Conceptual Structures: Logical, Linguistic, and by Keith Devlin (auth.), Bernhard Ganter, Guy W. Mineau (eds.)

By Keith Devlin (auth.), Bernhard Ganter, Guy W. Mineau (eds.)

Computerscientistscreatemodelsofaperceivedreality.ThroughAItechniques, those versions objective at offering the fundamental aid for emulating cognitive - havior comparable to reasoning and studying, that's one of many major ambitions of the AI examine e?ort. Such computing device versions are shaped throughout the interplay of varied acquisition and inference mechanisms: notion, notion studying, conceptual clustering, speculation checking out, probabilistic inference, etc., and are represented utilizing di?erent paradigms tightly associated with the methods that use them. between those paradigms allow us to cite: organic versions (neural nets, genetic programming), logic-based versions (?rst-order good judgment, modal common sense, rule-based s- tems), digital fact versions (object platforms, agent systems), probabilistic m- els(Bayesiannets,fuzzylogic),linguisticmodels(conceptualdependencygraphs, language-based representations), and so on. OneofthestrengthsoftheConceptualGraph(CG)theoryisitsversatilityin phrases of the illustration paradigms less than which it falls. it may be seen and accordingly used, below di?erent illustration paradigms, which makes it a p- ular selection for a wealth of purposes. Its complete coupling with di?erent cognitive strategies result in the outlet of the ?eld towards comparable study groups comparable to the outline common sense, Formal inspiration research, and Computational Linguistic groups. We now see a growing number of examine effects from one group enhance the opposite, laying the rules of universal philosophical grounds from which a profitable synergy can emerge.

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Extra resources for Conceptual Structures: Logical, Linguistic, and Computational Issues: 8th International Conference on Conceptual Structures, ICCS 2000, Darmstadt, Germany, August 14-18, 2000. Proceedings

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Conceptual Structures: Information Processing in Mind and Machine. The Systems Programming Series. Addison-Wesley, Reading, MA, 1984. [13] N. A. Streitz, J. M. Haake, J. Hannemann, A. Lemke, W. Schuler, H. Sch¨ utt, and M. Th¨ uring. SEPIA – A Cooperative Hypermedia Authoring System. In D. Lucarella, J. Nanard, M. Nanard, and P. Paolini, editors, Proceedings of ECHT’92, the Fourth ACM Conference on Hypertext, pages 11-22, Milano, Italy, 1992. ACM. Z. [14] J. M. Swales and C. B. Feak. Academic Writing for Graduate Students – A Course for Nonnative Speakers of English.

1. Schema Problem-Solution The Problem-Solution proposal references a problem, which must be of relevance to the audience (the target of the proposal), that is: they must have experienced the event as a problem. The author proposes a solution that solves the problem. Own literature may be cited to out-source detailed discussions or to add weight to the solution. Other literature is used to ground the problem and parts of the solution. 2 Schemata Provide a Guide to Requirements and Structure Guided by the pattern, the author expands the starting concept [Content] with some schemata to build the model of the content to be described in the document.

When different interpretations are possible, the user should be alerted to make a choice. This last facility, detailed in [7], is particularly interesting when the exploited ontologies reuse a natural language lexical database such as WordNet8 : it spares the user the complex and tedious work of declaring and organizing each term used. This facility (along with high-level notations and interfaces) seems an essential step to encourage Web (human) users to build knowledge representations. Similar ideas for the exploitation of lexical databases such as WordNet are developed in [3].

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