KnowNet: Building a Large Net of Knowledge from the Web

Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), pages 161–168 Manchester, August 2008 KnowNet: Building a Large Net of Knowledge from the Web Montse Cuadros TALP Research Center, UPC Barcelona, Spain cuadros@lsi.upc.edu German Rigau IXA NLP Group, UPV/EHU Donostia, Spain german.rigau@ehu.es Abstract This paper presents a new fully auto- matic method for building highly dense and accurate knowledge bases from ex- isting semantic resources. Basically, the method uses a wide-coverage and accu- rate knowledge-based Word Sense Dis- ambiguation algorithm to assign the most appropriate senses to large sets of topi- cally related words acquired from the web. KnowNet, the resulting knowledge-base which connects large sets of semantically- related concepts is a major step towards the autonomous acquisition of knowledge from raw corpora. In fact, KnowNet is sev- eral times larger than any available knowl- edge resource encoding relations between synsets, and the knowledge KnowNet con- tains outperform any other resource when is empirically evaluated in a common framework. 1 Introduction Using large-scale knowledge bases, such as Word- Net (Fellbaum, 1998), has become a usual, of- ten necessary, practice for most current Natural Language Processing (NLP) systems. Even now, building large and rich enough knowledge bases for broad–coverage semantic processing takes a great deal of expensive manual effort involving large research groups during long periods of de- velopment. In fact, hundreds of person-years have been invested in the development of wordnets for various languages (Vossen, 1998). For example, in c⃝ 2008. Licensed under the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported li- cense (http://creativecommons.org/licenses/by-nc-sa/3.0/). Some rights reserved. more than ten years of manual construction (from 1995 to 2006, that is from version 1.5 to 3.0), WordNet grew from 103,445 to 235,402 semantic relations1. But this data does not seem to be rich enough to support advanced concept-based NLP applications directly. It seems that applications will not scale up to work in open domains without more detailed and rich general-purpose (and also domain-specific) semantic knowledge built by au- tomatic means. Obviously, this fact has severely hampered the state-of-the-art of advanced NLP ap- plications. However, the Princeton WordNet (WN) is by far the most widely-used knowledge base (Fellbaum, 1998). In fact, WordNet is being used world-wide for anchoring different types of semantic knowl- edge including wordnets for languages other than English (Atserias et al., 2004), domain knowledge (Magnini and Cavagli`a, 2000) or ontologies like SUMO (Niles and Pease, 2001) or the EuroWord- Net Top Concept Ontology ( ´Alvez et al., 2008). It contains manually coded information about En- glish nouns, verbs, adjectives and adverbs and is organized around the notion of a synset. A synset is a set of words with the same part-of-speech that can be interchanged in a certain context. For ex- ample, <party, political party> form a synset be- cause they can be used to refer to the same concept. A synset is often further described by a gloss, in this case: ”an organization to gain political power” and by explicit semantic relations to other synsets. Fortunately, during …

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