Browsing by Author "Horng, YJ"

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A knowledge-based method for fuzzy query processing for document retrieval
(1997-01-01)
Chen, SM
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Hsiao, WH
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Horng, YJ
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交大名義發表
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資訊工程學系
This paper presents a knowledge-based method for processing fuzzy queries and weighted-fuzzy, queries for document retrieval, where fuzzy concept matrices are used for knowledge representation and the elements in a fuzzy concept matrix are represented by trapezoidal fuzzy numbers parameterized by quadruples (a,b,c,d), where 0 less than or equal to a less than or equal to b less than or equal to c less than or equal to d less than or equal to 1. Intelligent retrieval capability and flexible user's queries are consequently provided for.
A new method for fuzzy information retrieval based on fuzzy hierarchical clustering and fuzzy inference techniques
(2005-04-01)
Horng, YJ
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Chen, SM
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Chang, YC
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Lee, CH
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資訊工程學系
In this paper, we extend the work of Kraft et al to present a new method for fuzzy information retrieval based on fuzzy hierarchical clustering and fuzzy inference techniques. First, we present a fuzzy agglomerative hierarchical clustering algorithm for clustering documents and to get the document cluster centers of document clusters. Then, we present a method to construct fuzzy logic rules based on the document clusters and their document cluster centers. Finally, we apply the constructed fuzzy logic rules to modify the user's query for query expansion and to guide the information retrieval system to retrieve documents relevant to the user's request. The fuzzy logic rules can represent three kinds of fuzzy relationships (i.e., fuzzy positive association relationship, fuzzy specialization relationship and fuzzy generalization relationship) between index terms. The proposed fuzzy information retrieval method is more flexible and more intelligent than the existing methods due to the fact that it can expand users' queries for fuzzy information retrieval in a more effective manner.
Automatically constructing multi-relationship fuzzy concept networks for document retrieval
(2003-04-01)
Horng, YJ
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Chen, SM
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Lee, CH
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資訊工程學系
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Department of Computer Science
Although the knowledge bases incorporated in existing information retrieval systems can enhance retrieval effectiveness, many of them are built by domain experts. It is obvious that the construction of such knowledge bases requires a large amount of human effort. In this paper, an intelligent fuzzy information retrieval system with an automatically constructed knowledge base is presented; the knowledge base is represented by a multi-relationship fuzzy concept network. The multi-relationship fuzzy concept network can describe four kinds of context-independent and context-dependent fuzzy relationships, i.e., "fuzzy positive association" relationship, "fuzzy negative association" relationship, "fuzzy generalization" relationship, and "fuzzy specialization" relationship between concepts. The users of the fuzzy information retrieval system can submit a fuzzy contextual query which specifies the search context in the query formula. The fuzzy information retrieval system retrieves documents whose contents are relevant to the user's query by some kinds of fuzzy relationships for the specified search context of the user's query. The proposed fuzzy information retrieval method is more intelligent and more flexible than the existing methods due to the fact that it can construct multi-relationship fuzzy concept networks automatically and it can provide contextual search capability to allow the users to specify fuzzy contextual queries in a more intelligent and flexible manner.
Automatically constructing multi-relationship fuzzy concept networks in fuzzy information retrieval systems
(2001)
Horng, YJ
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Chen, SM
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Lee, CH
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資訊工程學系
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Department of Computer Science
In this paper, an intelligent fuzzy information retrieval system with an automatically constructed knowledge base is presented. The knowledge base is represented by a multi-relationship fuzzy concept network that can depict the relationships and their associating relevance degrees between concepts clearly. Based on the multi-relationship fuzzy concept network architecture, the users of the fuzzy information retrieval system can submit a fuzzy contextual query that specifies; the search context in the query formula. The fuzzy information retrieval system retrieves documents whose contents are relevant to the user's query by some required relationships beneath a specified search context The proposed fuzzy information retrieval method is more intelligent and more flexible than the existing methods since it can automatically construct knowledge bases (i.e, multi-relationship fuzzy concept networks) and it provides contextual search capability that allows users to specify fuzzy contextual queries in a more flexible manner.
Document retrieval using fuzzy-valued concept networks
(2001-02-01)
Chen, SM
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Horng, YJ
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Lee, CH
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資訊工程學系
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Department of Computer Science
This paper presents a new method for document retrieval using fuzzy-valued concept networks, where the relevant degrees between the concepts in a fuzzy-valued concept network are represented by arbitrary shapes of fuzzy numbers. There are two kinds of relevant relationships between any two concepts in a fuzzy-valued concept network, i.e., fuzzy positive association and fuzzy negative association. The relevant matrices and the relationship matrices are used to model the fuzzy-valued concept network. The elements in a relevant matrix represent the relevant degrees between concepts. The elements in a relationship matrix represent the relevant relationships between concepts. Furthermore, we also allow users' queries to be represented by arbitrary shapes of fuzzy numbers and to use fuzzy positive association relationship and fuzzy negative association relationship for formulating their queries for increasing the flexibility of fuzzy information retrieval systems. We also present an information retrieval method in the Internet environment based on the network-type fuzzy-valued concept network architecture.
Finding inheritance hierarchies in fuzzy-valued concept-networks
(1999-02-01)
Horng, YJ
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Chen, SM
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資訊工程學系
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Department of Computer Science
In this paper, we extend the works of [2] and [4] to present a new method for finding the inheritance hierarchies in fuzzy-valued concept-networks, where the relevant values (degrees of generalization or degrees of similarity) between concepts in a fuzzy-valued concept network are represented by fuzzy numbers. The proposed method is more flexible than the ones presented in [2] and [4] due to the fact that it allows the grades of similarity and the grades of generalization between concepts to be represented by fuzzy numbers rather than crisp real values between zero and one or interval values in [0, 1].
Finding inheritance hierarchies in interval-valued fuzzy concept-networks
(1996-11-25)
Chen, SM
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Horng, YJ
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資訊工程學系
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Department of Computer Science
This paper extends the work of Itzkovich and Hawkes (1994) to present the concepts of interval-valued fuzzy concept-networks and to present an algorithm for finding the collection of inheritance hierarchies in interval-valued fuzzy concept-networks, where the similarity relations and the generalization relations between concepts are represented by interval values in [0, 1]. The proposed method is more flexible than the one presented in Itzkovich and Hawkes (1994) due to the fact that it allows the grades of similarity relations and the generalization relations between concepts to be represented by interval-values rather than crisp real values between zero and one.
Fuzzy information retrieval based on multi-relationship fuzzy concept networks
(2003-11-16)
Chen, SM
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Horng, YJ
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Lee, CH
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資訊工程學系
;
Department of Computer Science
In this paper, we present a new method for fuzzy information retrieval based on multi-relationship fuzzy concept networks. There are four kinds of fuzzy relationships in a multi-relationship fuzzy concept network, i.e., "fuzzy positive association" relationship, "fuzzy negative association" relationship, "fuzzy generalization" relationship and "fuzzy specialization" relationship. By performing fuzzy inferences based on the multi-relationship fuzzy concept network, the fuzzy information retrieval system can retrieve documents containing concepts that are not directly specified by the user but are somehow related to the user's query. In order to perform fuzzy inferences more efficiently, we use concept matrices to represent the degrees of fuzzy relationships between concepts in a multi-relationship fuzzy concept network. By calculating the transitive closures of concept matrices, the implicit degrees of fuzzy relationships between concepts are obtained. Multiple degrees of satisfaction that a document satisfies the user's query with respect to the fuzzy relationships between concepts are calculated. These satisfaction degrees are aggregated according to the user's specification to find the most relevant documents with respect to the user's query. The proposed fuzzy information retrieval method is more flexible and more intelligent than the one we presented in (IEEE Trans. Systems Man Cybernet.-Part B: Cybernet. 29(1) (1999) 126). (C) 2002 Elsevier B.V. All rights reserved.
Fuzzy query processing for document retrieval based on extended fuzzy concept networks
(1999-02-01)
Chen, SM
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Horng, YJ
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資訊工程學系
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Department of Computer Science
In this paper, we present a new method for fuzzy query processing for document retrieval based on extended fuzzy concept networks. In an extended fuzzy concept network, there are four kinds of fuzzy relationships between concepts, i.e., fuzzy positive association, fuzzy negative association, fuzzy generalization, and fuzzy specialization, An extended fuzzy concept network can be modeled by a relation matrix and a relevance matrix, where the elements in a relation matrix represent the fuzzy relationships between concepts, and the elements in a relevance matrix indicate the degrees of relevance between concepts. The implicit fuzzy relationships between concepts can be inferred by the transitive closure of the relation matrix. The implicit degrees of relevance between concepts also can be inferred by the transitive closure of the relevance matrix. The proposed method is more flexible than the ones presented in [8] and [17] due to the fact that it allows the users to perform positive queries, negative queries, generalization queries, and specialization queries. The proposed method allows the users to perform fuzzy queries in a more flexible and more intelligent manner.
Temporal knowledge representation and reasoning techniques using time Petri nets
(1999-08-01)
Jong, WT
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Shiau, YS
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Horng, YJ
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Chen, HH
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Chen, SM
In this paper, we present temporal knowledge representation and reasoning techniques using time Petri nets. A method is also proposed to check the consistency of the temporal knowledge. The proposed method can overcome the drawback of the one presented in [16]. It provides a useful way to check the consistency of the temporal knowledge.