• Journal of Korean Library and Information Science Society
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• v.39 no.1
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• pp.265-278
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• 2008

This study aims to perform an evaluation of unified search services provided by major Korean search portals, Naver, Daum, Yahoo-Korea, and Empas. These unified search services are evaluated in terms of the relevance of search results. In conducting this study, real queries that real users submitted were used. This study also utilized click logs that consist of documents users clicked and viewed. The results of this study can be implemented to the development and improvement of portal's unified search services. Users can refer to the results of this study in choosing unified search services from search portals.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.35-40
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• 2022

In this paper, we propose a method of constructing clinical knowledge with clinical concept relations and predicting diseases based on a deep learning model to support clinical decision-making. Clinical terms in UMLS(Unified Medical Language System) and cancer-related medical knowledge are classified into five categories. Medical related documents in Wikipedia are extracted using the classified clinical terms. Clinical concept relations are established by matching the extracted medical related documents with the extracted clinical terms. After deep learning using clinical knowledge, a disease is predicted based on medical terms expressed in a query. Thereafter, medical terms related to the predicted disease are selected as an extended query for clinical document retrieval. To validate our method, we have experimented on TREC Clinical Decision Support (CDS) and TREC Precision Medicine (PM) test collections.