• Proceedings of the Korean Society for Information Management Conference
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• 1994.12a
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• pp.15-18
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• 1994

본 연구에서는 불논리검색의 단점을 보완하기 위한 가장 강력한 검색 모형인 퍼지검색과 확률검색의 효율을 불논리검색과 상호비교하였다. 실험데이터로 정보학 분야의 한국어 test collection인 KT Test Set을 이용하였고 색인어와 색인어의 문헌내 출현빈도를 바탕으로 퍼지시소러스를 생성하여 시소러스의 NT, BT로 탐색식을 확장한 다음 각각에 대해 3가지 검색을 행하고 검색효율을 평균재현율과 평균정확률로 측정하였다. 실험결과 검색효율은 재현율에서는 확률검색, 불논리검색, 퍼지검색 순으로. 정확률에서는 퍼지검색, 확률검색, 불논리검색 순으로 나타났다.

• Journal of the Korean Society for information Management
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• v.15 no.1
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• pp.129-148
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• 1998

The objective of this study is to develop a useful relevance feedback retrieval technique that can be applied to the current Boolean retrieval system. A feedback retrieval technique based on user model is recommended here to achieve this objective. To prove the usefulness of this feedback retrieval technique, two enhanced Boolean retrieval models including DNF model and P-norm model were evaluated first through retrieval effectiveness experiments. After selecting DNF model as the retrieval model, two feedback retrieval experiments were performed using initial and extended user models. It is proved that the feedback retrieval based on user model can greatly enhance the effectiveness of a Boolean retrieval system with a small modification.

• Journal of the Korean Society for information Management
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• v.18 no.2
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• pp.7-30
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• 2001

The characteristics of the short document representatives and short queries of OPAC systems need the different ranking algorithms from IR systems. This study tested and analyzed the effectiveness of four sorting schemes and four ranking algorithms and the six effectiveness measurements for the ranked Boolean OPAC systems. The sorting by publication year was better but without significant difference. The cover density ranking was significantly better than the frequency-based ranking of the Fuzzy or DNF models. The simple effectiveness measurement based on the average rank of relevant documents retrieved was as good as the others and better than the precision P.

• Journal of the Korean Society for information Management
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• v.20 no.4 s.50
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• pp.23-48
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• 2003

In this study an information filtering system was implemented and a series of relevance feedback experiments were conducted using the system. For the relevance feedback, the original queries were searched against the database and the results were reviewed by the researchers. Based on users' online relevance judgements a pair of 17 refined queries were generated using two methods called 'co-occurrence exclusion method' and 'lower frequencies exclusion method,' In order to generate them, the original queries, the descriptors and category codes appeared in either relevant or irrelevant document sets were applied as elements. Users' relevance judgments on the search results of the refined queries were compared and analyzed against those of the original queries.