Download Conceptual Structures: From Information to Intelligence: by Michel Chein (auth.), Madalina Croitoru, Sébastien Ferré, PDF

By Michel Chein (auth.), Madalina Croitoru, Sébastien Ferré, Dickson Lukose (eds.)

th The 18 overseas convention on Conceptual constructions (ICCS 2010) was once the newest in a sequence of annual meetings which were held in Europe, A- tralia, and North the US due to the fact 1993. the focal point of the convention has been the illustration and research of conceptual wisdom for study and useful program. ICCS brings jointly researchers and practitioners in details and computing device sciences in addition to social technological know-how to discover novel ways in which c- ceptual constructions should be deployed. coming up from the examine on wisdom illustration and reasoning with conceptual graphs, through the years ICCS has broadened its scope to incorporate in- vations from a much broader variety of theories and similar practices, between them different kinds of graph-based reasoning structures like RDF or existential graphs, formal inspiration research, Semantic net applied sciences, ontologies, thought mapping and extra. consequently, ICCS represents a relatives of ways with regards to conc- tualstructuresthatbuild onthesuccesseswithtechniquesderivedfromarti?cial intelligence, wisdom illustration and reasoning, utilized arithmetic and lattice idea, computational linguistics, conceptual modeling and layout, d- grammatic reasoning and good judgment, clever structures and information administration. The ICCS 2010 topic “From details to Intelligence” tricks at unve- ing the reasoning features of conceptual buildings. certainly, advancements in garage ability and function of computing infrastructure have additionally - fected the character of data illustration and reasoning (KRR) structures, transferring their concentration towards representational energy and execution functionality. accordingly, KRR study is now confronted with a problem of constructing wisdom illustration and reasoning constructions optimized for such reasonings.

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Additional resources for Conceptual Structures: From Information to Intelligence: 18th International Conference on Conceptual Structures, ICCS 2010, Kuching, Sarawak, Malaysia, July 26-30, 2010. Proceedings

Example text

These heuristic procedures essentially facilitate the template filling. For instance, the cicatrix in the sample text 1 will be stored as a value in the template slot for scars at the neck. Otherwise, due to linguistic considerations, the cicatrix could be wrongly related to the thyroid gland which is discussed in the same sentence. But the latter choice is incorrect for the PRs of diabetic patients where only skin scars are described. In this way the predefined template slots for neck and thyroid gland enable to constraint and shape the information structuring.

We do not allow for anonymous classes or properties: for this reason, we forbid ontological triples containing a blank either as a subject or as an object, as well as factual triples containing a blank as a property, or as an object when the predicate is rdf:type. Finally, the separability condition has to be fulfilled: a given SU can appear only in one of the categories “class” (SUc), “property” (SUp) and “instance” (SUi). In terms of CGs, this condition states that the sets TC , TR and I are pairwise disjoint.

Creation of the concept types rdfs:Resource, rdfs:Literal and rdfs:Datatype, as well as the relation type rdf:Property(rdfs:Resource, rdfs:Resource) 2. Treatment of ontological triples: – (SUc, rdf:type, rdfs:Class) → addition of the concept type SUc – (SUc1, rdfs:subClassOf, SUc2) → addition of concept types SUc1 and SUc2, where SUc1 ≤ SUc2 Translations between RDF(S) and Conceptual Graphs 39 – (SUp, rdf:type, rdf:Property) → addition of the relation type SUp – (SUp1, rdfs:subPropertyOf, SUp2) → addition of binary relations SUp1 and SUp2 with SUp1 ≤ SUp2 – (SUp, rdfs:domain, SUc) → addition of the binary relation type SUp, of the concept type SUc, and treatment of the domain information (as explained above) – (SUp, rdfs:range, SUc) → similar to above 3.

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