• Journal of the Korean Society for information Management
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• v.12 no.1
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• pp.87-97
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• 1995

Boolean retrieval systems have been most widely used in the area of information retrieval due to easy implementation and efficient retrieval. Conventional Boolean retrieval systems. however, cannot rank retrieved documents in decreasing order of query-document similarities because they cannot compute similarity coefficients between queries and documents. Extended Boolean models such as fuzzy set. Waller-Kraft, Paice, P-Norm and Infinite-One have been developed to provide the document ranking facility. In extended Boolean models, the formulas evaluating Boolean operators AND and OR are an important component to affect the quality of document ranking. In this paper we present mathematical properties of the formulas, and analyse their effect on retrieval effectiveness. Our analyses show that P-Norm is the most suitable for achieving high retrieval effectiveness.

• 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.10 no.1
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• pp.31-52
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• 1993

This study compared the retrieval effectiveness of conventional Boolean and fuzzy set retrieval strategies through a retrieval experiment. The recall ratio of fuzzy set retrieval is 75%, higher than the 60% of Boolean retrieval. On the other hand, the precision ratio of fuzzy set retrieval is 69%, a little bit lower than the 73% of Boolean retrieval.

• Journal of Information Management
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• v.24 no.3
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• pp.73-100
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• 1993

In attempting to respond to boolean retrieval system's limitations, this paper presents the design of a retrieval system using fuzzy logic. The fuzzy retrieval system introduces the weights of terms in the documents and in the query and makes use of them to determine how much relevant a document is to the given query. After comparing and analyzing the previous researches, an effective model of the fuzzy retrieval system is suggested and the performance of the system is evaluated through actual examples.

• Journal of the Korea Computer Industry Society
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• v.4 no.12
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• pp.901-910
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• 2003

Generally, most commercial retrieval engines adopt boolean query as user's query type. Although boolean query is useful to retrieval engines that need fast retrieval, it is not easy for user to express his demands with boolean operators. So, many researches have been studied for decades about information retrieval systems using natural language query that is convenient for user. To retrieve documents that are suitable for user's demands, they have to express their demands correctly, So, this thesis proposes interactive query process agent using natural language. This agent expresses demands concrete through gradual interaction with user, When users input a natural language Query, this agent analyzes the query and generates boolean query by selecting proper keyword and feedbacks the state of the keyword selected. If the keyword is a synonymy or a polysemy, the agent expands or limits the keyword through interaction with user. It makes user express demands more concrete and improve system performance. So, this agent can improve the precision of Information Retrieval.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.3
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• pp.597-604
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• 2001

In conventional boolean retrieval systems, document ranking is not supported and similarity coefficients cannot be computed between queries and documents. The MMM, Paice and P-norm models have been proposed in the past to support the ranking facility for boolean retrieval systems. They have common properties of interpreting boolean operators softly. In this paper we propose a new soft evaluation method for Information retrieval using query splitting relevance feedback model. We also show through performance comparison that query splitting relevance feedback(QSRF) is more efficient and effective than MMM, Paice and P-norm.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.745-751
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• 2004

In conventional boolean retrieval systems, document ranking is not supported and similarity coefficients cannot be computed between queries and documents. The MMM(Max and Min Model), Paice and P-norm models have been proposed in the past to support the ranking facility for boolean retrieval systems. They have common properties of interpreting boolean operators softly In this paper we propose a new soft evaluation method for web Information retrieval using local context analysis feedback model. We also show through performance comparison that local contort analysis feedback is more efficient and effective than MMM, Paice and P-norm.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.252-257
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• 2001

In conventional boolean retrieval systems, document ranking is not supported and similarity coefficients cannot be computed between queries and documents. The MMM, Paice and P-norm models have been proposed in the past to support the ranking facility for boolean retrieval systems. They have common properties of interpreting boolean operators softly. In this paper we propose a new soft evaluation method for Information retrieval using query splitting relevance feedback model. We also show through performance comparison that query splitting relevance feedback(QSRF) is more efficient and effective than MMM, Paice and P-norm.

• Journal of the Korean Society for information Management
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• v.7 no.1
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• pp.79-95
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• 1990

A retrieval technique was defined as a technique for comparing the document representations. So this study classified retrieval technique in terms of the charactristics of the retrieved set of documents and the representations that are used. The distinction is whether the set of retrieved documents contains only documents whose representations are an exact match with the query, or a partial match with query. For a partial match, the set of retrieved document will include also those that are an exact match with the query. Boolean-logic as one of the exact match retrieval techniques is in current in most of the large operational information retrieval systems despite of its problems and limitatlons. Partial match as an alternative technique has also various problems. Existing information retrieval systems are successful in aSSisting the user whose needs are well- defined (e.g. Boolean-logic), to retrieve relevant documents but it should be successful in providing retrieval assistance to the browser whose information requirements is ill-defined.

• Journal of the Korean Society for information Management
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• v.11 no.1
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• pp.31-56
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• 1994

To evaluate the retrieval effectiveness of the B03lean Request Conversion Mod4 the Relevance Weighted Boolean Model, and the P-NORM Model, the present study has been done with expenmental tests. It is proven that the Relevance Weighted Bdean Model is more effective in precision and the document output ranks than the other ones. The expenmental results indmte a promisii application of relevance mformation and weigh- schemes.