Ontologizing Semantic Relations

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 793–800, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Ontologizing Semantic Relations Marco Pennacchiotti ART Group - DISP University of Rome “Tor Vergata” Viale del Politecnico 1 Rome, Italy pennacchiotti@info.uniroma2.it Patrick Pantel Information Sciences Institute University of Southern California 4676 Admiralty Way Marina del Rey, CA90292 pantel@isi.edu Abstract Many algorithms have been developed to harvest lexical semantic resources, however few have linked the mined knowledge into formal knowledge re- positories. 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 algo- rithms for mining knowledge from text and the Web, including facts (Etzioni et al. 2005), se- mantic lexicons (Riloff and Shepherd 1997), concept lists (Lin and Pantel 2002), and word similarity lists (Hindle 1990). Many recent ef- forts have also focused on extracting binary se- mantic 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#1” and “orange#2 similar-to blue#2”. In his framework, Pantel proposed a method of inducing ontologi- cal co-occurrence vectors 1 which are subse- quently 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 causa- tion 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). The first algorithm that we explore, called the anchoring approach, was suggested as a promis- ing avenue of future work in (Pantel 2005). This bottom up algorithm is based on the intuition that x can be disambiguated by retrieving the set of terms that occur in the same relation r with y and then finding the senses of x that are most similar to this set. The assumption is that terms occur- ring in the same relation will tend to have similar meaning. In this paper, we propose a measure of similarity to capture …

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