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Titlebook: Conceptual Structures: Knowledge Visualization and Reasoning; 16th International C Peter Eklund,Ollivier Haemmerlé Conference proceedings 2

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Conference proceedings 2008ceptual Structure approaches that build on techniques derived from artificial intelligence, knowledge representation, applied mathematics and lattice theory, computational linguistics, conceptual modeling, intelligent systems and knowledge management.
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Web, Graphs and Semantics,mponents can be used to implement such notions as named graphs and properties as resources..In brief, we think that CG are an excellent framework to progress in the Semantic Web because the W3C now considers that RDF graphs are– along with XML trees – one of the two standard formats for the Web.
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Flexible Querying of Fuzzy RDF Annotations Using Fuzzy Conceptual Graphs,aphs having a semantic of preferences are compared with the fuzzy sets in the annotation graphs having a semantic of similarity or imprecision. These comparisons deliver several scores which are used by our flexible querying system to sort the answers according to a total order even if these scores are not commensurable.
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https://doi.org/10.1007/978-3-642-79471-1f PG-Deduction decreases and becomes not more difficult than in simple conceptual graphs. Secondly, we improve the brute-force algorithm with several kinds of techniques based on properties concerning the graph structure and the labels.
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https://doi.org/10.1057/9781137277374e set is large and the user requests valid implications with small premises. Other applications can be seen for formal contexts where the maximal premise size of an implication with proper premise is known, for example multivalued contexts scaled by multiordinal scales only.
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