of our approach with a gold standard provided by one of the authors, a biologist.

Keywords

ontology learning, selectional restrictions, Bio-NLP

Full paper

Finding the Appropriate Generalization Level for Binary Ontological Relations Extracted from the Genia Corpus