Semantic Web Dog Food

Presented at: 3rd European Semantic Web Conference (ESWC2006)

by Ting Wang, Kalina Bontcheva, Yaoyong Li, Hamish Cunningham

Automatic extraction of semantic relationships between entity instances in an ontology is useful for attaching richer semantic metadata to documents. In this paper we propose an SVM based approach to hierarchical relation extraction, using features derived automatically from a number of GATE-based open-source language processing tools. In comparison to the previous works, we use several new features including part of speech tag, entity subtype, entity class, entity role, semantic representation of sentence and WordNet synonym set. The impact of the features on the performance is investigated, as is the impact of the relation classification hierarchy. The results show there is a trade-off among these factors for relation extraction and the features containing more information such as semantic ones can improve the performance of the ontological relation extraction task.

Automatic Extraction of Hierarchical Relations from Text was presented at this event.

Keywords: Ontology population / generation, Web data extraction

Resource URI on the dog food server: http://data.semanticweb.org/conference/eswc/2006/paper/wang-bontcheva
Same as: http://www.eswc2006.org/full-papers/#wang-bontcheva

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