2006 OntologizingSemanticRelations
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- (Pennacchiotti & Pantel, 2006) ⇒ Marco Pennacchiotti, Patrick Pantel. (2006). “Ontologizing Semantic Relations.” In: Proceedings of Conference on Computational Linguistics / Association for Computational Linguistics (ACL 2006).
Subject Headings: Semantic Relation, Ontology, Ontology Population Task.
Notes
- It is expanded on in the Edited Book Chapter (Pantel & Pennacchiotti, 2008)
Cited By
Quotes
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 ontologizing (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 (Ravichandran 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#1” and “orange#2 similar-to blue#2”. In his framework, 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 take the next step and explore two algorithms for ontologizing binary semantic relations into WordNet and we present empirical results on the task of attaching part-of and causation relations. Formally, given an instance (x, r, y) of a binary relation r between terms x and y, the ontologizing task is to identify the WordNet senses of x and y where r holds. For example, the instance (proton, PART-OF, element) ontologizes into WordNet as (proton#1, PART-OF, element#2).
References
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