Fuzzy Knowledge Representation Research Articles

Fuzzy Knowledge Representation Based on Fuzzy Linguistic Variable Ontology and SWRL on the Semantic Web

Ontology is adopted as a standard for knowledge representation on the Semantic Web, and Ontology Web Language (OWL) is used to add structure and meaning to web applications. In order to share and resue the fuzzy knowledge on the Semantic Web, we propose the fuzzy linguistic variables ontology (FLVO), which utilizes ontology to represent formally the fuzzy linguistic variables and defines the semantic relationships between fuzzy concepts. Then fuzzy rules are described in Semantic Web Rule Language (SWRL) on the basis of FLVO model. Taking a sample case for students’ performance in physics for example, the fuzzy rule management system is built by using the tool protégé and SWRLTab, which shows that this research enables distributed fuzzy applications on the Semantic Web.

Efficacy of fuzzy representation of knowledge for integrated management of networked systems

Efficacy of fuzzy representation of knowledge for integrated management of networked systems

Towards the methodology for development of fuzzy relational database applications

In this paper we examine the possibilities to extend the relational data model with the mechanisms that can handle imprecise, uncertain and inconsistent attribute values using fuzzy logic and fuzzy sets. We present a fuzzy relational data model which we use for fuzzy knowledge representation in relational databases that guarantees the model in 3rd normal form. We also describe the CASE tool for the fuzzy database model development which is apparently the first implementation of such a CASE tool. In this sense, this paper presents a leap forward towards the specification of a methodology for fuzzy relational database applications development.

A Fuzzy Control Heuristic Applied to Non-linear Dynamic System Using a Fuzzy Knowledge Representation

This paper presents the design of a fuzzy control heuristic that can be applied for modeling nonlinear dynamic systems using a fuzzy knowledge representation. Nonlinear dynamic systems have been modeled traditionally on the basis of connections between the subsystems that compose it. Nevertheless, this model design does not consider some of the following problems: existing dynamics between the subsystems; order and priority of the connection between subsystems; degrees of influence or causality between subsystems; particular state of each subsystem and state of the system on the basis of the combination of the diverse states of the subsystems; positive or negative influences between subsystems. In this context, the main objective of this proposal is to manage the whole system state by managing the state combination of the subsystems involved. In the proposed design the diverse states of subsystems at different levels are represented by a knowledge base matrix of fuzzy intervals (KBMFI). This type of structure is a fuzzy hypercube that provides facilities operations like: insert, delete, and switching. It also allows Boolean operations between different KBMFI and inferences. Each subsystem in a specific level and its connectors are characterized by factors with fuzzy attributes represented by membership functions. Existing measures the degree of influence among the different levels are obtained (negatives, positives). In addition, the system state is determined based on the combination of the statements of the subsystems (stable, oscillatory, attractor, chaos). It allows introducing the dynamic effects in the calculation of each output level. The control and search of knowledge patterns are made by means of a fuzzy control heuristic. Finally, an application to the co-ordination of the activities among different levels of the operation of an underground mine is developed and discussed.

Fuzzy Knowledge Representation for Fuzzy Systems Based on Fuzzy Ontology on the Semantic Web

Ontology is adopted as a standard for knowledge representation on the Semantic Web, and Resource Description Framework (RDF) is used to add structure and meaning to Web applications. In order to incorporate fuzzy systems into the Semantic Web, this paper utilizes fuzzy ontology to represent formally the fuzzy linguistic variables, considering the semantic relationships between fuzzy concepts. Then fuzzy rule is described as a RDF resource with properties: “IF” and “THEN”, and rule's antecedent and consequent is represented in RDF statement. Taking the fuzzy control system of industrial washing machine for example, the fuzzy system with ontology and RDF is built, which shows that this research enables distributed fuzzy applications on the Semantic Web.

Fuzzy knowledge representation study for incremental learning in data streams and classification problems

The extraction of models from data streams has become a hot topic in data mining due to the proliferation of problems in which data are made available online. This has led to the design of several systems that create data models online. A novel approach to online learning of data streams can be found in Fuzzy-UCS, a young Michigan-style fuzzy-classifier system that has recently demonstrated to be highly competitive in extracting classification models from complex domains. Despite the promising results reported for Fuzzy-UCS, there still remain some hot issues that need to be analyzed in detail. This paper carefully studies two key aspects in Fuzzy-UCS: the ability of the system to learn models from data streams where concepts change over time and the behavior of different fuzzy representations. Four fuzzy representations that move through the dimensions of flexibility and interpretability are included in the system. The behavior of the different representations on a problem with concept changes is studied and compared to other machine learning techniques prepared to deal with these types of problems. Thereafter, the comparison is extended to a large collection of real-world problems, and a close examination of which problem characteristics benefit or affect the different representations is conducted. The overall results show that Fuzzy-UCS can effectively deal with problems with concept changes and lead to different interesting conclusions on the particular behavior of each representation.

A Fuzzy Knowledge Matching Method Based on Variable Weight IDM

Fuzzy knowledge representation and processing is a very important topic in artificial intelligence research, the key of the fuzzy knowledge inference is the knowledge matching. Via our investigation on kinds of conventional methods in fuzzy knowledge matching, we propose a knowledge matching method based on variable weights IDM (Inscribed Diameter Matching). Through our comparative analysis, Variable weights IDM is able to overcome some drawbacks exist in the traditional methods. We also give a fusion model which can improve efficiency and accuracy of knowledge inference. The experimental results verify the practicability and availability of our method.

Models of representation of fuzzy temporal knowledge in databases of temporal series

the problem of representation of fuzzy knowledge in databases of temporal series is considered. The existing approaches to formalization and search of temporal knowledge in such databases are described. A new class of models for representation of fuzzy temporal knowledge in databases of time series is proposed in order to support the processes of knowledge revealing. The methodology of estimating the efficiency of representations based on the use of general-system principles of human thinking and speaking communication is presented. Based on the selected system of criteria, the advantages of the proposed class of models are substantiated.

Extended Fuzzy Description Logics with Comparisons Between Fuzzy Membership Degrees

语义Web模糊知识的表示和应用常常涉及模糊隶属度比较,但现有描述逻辑的模糊扩展缺乏描述模糊隶属度比较的能力.提出支持模糊隶属度比较和描述逻辑ALCN(attributive concept description language with complements and number restriction)概念构造子的扩展模糊描述逻辑FCALCN(fuzzy comparable ALCN).FCALCN引入新的原子概念形式以支持模糊隶属度比较.给出FCALCN的推理算法,证明了在空TBox约束下FCALCN的推理问题复杂性是多项式空间完全的.FCALCN能够表达语义Web上涉及模糊隶属度比较的复杂模糊知识并实现对它们的推理.;The representation and application of fuzzy knowledge on the semantic Web often relate to the comparisons between fuzzy membership degrees. However, the current fuzzy extensions of description logics do not support the expression of such comparisons. This paper proposes an extended fuzzy description logic that supports the comparisons of fuzzy membership degrees and the concept constructors from description logic ALCN (attributive concept description language with complements and number restriction), written FCALCN (fuzzy comparable ALCN). FCALCN introduces new forms of atom concepts in order to support the comparisons between fuzzy membership degrees. A reasoning algorithm for FCALCN is proposed, and the complexity of the reasoning problems of FCALCN with empty TBox is proved to be PSpace-complete. FCALCN can represent expressive fuzzy knowledge involving the comparisons of fuzzy membership degrees on the semantic Web and enable reasoning of them.

A dynamic fuzzy description logic

Fuzzy description logics are considered as the logical infrastructure of fuzzy knowledge representation on the semantic Web. To deal with fuzzy and dynamic knowledge on the semantic Web and its applications, a new fuzzy extension of Attribute Language with Complement based on dynamic fuzzy logic called the dynamic fuzzy description logic (DFALC) is presented. The syntax and semantics of DFALC are formally defined, and the forms of axioms and assertions are specified. The DFALC provides more reasonable logic foundation for the semantic Web, and overcomes the insufficiency of using fuzzy description logic FALC to act as logical foundation for the semantic Web. The extended DFALC is more expressive than the existing fuzzy description logics and present more fuzzy information on the semantic Web.

Reasoning within extended fuzzy description logic

An extended fuzzy description logic is proposed to increase expressive power for complex fuzzy information. We introduce cut sets of the fuzzy concepts and fuzzy roles as atomic concepts and atomic roles, and inherit concept constructors of the description logics to support a new logic system for fuzzy knowledge representation. We define the syntax, semantics and knowledge base of the extended fuzzy description logic, and focus on sat-domain and consistency as main reasoning tasks. We also present sound and complete tableau algorithms for these reasoning tasks, and prove the complexity of them is PSPACE-complete. Compared with the other fuzzy description logics, the extended fuzzy description logic can express more wide fuzzy information.

Fuzzy knowledge representation and reasoning using a generalized fuzzy petri net and a similarity measure

In the study of weighted fuzzy production rules (WFPRs) reasoning, we often need to consider those rules whose consequences are represented by two or more propositions connected by “AND” or “OR”. To enhance the representation capability of those rules, this paper proposes two types of knowledge representation parameters, namely, the input weight and the output weight, for a rule. A Generalized Fuzzy Petri Net (GFPN) is also presented for WFPR reasoning. Furthermore, this paper gives a similarity measure to improve the evaluation method of WFPRs and the multilevel fuzzy reasoning in which the consequences and their certainty factors are deduced synchronously by using a GFPN.

A novel method for discovering fuzzy sequential patterns using the simple fuzzy partition method

AbstractSequential patterns refer to the frequently occurring patterns related to time or other sequences, and have been widely applied to solving decision problems. For example, they can help managers determine which items were bought after some items had been bought. However, since fuzzy sequential patterns described by natural language are one type of fuzzy knowledge representation, they are helpful in building a prototype fuzzy knowledge base in a business. Moreover, each fuzzy sequential pattern consisting of several fuzzy sets described by the natural language is well suited for the thinking of human subjects and will help to increase the flexibility for users in making decisions. Additionally, since the comprehensibility of fuzzy representation by human users is a criterion in designing a fuzzy system, the simple fuzzy partition method is preferable. In this method, each attribute is partitioned by its various fuzzy sets with pre‐specified membership functions. The advantage of the simple fuzzy partition method is that the linguistic interpretation of each fuzzy set is easily obtained. The main aim of this paper is exactly to propose a fuzzy data mining technique to discover fuzzy sequential patterns by using the simple partition method. Two numerical examples are utilized to demonstrate the usefulness of the proposed method.

A Fuzzy Data Mining Algorithm for Finding Sequential Patterns

Since fuzzy knowledge representation can facilitate interaction between an expert system and its users, the effective construction of a fuzzy knowledge base is important. Fuzzy sequential patterns described by natural language are one type of fuzzy knowledge representation, and can thus be helpful in building a prototype fuzzy knowledge base. We define that a fuzzy sequence is an ordered list of frequent fuzzy grids, and the length of a fuzzy sequence is the number of frequent fuzzy grids in the frequent fuzzy sequence. Frequent fuzzy grids and frequent fuzzy sequences can be determined by comparing individual fuzzy supports with the user-specified minimum fuzzy support. A fuzzy sequential pattern is just a frequent fuzzy sequence, but it is not contained in any other frequent fuzzy sequence. In this paper, an effective algorithm called the Fuzzy Grids Based Sequential Patterns Mining Algorithm (FGBSPMA) is proposed to generate fuzzy sequential patterns. A numerical example is used to show an analysis of the user visit to websites, demonstrating the usefulness of the proposed algorithm.

Knowledge acquisition in the fuzzy knowledge representation framework of a medical consultation system

This paper describes the fuzzy knowledge representation framework of the medical computer consultation system MedFrame/CADIAG-IV as well as the specific knowledge acquisition techniques that have been developed to support the definition of knowledge concepts and inference rules. As in its predecessor system CADIAG-II, fuzzy medical knowledge bases are used to model the uncertainty and the vagueness of medical concepts and fuzzy logic reasoning mechanisms provide the basic inference processes. The elicitation and acquisition of medical knowledge from domain experts has often been described as the most difficult and time-consuming task in knowledge-based system development in medicine. It comes as no surprise that this is even more so when unfamiliar representations like fuzzy membership functions are to be acquired. From previous projects we have learned that a user-centered approach is mandatory in complex and ill-defined knowledge domains such as internal medicine. This paper describes the knowledge acquisition framework that has been developed in order to make easier and more accessible the three main tasks of: (a) defining medical concepts; (b) providing appropriate interpretations for patient data; and (c) constructing inferential knowledge in a fuzzy knowledge representation framework. Special emphasis is laid on the motivations for some system design and data modeling decisions. The theoretical framework has been implemented in a software package, the Knowledge Base Builder Toolkit. The conception and the design of this system reflect the need for a user-centered, intuitive, and easy-to-handle tool. First results gained from pilot studies have shown that our approach can be successfully implemented in the context of a complex fuzzy theoretical framework. As a result, this critical aspect of knowledge-based system development can be accomplished more easily.

Segmentation of pelvis and femur from 3D CT images

This paper proposes a fuzzy expert system scheme for foot CT image segmentation. Our method aims at decomposing a fractured foot bone into each of fragments and normal bones, as a framework of developing a computer-aided orthopedic surgery planning system. It employs fuzzy knowledge representation to express orthopedic surgeon's knowledge: knowledge of distance to extract fragments and knowledge of intensity distribution to decompose adjoining normal bones on the articular cartilage. The two types of knowledge are compiled to a total degree, which is evaluated in the segmentation procedure. Qualitative evaluation of an orthopedic surgeon shows that our method could correctly segment the bones and fragments.

Fuzzy expert system for foot CT image segmentation

Abstract This paper proposes a fuzzy expert system scheme for foot CT image segmentation. Our method aims at decomposing a fractured foot bone into each of fragments and normal bones, as a framework of developing a computer-aided orthopedic surgery planning system. It employs fuzzy knowledge representation to express orthopedic surgeon's knowledge: knowledge of distance to extract fragments and knowledge of intensity distribution to decompose adjoining normal bones on the articular cartilage. The two types of knowledge are compiled to a total degree, which is evaluated in the segmentation procedure. Qualitative evaluation of an orthopedic surgeon shows that our method could correctly segment the bones and fragments.

Automated knowledge acquisition for design and manufacturing: The case of micromachined atomizer

In order to remain competitive in the global market, original equipment manufacturers (OEMs) are developing a process-based, knowledge-driven product development environment with emphasis on the acquisition, storing, and utilization of manufacturing knowledge. This is usually achieved by using the symbolic artificial intelligence (AI) approach. Specifically, knowledge-based expert systems are developed to capture human expertise, mostly in terms of IF–THEN production rules. It has been recognized that the development of symbolic knowledge-based expert systems suffers from the so-called knowledge acquisition bottleneck. Knowledge acquisition is the process of collecting domain knowledge and transforming the knowledge into a computerized representation. It is a challenging and time-consuming process due to the difficulties involved in eliciting knowledge from human experts. This paper presents an automated approach for knowledge acquisition by integrating neural networks' learning ability and fuzzy logic's structured knowledge representation. Using this approach, knowledge is automatically acquired from data and represented using humanly intelligible fuzzy rules. The approach is applied to a case study of the design and manufacturing of micromachined atomizers for gas turbine engine. The influence of geometric features on the performance of the atomizers is investigated. The results are then compared with those obtained using traditional regression analysis approach (abstract mathematical models). It was found that the automated approach provides an efficient means for knowledge acquisition. Since the fuzzy rules extracted are easy to understand, they can be used to allow more clear specification of manufacturing processes and to shorten learning curves for novice manufacturing engineers.

An incremental learning system for imprecise and uncertain knowledge discovery

Discovering knowledge from databases in order to classify new patterns is an interesting field for machine learning methods. Particularly, rule induction approaches constitute prominent machine learning methods that lead to avoid the disadvantages of the decision tree. The fuzzy incremental production rule (FIPR) based system is a rule induction system that generates imprecise and uncertain IF-THEN rules from data records. It allows the incremental maintenance of the knowledge base with a minimal overhead. The precision analysis with real world data sets, and the complexity analysis are used to compare this system with existing ones and to prove the usefulness of fuzzy knowledge representation.

Fuzzy contextual classification of multisource remote sensing images

The authors' objective has been to model satellite image classification as a cognitive process, providing a procedure that mimics the rich interaction of human activity in solving classification problems. The key features of this approach are the definition of a knowledge-based classification methodology designed to integrate contextual information into a multisource classification scheme, together with a fuzzy knowledge representation framework to model the overall process in a form that closely resembles the mental representation of human experts. An application for the identification of the glacier equilibrium line in two different zones of the Italian Alps has been developed to evaluate the performance of their methodology in a real domain where class discrimination requires the simultaneous use of contextual and multisource information. Numerical results are provided and compared with those obtained by a conventional classification procedure. The advantages of the approach, as seen in the experimental context, are examined.