Semantic relatedness metric for Wikipedia concepts based on link analysis and its application to word sense disambiguation
|Semantic relatedness metric for Wikipedia concepts based on link analysis and its application to word sense disambiguation|
|Author(s)||Turdakov D., Velikhov P.|
|Published in||CEUR Workshop Proceedings|
|Keyword(s)||Unknown (Extra: High quality, Knowledge base, Knowledge-based applications, Link analysis, Scale free networks, Semantic information, Semantic relatedness, Similarity measure, Statistical properties, Wikipedia, Word Sense Disambiguation, Heuristic methods, Knowledge based systems, Semantics, Websites, Natural language processing systems)|
|Article||BASE, CiteSeerX, Google Scholar|
|Web||Ask, Bing, Google (PDF), Yahoo!|
|Download and mirrors|
|Local copy||Not available|
|Remote mirror(s)||Not available|
|Export and share|
|BibTeX, CSV, RDF, JSON|
|Browse properties · List of conference papers|
Semantic relatedness metric for Wikipedia concepts based on link analysis and its application to word sense disambiguation is a 2008 conference paper written in English by Turdakov D., Velikhov P. and published in CEUR Workshop Proceedings.
Wikipedia has grown into a high quality up-todate knowledge base and can enable many knowledge-based applications, which rely on semantic information. One of the most general and quite powerful semantic tools is a measure of semantic relatedness between concepts. Moreover, the ability to efficiently produce a list of ranked similar concepts for a given concept is very important for a wide range of applications. We propose to use a simple measure of similarity between Wikipedia concepts, based on Dice's measure, and provide very efficient heuristic methods to compute top k ranking results. Furthermore, since our heuristics are based on statistical properties of scale-free networks, we show that these heuristics are applicable to other complex ontologies. Finally, in order to evaluate the measure, we have used it to solve the problem of word-sense disambiguation. Our approach to word sense disambiguation is based solely on the similarity measure and produces results with high accuracy.
- This section requires expansion. Please, help!
Cited byThis publication has 1 citations. Only those publications available in WikiPapers are shown here:
Cited 3 time(s)