{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T02:31:30Z","timestamp":1772159490146,"version":"3.50.1"},"reference-count":25,"publisher":"Emerald","issue":"5","license":[{"start":{"date-parts":[[2012,9,21]],"date-time":"2012-09-21T00:00:00Z","timestamp":1348185600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,9,21]]},"abstract":"<jats:sec>\n                    <jats:title content-type=\"abstract-heading\">Purpose<\/jats:title>\n                    <jats:p>Semantic similarity measures are very important in many computer\u2010related fields. Previous works on applications such as data integration, query expansion, tag refactoring or text clustering have used some semantic similarity measures in the past. Despite the usefulness of semantic similarity measures in these applications, the problem of measuring the similarity between two text expressions remains a key challenge. This paper aims to address this issue.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title>\n                    <jats:p>In this article, the authors propose an optimization environment to improve existing techniques that use the notion of co\u2010occurrence and the information available on the web to measure similarity between terms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title content-type=\"abstract-heading\">Findings<\/jats:title>\n                    <jats:p>The experimental results using the Miller and Charles and Gracia and Mena benchmark datasets show that the proposed approach is able to outperform classic probabilistic web\u2010based algorithms by a wide margin.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title>\n                    <jats:p>This paper presents two main contributions. The authors propose a novel technique that beats classic probabilistic techniques for measuring semantic similarity between terms. This new technique consists of using not only a search engine for computing web page counts, but a smart combination of several popular web search engines. The approach is evaluated on the Miller and Charles and Gracia and Mena benchmark datasets and compared with existing probabilistic web extraction techniques.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1108\/14684521211276000","type":"journal-article","created":{"date-parts":[[2012,9,20]],"date-time":"2012-09-20T08:26:43Z","timestamp":1348129603000},"page":"724-738","source":"Crossref","is-referenced-by-count":3,"title":["Smart combination of web measures for solving semantic similarity problems"],"prefix":"10.1108","volume":"36","author":[{"given":"Jorge","family":"Martinez\u2010Gil","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9 F.","family":"Aldana\u2010Montes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"key":"key2022021920084589500_b4","doi-asserted-by":"crossref","unstructured":"Bar\u2010Yossef, Z. and Gurevich, M. (2006), \u201cRandom sampling from a search engine's index\u201d, Proceedings of the World Wide Web Conference, ACM Press, New York, NY, pp. 367\u201076.","DOI":"10.1145\/1135777.1135833"},{"key":"key2022021920084589500_b1","unstructured":"Bollegala, D., Matsuo, Y. and Ishizuka, M. (2007), \u201cMeasuring semantic similarity between words using web search engines\u201d, Proceedings of the World Wide Web Conference, ACM Press, New York, NY, pp. 757\u201066."},{"key":"key2022021920084589500_b2","doi-asserted-by":"crossref","unstructured":"Bollegala, D., Matsuo, Y. and Ishizuka, M. (2009), \u201cMeasuring the similarity between implicit semantic relations from the web\u201d, Proceedings of the World Wide Web Conference, ACM Press, New York, NY, pp. 651\u201060.","DOI":"10.1145\/1498759.1498815"},{"key":"key2022021920084589500_b3","doi-asserted-by":"crossref","unstructured":"Bollegala, D., Honma, T., Matsuo, Y. and Ishizuka, M. 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