Browse wiki

Jump to: navigation, search
Semantic relatedness metric for Wikipedia concepts based on link analysis and its application to word sense disambiguation
Abstract Wikipedia has grown into a high quality upWikipedia 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.e and produces results with high accuracy.
Added by wikilit team Added on initial load  +
Collected data time dimension Cross-sectional  +
Conclusion We have presented a simple measure of semaWe have presented a simple measure of semantic relatedness, based on the link structure of Wikipedia. We addressed the problem of computing this measure efficiently and have provided heuristics for computing top k related articles. These heuristics achieve high accuracy, but limit the search space drastically and make the approach suitable for practical use in a variety of data intensive systems. We also presented a randomized algorithm to compute the relatedness measure between two articles efficiently and shown that its accuracy in ranking is very close to the true measure. In order to evaluate the quality of the measure, we have presented a simple method for word sense disambiguation, based on the relatedness measure. We evaluated our approach and found it to perform on par with the competing approaches and close to the performance of human experts.close to the performance of human experts.
Data source Experiment responses  + , Wikipedia pages  +
Google scholar url http://scholar.google.com/scholar?ie=UTF-8&q=%22Semantic%2Brelatedness%2Bmetric%2Bfor%2BWikipedia%2Bconcepts%2Bbased%2Bon%2Blink%2Banalysis%2Band%2Bits%2Bapplication%2Bto%2Bword%2Bsense%2Bdisambiguation%22  +
Has author Denis Turdakov + , Pavel Velikhov +
Has domain Computer science +
Has topic Semantic relatedness +
Peer reviewed Yes  +
Publication type Conference paper  +
Published in Spring Young Researcher's Colloquium On Database and Information Systems +
Research design Experiment  +
Research questions We propose to use a simple measure of simiWe 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 disambiguatione the problem of word-sense disambiguation
Revid 10,942  +
Theories Undetermined
Theory type Design and action  +
Title Semantic relatedness metric for Wikipedia concepts based on link analysis and its application to word sense disambiguation
Unit of analysis Article  +
Url http://ftp.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-355/turdakov.pdf  +
Wikipedia coverage Main topic  +
Wikipedia data extraction Live Wikipedia  +
Wikipedia language Not specified  +
Wikipedia page type Article  +
Year 2008  +
Creation dateThis property is a special property in this wiki. 15 March 2012 20:30:09  +
Categories Semantic relatedness  + , Computer science  + , Publications with missing comments  + , Publications  +
Modification dateThis property is a special property in this wiki. 30 January 2014 20:31:21  +
hide properties that link here 
  No properties link to this page.
 

 

Enter the name of the page to start browsing from.