• 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.

• 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 KIISE:Software and Applications
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• v.26 no.8
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• pp.964-973
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• 1999

본 논문에서는 퍼지 트리플을 사용하는 내용 기반 이미지 검색 방법을 제안한다. 이미지 내 객체들 사이의 공간 관계는 내용 기반 이미지 검색을 위해 사용되는 주요한 속성들 중의 하나이다. 그러나, 기존의 트리플을 이용한 이미지 검색 시스템들은 개념 기반 검색 방법을 지원하지 못하고, 방향들 사이의 근사 정합을 처리하지 못하는 문제점을 가지고 있다. 이 문제를 해결하기 위하여 본 논문에서는 개념 기반 정합과 근사 정합을 지원하는 퍼지 트리플을 이용한 이미지 검색 방법을 제안한다. 개념 기반 정합을 위해서는 퍼지 소속성 집합으로 이루어진 시소러스가 사용되며, 근사 정합을 위해서는 방향들 사이의 관계를 정량화 하기 위한 k-weight 함수가 각각 이용된다. 이 두 가지 정합은 퍼지 트리플 간의 퍼지 정합을 통하여 균일하게 지원될 수 있다. 본 논문에서는 또한, 개념 기반 정합과 근사 정합에 대한 검색 효과를 정량적으로 평가하는 작업을 수행한다. Abstract This paper proposes an inexact and a concept-based image match technique based on fuzzy triples. The most general method adopted to index and retrieve images based on this spatial structure may be triple framework. However, there are two significant drawbacks in this framework; one is that it can not support a concept-based image retrieval and the other is that it fails to deal with an inexact match among directions. To compensate these problems, we develope an image retrieval technique based on fuzzy triples to make the inexact and concept-based match possible. For the concept-based match, we employ a set of fuzzy membership functions structured like a thesaurus, whereas for the inexact match, we introduce k-weight functions to quantify the similarity between directions. In fuzzy triples, the two facilities are uniformly supported by fuzzy matching. In addition, we analyze the retrieval effectiveness of our framework regarding the degree of the conceptual matching and the inexact matching.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.3
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• pp.171-177
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• 2011

The decision making process is based on accurate and timely available information. To obtain precise information from the internet is becoming more difficult due to the continuous increase in vagueness and uncertainty from online information resources. This also poses a problem for blind people who desire the full use from online resources available to other users for decision making in their daily life. Ontology is considered as one of the emerging technology of knowledge representation and information sharing today. Fuzzy logic is a very popular technique of artificial intelligence which deals with imprecision and uncertainty. The classical ontology can deal ideally with crisp data but cannot give sufficient support to handle the imprecise data or information. In this paper, we incorporate fuzzy logic with heavyweight ontology to solve the imprecise information extraction problem from heterogeneous misty sources. Fuzzy ontology consists of fuzzy rules, fuzzy classes and their properties with axioms. We use Fuzzy OWL plug-in of Protege to model the fuzzy ontology. A prototype is developed which is based on OWL-2 (Web Ontology Language-2), PAL (Protege Axiom Language), and fuzzy logic in order to examine the effectiveness of the proposed system.

• 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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.1
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• pp.26-32
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• 2002

The objectives of this paper are twofold. First is to introduce a model for representing documents with semantics relatedness using rough sets but with tolerance relations instead of equivalence relations (TRSM). Second is to introduce two document hierarchical and nonhierarchical clustering algorithms based on this model and TRSM cluster-based information retrieval using these two algorithms. The experimental results show that TRSM offers an alterative approach to text clustering and information retrieval.

• 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.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.894-897
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• 2000

In this paper, we present a content-based image retrieval method that is less sensitive to some rotations and translations of an image by using the fuzzy region segmentation. The algorithm retrieves similar images from a database using the two features of color and color spatial information. To index images, we use the average coordinates of color distribution to obtain the spatial information of each segmented region. Furthermore, we also propose the alternative to the ripple phenomenon, which is occurred in the conventional fuzzy region segmentation algorithm.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.2
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• pp.167-172
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• 2012

In biology the advent of the high-throughput technology for sequencing, probing, or screening has produced huge volume of data which could not be manually handled. Biologists have resorted to software tools in order to effectively handle them. This paper introduces a bioinformatics tool to help biologists find potentially interesting pathway maps from a transcriptome data set in which the expression levels of genes are described for both case and control samples. The tool accepts a transcriptome data set, and then selects and categorizes some of genes into four classes using a fuzzy filtering technique where classes are defined by membership functions. It collects and edits the pathway maps related to those selected genes without analyst' intervention. It invokes a sequence of web service functions from KEGG, which an online pathway database system, in order to retrieve related information, locate pathway maps, and manipulate them. It maintains all retrieved pathway maps in a local database and presents them to the analysts with graphical user interface. The tool has been successfully used in identifying target genes for further analysis in transcriptome study of human cytomegalovirous. The tool is very helpful in that it can considerably save analysts' time and efforts by collecting and presenting the pathway maps that contain some interesting genes, once a transcriptome data set is just given.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.552-555
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• 1998

In dealing with representing knowledge under uncertainty there is a sustain tendency to increase flexibility in order to avoid problems of inconsistency in the knowledge. Many knowledge systems(information retrieval systems, expert system) include hybrid representation models. Funny retrieval systems appear as a complement or as an enrichment of this models. In this paper, we describe dynamic rule modification through situation assessment for uncertainty management.