tailieunhanh - Báo cáo khoa học: "Ontologizing Semantic Relations"

Many algorithms have been developed to harvest lexical semantic resources, however few have linked the mined knowledge into formal knowledge repositories. In this paper, we propose two algorithms for automatically ontologizing (attaching) semantic relations into WordNet. We present an empirical evaluation on the task of attaching partof and causation relations, showing an improvement on F-score over a baseline model. iati | Ontologizing Semantic Relations Marco Pennacchiotti ART Group - DISP University of Rome Tor Vergata Viale del Politecnico 1 Rome Italy pennacchiotti@ Abstract Many algorithms have been developed to harvest lexical semantic resources however few have linked the mined knowledge into formal knowledge repositories. In this paper we propose two algorithms for automatically ontologiz-ing attaching semantic relations into WordNet. We present an empirical evaluation on the task of attaching part-of and causation relations showing an improvement on F-score over a baseline model. 1 Introduction NLP researchers have developed many algorithms for mining knowledge from text and the Web including facts Etzioni et al. 2005 semantic lexicons Riloff and Shepherd 1997 concept lists Lin and Pantel 2002 and word similarity lists Hindle 1990 . Many recent efforts have also focused on extracting binary semantic relations between entities such as entailments Szpektor et al. 2004 is-a Ravi-chandran and Hovy 2002 part-of Girju et al. 2003 and other relations. The output of most of these systems is flat lists of lexical semantic knowledge such as Italy is-a country and orange similar-to blue . However using this knowledge beyond simple keyword matching for example in inferences requires it to be linked into formal semantic repositories such as ontologies or term banks like WordNet Fellbaum 1998 . Pantel 2005 defined the task of ontologizing a lexical semantic resource as linking its terms to the concepts in a WordNet-like hierarchy. For example orange similar-to blue ontologizes in WordNet to orange 2 similar-to blue and orange 2 similar-to blue 2 . In his framework Patrick Pantel Information Sciences Institute University of Southern California 4676 Admiralty Way Marina del Rey CA90292 pantel@ Pantel proposed a method of inducing ontological co-occurrence vectors 1 which are subsequently used to ontologize unknown terms into WordNet with 74 accuracy. In this paper we