Yih-Jen Horng

Fuzzy query processing for document retrieval based on extended fuzzy concept networks

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 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.

Fuzzy information retrieval based on multi-relationship fuzzy concept networks

Abstract 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 users 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 users query with respect to the fuzzy relationships between concepts are calculated. These satisfaction degrees are aggregated according to the users specification to find the most relevant documents with respect to the users 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).

Temporal knowledge representation and reasoning techniques using time Petri nets

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 Yao (1994). It provides a useful way to check the consistency of the temporal knowledge.

Finding inheritance hierarchies in interval-valued fuzzy concept-networks

Abstract 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.

Finding inheritance hierarchies in fuzzy-valued concept-networks

In this paper, we extend the works of Chen and Horng (1996) and Itzkovich and Hawkes (1994) 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 previously 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].

Automatically constructing multi-relationship fuzzy concept networks in fuzzy information retrieval systems

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 users 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.