%0 Conference Paper %1 milne2008effective %A Milne, David %A Witten, Ian H. %B In Proceedings of the Conference on Artificial Intelligence %D 2008 %K hyperlinks semantic\_similarity seminar ss2015 talk wikipedia %T An Effective, Low-cost Measure of Semantic Relatedness Obtained from Wikipedia Links %X This paper describes a new technique for obtaining measures of semantic relatedness. Like other recent approaches, it uses Wikipedia to provide structured world knowledge about the terms of interest. Our approach is unique in that it does so using the hyperlink structure of Wikipedia rather than its category hierarchy or textual content. Evaluation with manually defined measures of semantic relatedness reveals this to be an effective compromise between the ease of computation of the former approach and the accuracy of the latter.

@inproceedings{milne2008effective, abstract = {{This paper describes a new technique for obtaining measures of semantic relatedness. Like other recent approaches, it uses Wikipedia to provide structured world knowledge about the terms of interest. Our approach is unique in that it does so using the hyperlink structure of Wikipedia rather than its category hierarchy or textual content. Evaluation with manually defined measures of semantic relatedness reveals this to be an effective compromise between the ease of computation of the former approach and the accuracy of the latter.}}, added-at = {2015-06-17T22:12:42.000+0200}, author = {Milne, David and Witten, Ian H.}, biburl = {https://www.bibsonomy.org/bibtex/2df09e53194bf968663b03f1778e62211/magnuslechner}, booktitle = {In Proceedings of the Conference on Artificial Intelligence}, citeulike-article-id = {4956624}, citeulike-linkout-0 = {http://www.aaai.org/Papers/Workshops/2008/WS-08-15/WS08-15-005.pdf}, comment = {Authors propose link-based method for similarity computation in Wikipedia. It is based on the intersection and number of incoming links for two articles. Measure shows good performance for two similarity benchmarks. Resolving ambiguity is also discussed. It is based on anchor links which results into up to 26 candidates per article. The following approaches are used: most common, most related, highest (common+related), sequential and anchor-based (check for two words appearing in anchor).}, interhash = {f8b0b3ba8f4a1c20e3d5d732a221f102}, intrahash = {df09e53194bf968663b03f1778e62211}, keywords = {hyperlinks semantic\_similarity seminar ss2015 talk wikipedia}, posted-at = {2010-04-20 11:06:23}, priority = {0}, series = {AAAI '08}, timestamp = {2015-06-17T22:12:42.000+0200}, title = {{An Effective, Low-cost Measure of Semantic Relatedness Obtained from Wikipedia Links}}, year = 2008 }