A knowledge net or deepKnowNet (KN), is a new fully automatic method for building highly dense and accurate knowledge bases from existing semantic resources. Basically, the method applyies a knowledge-based Word Sense Disambiguation algorithm to assign the most appropriate WordNet sense to large sets of topically related words acquired from the web, named TSWEB. This Word Sense Disambiguation algorithm is the page-rank algorithm encoded in UKB. This new method improves by automatic means the current content of WordNet by creating large volumes of new and accurate semantic relations between synsets. KnowNet was our first attempt towards the acquisition of large volumes of semantic relations. However, KnowNet had some limitations that have been overcomed with deepKnowNet. deepKnowNet uses the first hundred words of all Topic Signatures from the web (TSWEB) to be desambiguated. In this case, the method highlights the most relevant word senses of each Topic Signature and filter out the ones that are not so related to the topic. In fact, the knowledge it contains outperform any other resource when is empirically evaluated in a common framework
- deepKnowNet-80: [.gz]
- deepKnowNet-85: [.gz]
- deepKnowNet-90: [.gz]
- deepKnowNet-95: [.gz]
- deepKnowNet-99: [.gz]
These packages are distributed under Attribution 3.0 Unported (CC BY 3.0) license. You can find it at http://creativecommons.org/licenses/by/3.0.
Cuadros M., Padró L. and Rigau G. Highlighting relevant concepts from Topic Signatures. 8th international conference on Language Resources and Evaluation (LREC'12). Istambul, Turkey. 2012.