人工知能学会論文誌
Online ISSN : 1346-8030
Print ISSN : 1346-0714
原著論文
動向に関する問いを対象とした検索エンジンの提案
高間 康史加藤 優桑折 章吾石川 博
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2015 年 30 巻 1 号 p. 138-147

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This paper proposes a search engine that is designed for answering trend-related queries. Recently, people can gather huge amount of information by using existing Web search engines. However, there is a significant difference between function provided by existing search engines and users' information needs. That is, a function of existing Web search engines is just to find Web pages containing keywords specified as a query. When we want to find some information from the Web, we have to break our information need into a series of queries by ourselves. As this process is burdensome for novices and even for experienced users, advanced search engine that can solve this problem should be realized. This paper focuses on the task of answering trend-related queries. By focusing on a specific task, proposed search engine can provide advanced search functions compared with existing Web search engines. At the same time, as this task is supposed to be useful in various domains, the proposed search engine can inherit one of important characteristics of existing search engines that it can be widely used regardless of domains. Prototype search engine collects various trend-information from the Web and provides three basic functions: a function for searching time periods when a query item had a characteristic trend, that for searching items having characteristic trend during query time period, and that for searching items having similar trend with query item. These functions are derived from analysis results of search intention using existing search engines, and users cans satisfy their various kinds of information needs related with trend information by combining those functions. The effectiveness of the prototype search engine is shown through experiments with test participants. Experimental results show that test participants can issue correct queries efficiently for given information needs. It is also shown that they can break their own information needs into a series of queries corresponding to various search intentions.

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© 人工知能学会 2015
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