934 resultados para Knowledge representation and reasoning


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Includes bibliographical references and index.

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Includes bibliographical references and index.

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A social Semantic Web empowers its users to have access to collective Web knowledge in a simple manner, and for that reason, controlling online privacy and reputation becomes increasingly important, and must be taken seriously. This chapter presents Fuzzy Cognitive Maps (FCM) as a vehicle for Web knowledge aggregation, representation, and reasoning. With this in mind, a conceptual framework for Web knowledge aggregation, representation, and reasoning is introduced along with a use case, in which the importance of investigative searching for online privacy and reputation is highlighted. Thereby it is demonstrated how a user can establish a positive online presence.

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With the rapid growth in the quantity and complexity of scientific knowledge available for scientists, and allied professionals, the problems associated with harnessing this knowledge are well recognized. Some of these problems are a result of the uncertainties and inconsistencies that arise in this knowledge. Other problems arise from heterogeneous and informal formats for this knowledge. To address these problems, developments in the application of knowledge representation and reasoning technologies can allow scientific knowledge to be captured in logic-based formalisms. Using such formalisms, we can undertake reasoning with the uncertainty and inconsistency to allow automated techniques to be used for querying and combining of scientific knowledge. Furthermore, by harnessing background knowledge, the querying and combining tasks can be carried out more intelligently. In this paper, we review some of the significant proposals for formalisms for representing and reasoning with scientific knowledge.

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The topic of designers’ knowledge and how they conduct design process has been widely investigated in design research. Understanding theoretical and experiential knowledge in design has involved recognition of the importance of designers’ experience of experiencing, seeing, and absorbing ideas from the world as points of reference (or precedents) that are consulted whenever a design problem arises (Lawson, 2004). Hence, various types of design knowledge have been categorized (Lawson, 2004), and the nature of design knowledge continues to be studied (Cross, 2006); nevertheless, the study of the experiential aspects embedded in design knowledge is a topic not fully addressed. In particular there has been little emphasis on the investigation of the ways in which designers’ individual experience influences different types of design tasks. This research focuses on the investigation of the ways in which designers inform a usability design process. It aims to understand how designers design product usability, what informs their process, and the role their individual experience (and episodic knowledge) plays within the design process. This paper introduces initial outcomes from an empirical study involving observation of a design task that emphasized usability issues. It discusses the experiential knowledge observed in the visual representations (sketches) produced by designers as part of the design tasks. Through the use of visuals as means to represent experiential knowledge, this paper presents initial research outcomes to demonstrate how designers’ individual experience is integrated into design tasks and communicated within the design process. Initial outcomes demonstrate the influence of designers’ experience in the design of product usability. It is expected that outcomes will help identify the causal relationships between experience, context of use, and product usability, which will contribute to enhance our understanding about the design of user-product interactions.

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N.J. Lacey and M.H. Lee, ?The Implications of Philosophical Foundations for Knowledge Representation and Learning in Agents?, Springer-Verlag Lecture Notes on Artificial Intelligence, Vol 2636 on Adaptive Agents and Multi-Agent Systems, 2002.

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Lacey N and Lee M.H., The Implications of Philosophical Foundations for Knowledge Representation and Learning in Agents, in Proc. AISB?01 Symposium on Adaptive Agents and Multi-agent Systems, York, March 2001, pp13-24.

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Characteristics of speech, especially figures of speech, are used by specific communities or domains, and, in this way, reflect their identities through their choice of vocabulary. This topic should be an object of study in the context of knowledge representation once it deals with different contexts of production of documents. This study aims to explore the dimensions of the concepts of euphemism, dysphemism, and orthophemism, focusing on the latter with the goal of extracting a concept which can be included in discussions about subject analysis and indexing. Euphemism is used as an alternative to a non-preferred expression or as an alternative to an offensive attribution-to avoid potential offense taken by the listener or by other persons, for instance, pass away. Dysphemism, on the other hand, is used by speakers to talk about people and things that frustrate and annoy them-their choice of language indicates disapproval and the topic is therefore denigrated, humiliated, or degraded, for instance, kick the bucket. While euphemism tries to make something sound better, dysphemism tries to make something sound worse. Orthophemism (Allan and Burridge 2006) is also used as an alternative to expressions, but it is a preferred, formal, and direct language of expression when representing an object or a situation, for instance, die. This paper suggests that the comprehension and use of such concepts could support the following issues: possible contributions from linguistics and terminology to subject analysis as demonstrated by Talamo et al. (1992); decrease of polysemy and ambiguity of terms used to represent certain topics of documents; and construction and evaluation of indexing languages. The concept of orthophemism can also serves to support associative relationships in the context of subject analysis, indexing, and even information retrieval related to more specific requests.

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Our approach for knowledge presentation is based on the idea of expert system shell. At first we will build a graph shell of both possible dependencies and possible actions. Then, reasoning by means of Loglinear models, we will activate some nodes and some directed links. In this way a Bayesian network and networks presenting loglinear models are generated.