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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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