%0 Generic %1 Turdakov_semanticrelatedness %A Turdakov, Denis %A Velikhov, Pavel %D 2008 %K wikisempaths %T Semantic Relatedness Metric for Wikipedia Concepts Based on Link Analysis and its Application to Word Sense Disambiguation %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.143.864 %X 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. 1

@misc{Turdakov_semanticrelatedness, abstract = {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. 1}, added-at = {2012-08-22T15:06:02.000+0200}, author = {Turdakov, Denis and Velikhov, Pavel}, biburl = {https://www.bibsonomy.org/bibtex/24d199b67f100e1ae212740f6ea2250d3/psinger}, description = {CiteSeerX — Semantic Relatedness Metric for Wikipedia Concepts Based on Link Analysis and its Application to Word Sense Disambiguation}, interhash = {dfda61750a570bc67fa67e2b05464138}, intrahash = {4d199b67f100e1ae212740f6ea2250d3}, keywords = {wikisempaths}, timestamp = {2012-08-22T15:06:02.000+0200}, title = {Semantic Relatedness Metric for Wikipedia Concepts Based on Link Analysis and its Application to Word Sense Disambiguation}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.143.864}, year = 2008 }