A knowledge net or KnowNet (KN), is an extensible, large and accurate knowledge base, which has been derived by semantically disambiguating small portions of the Topic Signatures acquired from the web (Martínez et al. 08). Basically, the method uses a robust and accurate knowledge-based Word Sense Disambiguation algorithm to assign the most appropriate senses to the topic words associated to a particular synset. The resulting knowledge-base which connects large sets of topically-related concepts is a major step towards the autonomous acquisition of knowledge from raw text.
Varying from five to twenty the number of processed words from each Topic Signature, we created automatically four different KnowNet versions with millions of new semantic relations between synsets. In fact, KnowNet is several times larger than WordNet, and when evaluated empirically in a common framework, the knowledge it contains outperforms any other semantic resource. KnowNet is several times larger than any available knowledge resource encoding relations between synsets, and the knowledge KnowNet contains outperform any other resource when is empirically evaluated in a common framework.
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License
This package is distributed under Attribution 3.0 Unported (CC BY 3.0) license. You can find it at http://creativecommons.org/licenses/by/3.0.
Publications
Cuadros M. and Rigau G. Bases de Conocimiento Multilíngües para el Procesamiento Semántico a Gran Escala. Procesamiento del Lenguaje Natural. (SEPLN). Vol. 40, 35-42. ISSN 1135-5948. 2008
Cuadros M. and Rigau G. KnowNet: building a large net of knowledge from the web. The 22nd International Conference on Computational Linguistics (Coling'08), UK, Manchester. 2008.
Cuadros M. and Rigau G. Multilingual Evaluation of KnowNet. Proceedings of the 24th edition of the conference of the Spanish Society for Natural Language Processing (Sepln'08), Spain, Madrid. 2008.
Cuadros M. and Rigau G. KnowNet: a proposal for building knowledge bases from the web. First Symposium on Semantics in Systems for Text Processing, STEP'08. Venice, Italy. 2008.
References
Martínez D., Agirre E. and Lopez de la Calle O. On the use of automatically acquired examples for all-nouns WSD. Journal of Artificial Intelligence Research, 79-107, vol. 33 ISSN 1076-957. 2008