%0 Journal Article %1 citeulike:1280932 %A Yoshida, Tetsuya %A Shinkai, Daiki %A Nishida, Shogo %C Amsterdam, The Netherlands, The Netherlands %D 2005 %I IOS Press %J Web Intelligence and Agent Systems %K adaptive-search %N 3 %P 171--182 %T A document retrieval support system with term relationship %U http://portal.acm.org/citation.cfm?id=1239798.1239801 %V 3 %X Recent development of the network such as the Internet makes it easy to retrieve documents over the World Wide Web (WWW). Document retrieval with search engines is convenient, especially to retrieve documents for a domain with which a user is not so familiar, since the retrieved documents can be used to get a gross overview of the domain as a preliminary investigation. However, when inappropriate query terms are used, it often results in retrieving too many documents and the retrieved documents have to be scanned by the user him/herself to find out truly valuable ones. This paper proposes a document retrieval support system to reduce such "dual" retrieval by suggesting terms based on the relationship of relevant terms to the specified query terms. Semantic relations of terms are dealt with using a thesaurus and relevant terms for the terms are organized into a tree structure based on the relations defined in the thesaurus. Since the value of retrieved documents is intrinsically user-dependent, even when the same query terms are used, appropriate terms to facilitate document retrieval can vary depending on users. To realize personalization in term suggestion, the relationship of terms for the user is learned from a user-supplied case so that personalized terms can be suggested. A prototype system has been implemented and it is evaluated through experiments. The results of experiments are encouraging and show that it is worth following this path, especially for supporting users who are not yet familiar with the target domain.

@article{citeulike:1280932, abstract = {Recent development of the network such as the Internet makes it easy to retrieve documents over the World Wide Web (WWW). Document retrieval with search engines is convenient, especially to retrieve documents for a domain with which a user is not so familiar, since the retrieved documents can be used to get a gross overview of the domain as a preliminary investigation. However, when inappropriate query terms are used, it often results in retrieving too many documents and the retrieved documents have to be scanned by the user him/herself to find out truly valuable ones. This paper proposes a document retrieval support system to reduce such "dual" retrieval by suggesting terms based on the relationship of relevant terms to the specified query terms. Semantic relations of terms are dealt with using a thesaurus and relevant terms for the terms are organized into a tree structure based on the relations defined in the thesaurus. Since the value of retrieved documents is intrinsically user-dependent, even when the same query terms are used, appropriate terms to facilitate document retrieval can vary depending on users. To realize personalization in term suggestion, the relationship of terms for the user is learned from a user-supplied case so that personalized terms can be suggested. A prototype system has been implemented and it is evaluated through experiments. The results of experiments are encouraging and show that it is worth following this path, especially for supporting users who are not yet familiar with the target domain.}, added-at = {2009-07-01T11:12:30.000+0200}, address = {Amsterdam, The Netherlands, The Netherlands}, author = {Yoshida, Tetsuya and Shinkai, Daiki and Nishida, Shogo}, biburl = {https://www.bibsonomy.org/bibtex/2960d4cd5bcdc08d37bd29fe24f251029/brusilovsky}, citeulike-article-id = {1280932}, interhash = {4cab43d3e602f4e47ba06113b8a98f0d}, intrahash = {960d4cd5bcdc08d37bd29fe24f251029}, issn = {1570-1263}, journal = {Web Intelligence and Agent Systems}, keywords = {adaptive-search}, month = {July}, number = 3, pages = {171--182}, posted-at = {2007-05-06 19:43:53}, priority = {2}, publisher = {IOS Press}, timestamp = {2009-07-01T11:12:38.000+0200}, title = {A document retrieval support system with term relationship}, url = {http://portal.acm.org/citation.cfm?id=1239798.1239801}, volume = 3, year = 2005 }