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Automatic mapping of user tags to Wikipedia concepts: the case of a Q & A website - StackOverflow

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dc.contributor.author Joorabchi, Arash
dc.contributor.author English, Michael
dc.contributor.author Mahdi, Abdulhussain E.
dc.date.accessioned 2015-10-05T08:29:46Z
dc.date.available 2015-10-05T08:29:46Z
dc.date.issued 2015
dc.identifier.citation Joorabchi, A; Michael, E; Mahdi, AE (2015) 'Automatic Mapping of User Tags to Wikipedia Concepts: the Case of Q & A website - StackOverflow'. Journal Of Information Science, 41 (5):570-583. en_US
dc.identifier.uri http://hdl.handle.net/10344/4674
dc.description peer-reviewed en_US
dc.description.abstract The uncontrolled nature of user-assigned tags makes them prone to various inconsistencies caused by spelling variations, synonyms, acronyms, and hyponyms. These inconsistencies in turn lead to some of the common problems associated with the use of folksonomies such as the tag explosion phenomenon. Mapping user tags to their corresponding Wikipedia articles, as well-formed concepts, offers multi-facet benefits to the process of subject metadata generation and management in a wide range of online environments. These include normalization of inconsistencies, elimination of personal tags, and improvement of the interchangeability of existing subject metadata. In this article, we propose a machine learning-based method capable of automatic mapping of user tags to their equivalent Wikipedia concepts. We have demonstrated the application of the proposed method and evaluated its performance using the currently most popular computer programming Q&A website, StackOverflow.com, as our test platform. Currently, around 20 million posts in StackOverflow are annotated with about 37,000 unique user tags, from which we have chosen a subset of 1,256 tags to evaluate the accuracy performance of our proposed mapping method. We have evaluated the performance of our method using the standard information retrieval measures of precision, recall, and F1. Depending on the machine learning-based classification algorithm used as part of the mapping process, F1 scores as high as 99.6% were achieved. en_US
dc.language.iso eng en_US
dc.publisher SAGE Publications en_US
dc.relation.ispartofseries Journal of Information Science;41 (5), pp. 570-583
dc.relation.uri http://dx.doi.org/10.1177/0165551515586669
dc.subject semantic mapping en_US
dc.subject subject metadata en_US
dc.subject user tags en_US
dc.subject Wikipedia en_US
dc.title Automatic mapping of user tags to Wikipedia concepts: the case of a Q & A website - StackOverflow en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.date.updated 2015-10-02T16:18:56Z
dc.description.version ACCEPTED
dc.identifier.doi 10.1177/0165551515586669
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US
dc.internal.rssid 1598609
dc.internal.copyrightchecked Yes
dc.identifier.journaltitle Journal Of Information Science
dc.description.status peer-reviewed


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