Literature Survey: Fuzzy Ontology

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CHAPTER 1

Introduction

1.1 Literature Survey

C. S. Lee, Z. W. Jian, and L. K. Huang is present a fuzzy ontology and apply it to news summarization. The fuzzy ontology is an extension of the domain ontology that is more suitable to describe the domain knowledge for solving the uncertainty reasoning problems. In addition, a news agent based on the fuzzy ontology is also developed for news summarization. The document pre-processing mechanism will generate the meaningful terms based on the news corpus produced by the retrieval agent and the Chinese news dictionary defined by domain experts. Then, the term classifier will classify the meaningful terms according to the events of the news. The fuzzy inference mechanism will generate the membership degrees for each fuzzy concept of the fuzzy ontology. Every fuzzy concept has a set of membership degrees associated with various events of the domain ontology. Moreover, the Sentence Path Extractor (SPE) will extract a set of possible sentence paths from the fuzzy ontology and send the template set to Sentence Generator (SG). The SG will produce a set of sentences from the class layer of the fuzzy ontology. Finally, the Sentence Filter (SF) will filter noisy sentences and create a set of summarized sentences. The fuzzy ontology with fuzzy concepts is an extension of the domain ontology with crisp concepts that is more suitable to describe the domain knowledge for solving the uncertainty reasoning problem.

T. T. Quan, S. C. Hui, and A. C. M. Fong proposed a semantic web, ontology is adopted as a standard for knowledge representation. Programs can use the knowledge from the semantic web for processing information in a semantic manner. As such, the semantic web enables machine service knowledge from different machines or models produced by different manufacturers to be shared and integrated. Moreover, the generated machine ontology can also be used to provide an interpretation on the common faults that have occurred for a certain machine model from a concept hierarchy. Thus, supporting machine services utilizing semantic web technologies will likely improve customer satisfaction in terms of reducing machine down time as well as increasing productivity. In this paper, we consider using semantic web technologies to support customer service more effectively. This paper describes a semantic help-desk for supporting customer services utilizing the semantic web technologies. It focuses on an automatic generation approach, known as fuzzy formal concept analysis (FFCA), for fuzzy machine service ontology that can also deal with uncertainty data. FFCA has been investigated in our previous work to construct ontology on scholarly knowledge. In this paper, apart from using FFCA for constructing ontology, we also present architecture on how to deploy the constructed ontology in a distributed manner on the semantic web.

C. Hudelot, J. Atif, and I. Bloch implement An important type of knowledge that guides spatial reasoning (and therefore image interpretation) consists of spatial relations, as advocated by works in many domains, such as philosophy, linguistics, perception, cognition, robotics, artificial intelligence, geographic information systems (GIS), or computer vision. The research focuses on image interpretation based on prior knowledge on the spatial organization of the observed structures. Alternative approaches such as Markov random fields (MRFs) and probabilistic relaxation enable to introduce and model the spatial context to guide image interpretation. Nevertheless, in these approaches, the formal models and the contextual information are both different from the ones in our proposal. Recently, our work on spatial relations has been used by another group in a MRF framework, but usually the context is considered in a much more local way and does not model the whole scene. In this project, we propose to reduce the semantic gap between numerical information contained in the image and higher level concepts by enriching ontologies with a fuzzy formalism layer. Fuzzy representations have several advantages: They allow representing the imprecision which is inherent to the definition of a concept; for instance, the concept “close to’’ is intrinsically vague and imprecise, and its semantics depends on the context in which objects are embedded, on the scale of the objects and of their environment. They allow managing imprecision related to the expert knowledge in the concerned domain. They constitute an adequate framework for knowledge representation and reasoning, reducing the semantic gap between symbolic concepts and numerical information. More specifically, we introduce ontology of spatial relations and propose to enrich it by fuzzy representations of these relations in the spatial domain. The choice of spatial relations is motivated on the one hand by the importance of structural information in image interpretation, and on the other hand by the intrinsically ambiguous nature of most spatial relations. Once linked to anatomy ontology, our enriched ontology exhibits all required characteristics for ontology’s in the domain of biomedicine, according: good lexical coverage, good coverage in terms of relations, compatibility with standards, modularity, and ability to represent variation in reality.

G. Acampora and V. Loia, present the paper is basically organized in three parts. Firstly describe the toolset used to obtain the hardware transparency in fuzzy controller; in the second part, the mobile capability of our multi-agent platform is discussed by underlining the benefits in terms of distributed fuzzy control flow; in the third part we describe how proposal has been extended to support adaptive services by means of customizable actuators that can change the related control functionality. This paper presents a multi agent based framework that allows the designer to achieve three main goals: customize the control strategy on specific hardware through an automatic procedure; distribute fuzzy control activities in order to minimize global deduction time and better exploit the natural distributed knowledge repositories; acquire, at run time, the user’s behaviour and environment status and transform such information into a knowledge based repository useful for adaptive services. As a fundamental step necessary to achieve this objective, an extensible mark-up language (XML)-derived technologies has been adopted. Essentially, XML permits to create data-oriented markup languages, in other words computer languages able to describe information in a designed working context. In the case, the working context is fuzzy logic applied to control systems. Extensible Style sheet Language Transformations (XSLTs) modules are able to convert the FML fuzzy controller in a general purpose computer language using an XSL file containing the translation description. At this level, the control is executable for the hardware.

D. U. Campos-Delgado, M. Hernandez-Ordonez, R. Femat, and A. Gordillo-Moscoso implement the control theory field, the fuzzy logic has emerged as a powerful tool to incorporate expert knowledge about the systems into the controllers design, in particular, the ability of synthesizing expert knowledge in the fuzzy logic framework has raised a lot of attention in the biomedical engineering field. Thus, for example, applications to electro-diagnosis, control of blood pressure during anesthesia, and nutrition advice have been proposed. This work exploits fuzzy-based strategies to regulate the BGL around euglycemia with a MDIR, where a Mamdani-type of fuzzy controller is designed using expert rules about diabetes treatment. Similar, a two-loop structure is adopted for the control scheme in order to improve the glucose regulation due to variability of the human metabolism. The intensive insulin therapy based on a MDIR consists of a combination of RSAI and ILAI, scheduled before each meal (3 doses per day). It is assumed that the patient is in a chronic condition and has a prescribed diet regimen by a physician. Moreover, the algorithm does not rely on a direct glucose prediction or estimation to evaluate the insulin adjustments as in previous approaches, and the two-loop feedback architecture adds robustness to the overall control scheme. The control strategy presented in this work formalizes expert knowledge in the fuzzy logic framework, compared to some other previously published methods that use the expert knowledge in a more ad hoc manner.

Q. Liang, N. N. Karnik, and J. M. Mendel presented that each one of the results that square measure required to implement Associate in Nursing IT2 FLS is often obtained victimization T1 FS arithmetic. The key to doing this can be the Mendel–John illustration Theorem for a T2 FS. We will currently develop Associate in Nursing IT2 FLS during a far easier manner. Since Associate in Nursing IT2 FLS models higher levels of uncertainty than will a T1 FLS, this unveil Associate in Nursing economical manner of developing improved management systems and for modeling human higher cognitive process. we tend to believe that the leads to this paper can create IT2 Sunshine States far more accessible to practitioners of FL since the time and energy currently needed to find out regarding IT2 FLSs is extremely little. we tend to additionally believe that the approach taken during this paper are often accustomed extend several existing T1 FS results to IT2 FSs. whether or not or not comparable results are often obtained for general T2 FLSs is Associate in Nursing open question. Looking back, we tend to believe that requiring someone to use T2 FS arithmetic represents a barrier to the utilization of IT2 FSs and FLSs. Here, we tend to demonstrate that it's spare to require the on top of route; from general T2 FS to IT2 FS, which all of the results that square measure required to implement Associate in Nursing IT2 FLS are often obtained victimization T1 FS arithmetic.

Jerry M. Mendel and Robert I. Bob John described Type-2 fuzzy sets are difficult to understand and use because: (1) the three-dimensional nature of type-2 fuzzy sets makes them very difficult to draw; (2) there is no simple collection of well-defined terms that let us effectively communicate about type-2 fuzzy sets, and to then be mathematically precise about them (terms do exist but have not been precisely defined2 ); (3) derivations of the formulas for the union, intersection, and complement of type-2 fuzzy sets all rely on using Principle, which in itself is a difficult concept (especially for newcomers to FL) and is somewhat ad hoc, so that deriving things using it may be considered problematic; and, (4) using type-2 fuzzy sets is computationally more complicated than using type-1 fuzzy sets. In this paper, we focus on overcoming difficulties 1–3, because doing so makes type-2 fuzzy sets easy to use and understand. Difficulty 4 is the price one must pay for achieving better performance in the face of uncertainties, and is analogous to using probability rather than determinism.3 we only touch on it very briefly in this paper. They seem to be applicable when: (1) the data-generating system is known to be time-varying but the mathematical description of the time-variability is unknown (e.g., as in mobile communications); (2) measurement noise is non stationary and the mathematical description of the non-stationary is unknown (e.g., as in a time-varying SNR); (3) features in a pattern recognition application have statistical attributes that are non-stationary and the mathematical descriptions of the non-stationeries are unknown; (4) knowledge is mined from a group of experts using questionnaires that involve uncertain words; and (5) linguistic terms are used that have a non-measurable domain.

J. M. Mendel, R. I. John, and F. Liu presented that all of the results that are needed to implement an IT2 FLS can be obtained using T1 FS mathematics. The key to doing this is the Mendel–John Representation Theorem for a T2 FS. We can now develop an IT2 FLS in a much more straightforward way. Since an IT2 FLS models higher levels of uncertainty than does a T1 FLS, this opens up an efficient way of developing improved control systems and for modeling human decision making. We believe that the results in this paper will make IT2 FLSs much more accessible to practitioners of FL since the time and effort now required to learn about IT2 FLSs is very small. We also believe that the approach taken in this paper can be used to extend many existing T1 FS results to IT2 FSs. Whether or not comparable results can be obtained for general T2 FLSs is an open question. In retrospect, we believe that requiring a person to use T2 FS mathematics represents a barrier to the use of IT2 FSs and FLSs. Here, we demonstrate that it is unnecessary to take the above route; from general T2 FS to IT2 FS, and that all of the results that are needed to implement an IT2 FLS can be obtained using T1 FS mathematics.

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