A knowledge-based approach to online document retrieval system design

This paper presents the design and development of a prototype document retrieval system using a knowledge-based systems approach. Both the domain-specific knowledge base and the inferencing schemes are based on a fuzzy set theoretic framework. A query in natural language represents a request to retrieve a relevant subset of documents from a document base. Such a query, which can include both fuzzy terms and fuzzy relational operators, is converted into an unambiguous intermediate form by a natural language interface. Concepts that describe domain topics and the relationships between concepts, such as the synonym relation and the implication relation between a general concept and more specific concepts, have been captured in a knowledge base. The knowledge base enables the system to emulate the reasoning process followed by an expert, such as a librarian, in understanding and reformulating user queries. The retrieval mechanism processes the query in two steps. First it produces a pruned list of documents pertinent to the query. Second, it uses an evidence combination scheme to compute a degree of support between the query and individual documents produced in step one. The front-end component of the system then presents a set of document citations to the user in ranked order as an answer to the information request.

Design and implementation issues are discussed for a fuzzy information retrieval system using a knowledge-based system approach. In this context, system performance is identified as a critical point, and heuristics are suggested for a constrained spreading activation in the knowledge base and for optimization of the retrieval procedure. The design and implementation issues outlined are being tested in a prototype system named FIRST (Fuzzy Information Retrieval SysTem), which is being implemented in Prolog on a personal workstation. >

Fuzzy subsets on the non-empty set is a mapping of this set to the interval . The concept of fuzzy subgroups introduced from advanced concept of fuzzy set in group theory. In concept of fuzzy set there is the concept of relations is fuzzy relations. In this study examined that fuzzy relations related to the equivalence and congruence on a fuzzy group and fuzzy factor group. The results of this study was to show that a fuzzy relation if and if is a fuzzy congruence relations on fuzzy group and a fuzzy relation defined of is a fuzzy congruence relations on fuzzy factor group.

Fuzzy set theoretical approach to document retrieval

Fuzzy relations and fuzzy relational databases

Sensitivity of muscle force response of a two-state cross-bridge model to variations in model parameters.

The one central problem in global forecasting area is to minimize the forecasting error and to have a simple model. This paper focuses on the fuzzy time series (FTS) forecasting which is one branch of fuzzy set (FS). Numerous FTS models have been proposed in literature during the past decades and the number of new models is still growing especially in stock markets and student enrolment problems. Some of the FTS models found in literature do not explain the fuzzy logical relationship strength between Ai and Aj which are the left-hand side (LHS) and right-hand side (RHS) of the fuzzy logical relationship respectively. This paper proposed Weighted Subsethood Fuzzy Time Series (WeSuFTS) to build fuzzy reasoning based to address the drawback in forecasting enrolment of students. In this paper, we create the weighted subsethood fuzzy rules to explain the fuzzy relationship between Ai and Aj . The simple centre mid-point defuzzification technique was introduced in defuzzification step. The results show that WeSuFTS method gives promising results where the average error forecasting is 2.31% which is smaller than the one presented by Song and Chissom in 1993 and Chen in 1996, the pioneer of FTS. The proposed method has an advantage since it can give a new interpretation of FTS model. Furthermore, this method able to present the degree of importance of LHS to RHS in explaining the fuzzy logical relationship in FTS.

Rule-based methods have traditionally been applied to develop knowledge-based systems that replicate expert performance on a deep but narrow problem domain. Knowledge engineers capture expert knowledge and encode it as a set of rules for automating the expert’s reasoning process to solve problems in a variety of domains. We describe the development of a knowledge-based system approach to enhance program comprehension of Service Oriented Architecture (SOA) software. Our approach uses rule-based methods to automate the analysis of the set of artifacts involved in building and deploying a SOA composite application. The rules codify expert knowledge to abstract information from these artifacts to facilitate program comprehension and thus assist Software Engineers as they perform system maintenance activities. A main advantage of the knowledge-based approach is its adaptability to the heterogeneous and dynamically evolving nature of SOA environments.

On fuzzy implication relations

By normalizing the values of its pixels with respect to the length of the used scale, a gray image can be interpreted as a fuzzy relation R which is divided in submatrices (possibly square) called blocks. Every block R B is compressed to a block G B , which in turn is decompressed to a block D B (unsigned) ?R B . Both G B and D B are obtained via fuzzy relation equations with continuous triangular norms in which fuzzy sets with Gaussian membership functions are used as coders. The blocks D B are recomposed in order to give a fuzzy relation D. We use the Lukasiewicz t-norm and a watermark (matrix) is embedded in every G B with the LSBM (Least Significant Bit Modification) algorithm by obtaining a block [InlineMediaObject not available: see fulltext.], decompressed to a block [InlineMediaObject not available: see fulltext.] (signed). Both [InlineMediaObject not available: see fulltext.] and [InlineMediaObject not available: see fulltext.] are obtained by using the same fuzzy relation equations. The blocks [InlineMediaObject not available: see fulltext.] are recomposed by obtaining the fuzzy relation [InlineMediaObject not available: see fulltext.] (signed). By evaluating the quality of the reconstructed images via the PSNR (Peak Signal to Noise Ratio) with respect to the original image R, we show that the signed image [InlineMediaObject not available: see fulltext.] is very similar to the unsigned image D for low values of the compression rate.

Properties of the fuzzy composition based on aggregation operators

Introduces the concept of reusable software component retrieval using normalized formal specifications. Reusable Ada software components are stored in a software base that supports a rapid prototyping system called CAPS (Computer Aided Prototyping System). Each component in the software base has a corresponding formal specification. A query in the form of a formal specification is used to search for candidate components that satisfy the requirements of the query. The specification languages used are the Prototype System Description Language (PSDL) and OBJ3. Each specification is normalized to facilitate component retrieval. This paper describes the software base model, syntactic and semantic normalization, and the component retrieval mechanisms. >

Attribute reduction based on neighborhood constrained fuzzy rough sets

The authors have designed a system functional requirement (SFR) specification processor using a knowledge-based system approach in conjunction with other artificial intelligence techniques, this processor can be used for a variety of software systems. Domain-specific expert knowledge is defined for each software system application. The adopted specification language can easily be read by a variety of people such as customers, managers, requirement writers, software designers, and the test team. The processor, for a given software system application, not only checks for consistency, completeness, nonredundancy, and nonambiguity, but also uses domain-specific expert knowledge to verify that the SFR specification document is correct from the domain expert's point of view. To illustrate the proposed approach, the authors give an example of a component of a telephone switching system and the processor system methodology.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Intuitionistic Fuzzy Sets extend the classical notion of Fuzzy Sets, so as to represent “hesitation”: indeed, an item has both a membership degree and a non-membership degree, whose sum could be less than 1; the difference denotes the “hesitation” about the fact that the item belongs or not to the fuzzy set. Similarly, Intuitionistic Fuzzy Relations involve two domains.Supposing that Intuitionistic Fuzzy Sets and Relations are provided as JSON data sets, is there a stand-alone tool to process them? This paper studies if the constructs currently provided by J-CO-QL\(^+\) (the query language of the J-CO Framework) for managing fuzzy sets can actually deal with Intuitionistic Fuzzy Sets and Relations. The results will suggest how to extend J-CO-QL\(^+\) to deal with classical and Intuitionistic Fuzzy Sets in an integrated way.KeywordsIntuitionistic Fuzzy Sets and relationsSoft Querying on JSON data setsManaging Intuitionistic Fuzzy Sets in J-CO-QL\(^+\)