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

th The 18 overseas convention on Conceptual buildings (ICCS 2010) was once the most recent in a chain of annual meetings which have been held in Europe, A- tralia, and North the United States because 1993. the point of interest of the convention has been the illustration and research of conceptual wisdom for learn and sensible 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 will 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 wider diversity of theories and comparable practices, between them other kinds of graph-based reasoning platforms like RDF or existential graphs, formal idea research, Semantic internet applied sciences, ontologies, idea mapping and extra. therefore, ICCS represents a relations of techniques concerning conc- tualstructuresthatbuild onthesuccesseswithtechniquesderivedfromarti?cial intelligence, wisdom illustration and reasoning, utilized arithmetic and lattice thought, computational linguistics, conceptual modeling and layout, d- grammatic reasoning and common sense, 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 means and function of computing infrastructure have additionally - fected the character of data illustration and reasoning (KRR) structures, moving their concentration towards representational energy and execution functionality. consequently, KRR learn is now confronted with a problem of constructing wisdom illustration and reasoning buildings optimized for such reasonings.

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

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T1 = (a, b, c), translated into F1 = [ : a]-> (b)-> [ : c]; 3. t2 = (a, b, b), translated into F2 = [ : a]-> (b)-> [ : b]. -F. Baget et al. One has t2 s tQ but not t1 s tQ since models of t1 that map b and c to distinct elements are not models of tQ . However, there is a BG homomorphism from Q to both F1 and F2 . The trouble is that the translation of tQ into a BG does not keep the information that the object and the predicate of the triple have the same variable name. We present here two solutions solving that problem: in the first, we change the structure of the built BG, while in the second we restrict the domain of the translation to a subset of RDF.

ICCS 2000. LNAI, vol. 1867, pp. 172–192. Springer, Heidelberg (2000) 10. : Conceptual Structures: Information Processing in Mind and Machine. fr Abstract. In this paper, we explore the expressivity of default CG rules (a CGoriented subset of Reiter’s default logics) through two applications. In the first one, we show that default CG rules provide a unifying framework for CG rules as well as polarized CGs (CGs with atomic negation). This framework allows us to study decidable subclasses of a new language mixing CG rules with atomic negation.

4 Evaluation In-depth experiments and assessment of our present IE procedures were performed using a corpus of 197 PRs as a training set and another 1500 PRs as a test set [13]. The corpus contains anonymised PRs which include phrases discussing the patient sex, age and diabetes duration. The extraction of these attributes was evaluated as well. There are few PRs without any description of organ status but they are removed from the evaluation figures in Table 1. The accuracy is measured by the precision (percentage of correctly extracted entities as a subset of all extracted entities), recall (percentage correctly extracted entities as a subset of all entities available in the corpus) and their harmonic mean F − measure: F = 2 * P recision * Recall / (P recision + Recall).

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