Representation Of Imprecise Knowledge Research Articles

Situation Assessment Method Based on Bayesian Network and Its Application in Space Battlefield

The space situation awareness ability is the development of space resources, the protection of space assets and the control of space. It’s the key to the implementation of space military action. Therefore, the satellite in the space military confrontation as the research object is discussed to solve the situation assessment problem of space battlefield. A method of situation assessment is proposed which based on Bayesian Network and its advantages to solve the imprecise knowledge representation problems and inference problems. Firstly, a Bayesian Network model for situation assessment is established based on expert experience knowledge. Then, the Bayesian inference algorithm is used to predict the spatial situation and achieve the situation assessment. Finally, the algorithm is validated and analyzed by using the BNT toolbox of MATLAB. Simulation results show that the method can adapt to the change of battlefield situation, and are more accurate and reliable for the prediction of situation. It has some important practical values in the future space battlefield.

A fuzzy extension of the semantic Building Information Model

Abstract The Building Information Model (BIM) has become a key tool to achieve communication during the whole building life-cycle. Open standards, such as the Industry Foundation Classes (IFC), have contributed to expand its adoption, but they have limited capabilities for cross-domain information integration and query. To address these challenges, the Linked Building Data initiative promotes the use of ontologies and Semantic Web technologies in order to create more formal and interoperable BIMs. In this paper, we present a fuzzy logic-based extension of such semantic BIMs that provides support for imprecise knowledge representation and retrieval. We propose an expressive fuzzy ontology language, and describe how to use a fuzzy reasoning engine in a BIM context with selected examples. The resulting fuzzy semantic BIM enables new functionalities in the project design and analysis stages—namely, soft integration of cross-domain knowledge, flexible BIM query, and imprecise parametric modeling.

Fuzzy Ontology Evolution: Classification of a New Individual

Ontologies help to conceive the real world with its semantic constraints. But this world has several uncertainties and imperfections that we cannot conceive using traditional In our work, we are mainly interested in the imprecise knowledge representation problem. We believe that the most appropriate way is to use fuzzy logic in order to build ontologies called Fuzzy ontologies. Even if there are several researches revolve around handling imprecise knowledge, however, there are still some issues to be further studied; one of them is the evolution problem which is still a tedious field. In this paper, and as a first step in the problem of fuzzy ontology evolution, we are interested in the classification of new individuals; for this reason, we use the classificatory reasoning mechanism to enable the classification of new individuals in a fuzzy hierarchy.

Reasoning within intuitionistic fuzzy rough description logics

It is generally accepted that the management of imprecision and vagueness will yield more intelligent and realistic knowledge-based applications. Description Logics (DLs) are suitable, well-known logics for managing structured knowledge that have gained considerable attention the last decade. The current research progress and the existing problems of uncertain or imprecise knowledge representation and reasoning in DLs are analyzed in this paper. An integration between the theories of intuitionistic fuzzy DLs and rough DLs has been attempted by providing intuitionistic fuzzy rough DLs based on intuitionistic fuzzy rough set theory. The syntax, semantics and properties of intuitionistic fuzzy rough DLs are given. It is proved that the satisfiability, subsumption, entailment and ABox consistency reasoning in intuitionistic fuzzy rough DLs may be reduced to the corresponding satisfiability, subsumption, entailment and ABox consistency reasoning in fuzzy DL over complete lattice, respectively.

Reasoning within expressive fuzzy rough description logics

It is generally accepted that the management of imprecision and vagueness will yield more intelligent and realistic knowledge-based applications. Description Logics (DLs) are suitable, well-known logics for managing structured knowledge that have gained considerable attention the last decade. The current research progress and the existing problems of uncertain or imprecise knowledge representation and reasoning in DLs are analyzed in this paper. An integration between the theories of fuzzy DLs and rough DLs has been attempted by providing fuzzy rough DLs based on fuzzy rough set theory. The syntax, semantics and properties of fuzzy rough DLs are given. It is proved that the satisfiability, subsumption, entailment and ABox consistency reasoning in fuzzy rough DLs may be reduced to the ABox consistency reasoning in the corresponding fuzzy DLs.

Reasoning with rough description logics: An approximate concepts approach

The current research progress and the existing problems of uncertain or imprecise knowledge representation and reasoning in description logics are analyzed in this paper. Approximate concepts are introduced to description logics based on rough set theory, and a kind of new rough description logic RDLAC (rough description logic based on approximate concepts) is proposed based on approximate concepts. The syntax, semantics and properties of the RDLAC are given. It is proved that the approximate concept satisfiability (definitely satisfiability and possibly satisfiability) reasoning problem and approximate concepts rough subsumption reasoning problem w.r.t. rough TBox in RDLAC may be reduced to the concept satisfiability reasoning problem in (almost) standard ALC (the description logic that provides the Boolean concept constructors plus the existential and universal restriction constructors). The works of this paper provide logic foundations for approximate ontologies and theoretical foundations for reasoning algorithms of more expressive rough description logics including approximate concepts, number restrictions, nominals, inverse roles and role hierarchies.

Concept-based search and questionnaire systems

World Wide Web search engines including Google, Yahoo and MSN have become the most heavily-used online services (including the targeted advertising), with millions of searches performed each day on unstructured sites. In this presentation, we would like to go beyond the traditional web search engines that are based on keyword search and the Semantic Web which provides a common framework that allows data to be shared and reused across application. For this reason, our view is that "Before one can use the power of web search the relevant information has to be mined through the concept-based search mechanism and logical reasoning with capability to Q&A representation rather than simple keyword search". In this paper, we will first present the state of the search engines. Then we will focus on development of a framework for reasoning and deduction in the web. A new web search model will be presented. One of the main core ideas that we will use to extend our technique is to change terms-documents-concepts (TDC) matrix into a rule-based and graph-based representation. This will allow us to evolve the traditional search engine (keyword-based search) into a concept-based search and then into Q&A model. Given TDC, we will transform each document into a rule-based model including it's equivalent graph model. Once the TDC matrix has been transformed into maximally compact concept based on graph representation and rules based on possibilistic relational universal fuzzy-type II (pertaining to composition), one can use Z(n)-compact algorithm and transform the TDC into a decision-tree and hierarchical graph that will represents a Q&A model. Finally, the concept of semantic equivalence and semantic entailment based on possibilistic relational universal fuzzy will be used as a basis for question-answering (Q&A) and inference from fuzzy premises. This will provide a foundation for approximate reasoning, language for representation of imprecise knowledge, a meaning representation language for natural languages, precisiation of fuzzy propositions expressed in a natural language, and as a tool for Precisiated Natural Language (PNL) and precisation of meaning. The maximally compact documents based on Z(n)-compact algorithm and possibilistic relational universal fuzzy-type II will be used to cluster the documents based on concept-based query-based search criteria.

Using Bézier curves in approximate reasoning

The possibilities of using Bézier curves as a concrete tool in the development of the inference engine of a decision support system allowing approximate reasoning are explored. We propose using Bézier curves for representing membership functions or implementing some kind of fuzzy implication. The effects on the axioms characterizing fuzzy implications are investigated. Bézier curves are easily computable, flexible and have nice smooth properties. They are extensively used in the CAD/CAM domain. They could play an important role in some processes of imprecise knowledge representation and manipulation. Such techniques are promising in the implementation of several stages of developing the inference engine of a fuzzy decision support system. Moreover, these methods capture some of the flexibility and subtlety characteristic of human reasoning.

Intelligent Traffic Data Processing for ITS Applications

Real-time traffic data are the lifeline sustaining the operation of Advanced Traffic Management and Advanced Traveler Information Systems (ATMS/ATIS). Data are essential to drive algorithms related to congestion/incident detection, travel time forecasting, and real-time route guidance. A common problem in many ATMS/ATIS applications is the sparsity of real-time traffic data, reflecting the financial constraints of acquiring and maintaining large-scale traffic monitoring systems. This paper proposes the use of intelligent processing, or data integration tools, to overcome the data sparsity problem and make the best use of existing data resources. This approach recognizes the elements of uncertainty and vagueness in defining and solving the problem. An example application of the proposed data integration method is presented in the context of a congestion detection algorithm. The method uses an imprecise knowledge representation within the framework of fuzzy operator logic (FOL) and the modified Dempster-Shafer rule of combination. Results indicate that knowledge of a link congestion status (i.e., congested or uncongested) increased several folds after the data integration algorithm was applied. Further work is needed to calibrate the algorithm in the field (in this study simulation was used) and to apply the procedures on large-scale networks.

Expressing imprecision in probabilistic knowledge

The representation of imprecise knowledge in probabilistic expert systems is investigated. It is argued that Bayesian statistics offers a multitude of models and methods that may be employed to express probabilistic knowledge by first and second order probability distributions. Distributions offer a much richer repertoire to express uncertainty that point probabilities. Special attention is payed to Dirichletian weights of evidence in discrete systems and to predictons and inverse predictions in linear regression. Approximate methods for non-conjugate distributions and for incomplete data are discussed. An important property of imprecision in complex systems is that it explodes rapidly when the conclusions are derived from long chains of premises. Thus, imprecision is a pruning criterion is a pruning criterion in complex knowledge systems.

PRUF—a meaning representation language for natural languages

PRUF—an acronym for Possibilistic Relational Universal Fuzzy—is a meaning representation language for natural languages which departs from the conventional approaches to the theory of meaning in several important respects. First, a basic assumption underlying PRUF is that the imprecision that is intrinsic in natural languages is, for the most part, possibilistic rather than probabilistic in nature. Thus, a proposition such as “Richard is tall” translates in PRUF into a possibility distribution of the variable Height (Richard), which associates with each value of the variable a number in the interval [0,11 representing the possibility that Height (Richard) could assume the value in question. More generally, a proposition, p , translates into a procedure, P, which returns a possibility distribution, Π p , with P and Π p representing, respectively, the meaning of p and the information conveyed by p . In this sense, the concept of a possibility distribution replaces that of truth as a foundation for the representation of meaning in natural languages. Second, the logic underlying PRUF is not a two-valued or multivalued logic, but a fuzzy logic, FL, in which the truth-values are linguistic, that is, are of the form true, not true, very true, more or less true, not very true , etc., with each such truth-value representing a fuzzy subset of the unit interval. The truth-value of a proposition is defined as its compatibility with a reference proposition, so that given two propositions p and r, one can compute the truth of p relative to r . Third, the quantifiers in PRUF—like the truth-values—are allowed to be linguistic, i.e. may be expressed as most, many, few, some, not very many, almost all, etc. Based on the concept of the cardinality of a fuzzy set, such quantifiers are given a concrete interpretation which makes it possible to translate into PRUF propositions exemplified by “Many tall men are much taller than most men,” “All tall women are blonde is not very true,” etc. The translation rules in PRUF are of four basic types: Type I—pertaining to modification; Type II—pertaining to composition; Type III—pertaining to quantification; and Type IV—pertaining to qualification and, in particular, to truth qualification, probability qualification and possibility qualification. The concepts of semantic equivalence and semantic entailment in PRUF provide a basis for question-answering and inference from fuzzy premises. In addition to serving as a foundation for approximate reasoning, PRUF may be employed as a language for the representation of imprecise knowledge and as a means of precisiation of fuzzy propositions expressed in a natural language.