• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.240-245
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• 1998

Fuzzy information retrieval which uses the concept of fuzzy relation is able to retrieve documents in the way based on not morphology but semantics, dissimilar to traditional information retrieval theories. Fuzzy information retrieval logically consists of three sets : the set of documents, the set of terms and the set of queries. It maintains a fuzzy relational matrix which describes the relationship between documents and terms and creates a thesaurus with fuzzy relational product. It also provides the user with documents which are relevant to his query. However, there are some problems on building a thesaurus with fuzzy relational product such that it has big time complexity and it uses fuzzy values to be processed with flating-point. Actually, fuzzy values have to be expressed and processed with floating-point. However, floating-point operations have complex logics and make the system be slow. If it is possible to exchange fuzzy values with binary values, we could expect sp eding up building the thesaurus. In addition, binary value expressions require just a bit of memory space, but floating -point expression needs couple of bytes. In this study, we suggest a new method of building a thesaurus, which accelerates the operation of the system by pre-applying an ${\alpha}$-cut. The experiments show the improvement of performance and reliability of the system.

• Proceedings of the Korea Contents Association Conference
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• 2003.05a
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• pp.411-415
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• 2003

This paper compared fuzzy thesaurus through query extension with existent thesaurus and direct matching retrieval etc. Performance of fuzzy thesaurus is evaluated by the recall and precision through simulation. An experiment did through assessment that use critical value, query extension, and reusability satisfaction. As a result of the experiment, we knew component retrieval efficiency of fuzzy thesaurus excels.

• The KIPS Transactions:PartD
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• v.10D no.5
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• pp.753-762
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• 2003

Many Methodologies have proposed for component retrieval. Among them, thesaurus concept has introduced for similar component retrieval. This paper classified classes by concept according to inheritance relation for efficient retrieval of component, and applied fuzzy logic to thesaurus method and constructed object-oriented thesaurus. Proposed method could express category between concepts automatically, and calculate fuzzy degree between classes by comparing matching and mismatching degree to each class and category and construct thesaurus. Component retrieval is that using classes of component, candidate components are retrieved according to priority order using fuzzy similarity. Also, we improved retrieval performance by thesaurus greatly, setting critical of most suitable through simulation.

• The Journal of the Korea Contents Association
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• v.4 no.1
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• pp.76-82
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• 2004

In this paper, we used query evaluation method through thesaurus for retrieving Components having concept relation with any classes in a query. Queries are presented in boolean and expanded by similar table. Query expansion by thesaurus is the solution of the term mismatching and it enhanced precision and recall of the components retrieval. For efficiency evaluation of query expansion, we defined most critical value through a simulation and compared precision and recall each other.

• Journal of KIISE:Software and Applications
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• v.27 no.2
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• pp.168-179
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• 2000

In reusable component retrieval systems with facet-based schemes, facet queries are generally used for representing the characteristics of components relevant to users. This paper proposes an expanded facet query equipped with an object-based thesaurus to precisely formulate user's intents. To evaluate the query, a component retrieval system is also designed and implemented. For exactly retrieving the components, user's query should include relevant facet values capable of fully specifying their characteristics. However, simply listing a series of facet values directly inputted by users, conventional queries fails to precisely represent user's intents. Our query, called expanded facet query, employs fuzzy boolean operators and object-based thesaurus; the former logically expresses the fuzzy connectives between facet queries and required components, whereas the latter helps users appropriately select the specific facet values into the query. A thesaurus query is provided to recommend the relevant facet values with their fuzzy degrees from the thesaurus as well. Furthermore, our retrieval system can automatically formulate queries with the recommended facet values, if necessary.

• Journal of KIISE:Software and Applications
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• v.29 no.4
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• pp.278-288
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• 2002

This paper presents a design and implementation of fuzzy retrieval system that could support an associated learning in problem banks. It tries to retrieve some of the problems conceptually related to specific semantics described by user's queries. In particular, the problem retrieval system employs a fuzzy thesaurus which represents relationships between domain dependent vocabularies as fuzzy degrees. It would keep track of characteristics of the associated learning, which should guarantee high recall and acceptable precision for retrieval effectiveness. That is, since the thesaurus could make a vocabulary mismatch problem resolved among query terms and document index terms, this retrieval system could take a chance to effectively support user's associated teaming. Finally, we have evaluated whether the fuzzy retrieval system is appropriate for the associated teaming or not, by means of its precision and recall rate point of view.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.3
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• pp.218-227
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• 2000

In this paper, we present a new fuzzy information retrieval method based on thesaurus. In the proposed method th thesaurus is represented by a fuzzy linguistic matrix, where the elements in fuzzy linguistic matrix represent a qualitative linguistic values between terms. In the fuzzy linguistic matrix, there are three kinds of fuzzy relationships between terms, i.e., similar relation, hierarchical relation, and associative relation. The implicit fuzzy relationships between terms are inferred by the transitive closure of the fuzzy linguistic matrix based on fuzzy theory. And the proposed method has the capability to deal with a qualitative linguistic weights in a query and in indexing of information items to reflect qualitative measure of human based on vague and uncertain decisions rather than a quantitiative measure. Therefore the proposed method is more flexible than the ones presented in papers[1-3]. Moreover our method is more effectual of time than the ones presented in papers[1-3] because we use a fuzzy linguistic matrix and AON (Associate Ordinary Number) values in query evaluation process. As a result, the proposed method allows the users to perform fuzzy queries in a more flexible and more intelligent manner.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.3
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• pp.303-317
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• 2002

In this paper, we design and implement E-COIRS enabling users to query with concepts and image features used for further refining the concepts. For example, E-COIRS supports the query "retrieve images containing black home appliance to north of reception set. "The query includes two types of concepts: IS-A and composite. "home appliance"is an IS-A concept, and "reception set" is a composite concept. For evaluating such a query. E-COIRS includes three important components: a visual image indexer, thesauri and a query processor. Each pair of objects in an mage captured by the visual image indexer is converted into a triple. The triple consists of the two object identifiers (oids) and their spatial relationship. All the features of an object is referenced by its old. A composite concept is detected by the triple thesaurus and IS-A concept is recolonized by the fuzzy term thesaurus. The query processor obtains an image set by matching each triple in a user with an inverted file and CS-Tree. To support efficient storage use and fast retrieval on high-dimensional feature vectors, E-COIRS uses Cell-based Signature tree(CS-Tree). E-COIRS is a more advanced content-based image retrieval system than other systems which support only concepts or image features.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.1
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• pp.53-62
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• 2008

Cross-lingual text retrieval(CLTR) is the information retrieval in which a user tries to search a set of documents written in one language for a query another language. This thesis proposes a CLTR system based on fuzzy multilingual thesaurus to handle a partial matching between terms of two different languages. The proposed CLTR system uses a fuzzy term matrix defined in our thesis to perform the information retrieval effectively. In the defined fuzzy term matrix, all relation degrees between terms are inferred from using the transitive closure algorithm to reflect all implicit links between terms into processing of the information retrieval. With this framework, the CLTR system proposed in our thesis enhances the retrieval effectiveness because it is able to emulate a human expert's decision making well in CLTR.

• Journal of the Korean Society for information Management
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• v.10 no.2
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• pp.3-22
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• 1993

In recent years various document ranking methods such as Relevance. R-Distance and K-Distance have been developed wh~ch can be used in thesaurus-based boolean retrieval systems. They give high quality document rankings in many cases by using term dependence lnformatlon from a thesaurus. However, they suffer from several problems resulting from inefficient and Ineffective evaluation of boolean operators AND. OR and NOT. In this paper we propose new thesaurus-based document ranking methods called KB-FSM and KB-EBM by exploitmg the enhanced fuzzy set model and the extended boolean model. The proposed methods overcome the problems of the previous methods and use term dependencies from a thesaurs effectively. We also show through performance comparison that KB-FSM and KBEBM provide higher retrieval effectiveness than Relevance. R-D~stance and K-Distance.