A Typology of Temporal Data Imperfection

International audienceTemporal data may be subject to several types of imperfection (e.g., uncertainty, imprecision..). In this context, several typologies of data imperfections have been already proposed. However, these typologies cannot be applied to temporal data because of the complexity of this type of data and the specificity that it contains. Besides, to the best of our knowledge, there is no typology of temporal data imperfections. In this paper, we propose a typology of temporal data imperfections. Our typology is divided into direct imperfections of both numeric temporal data and natural language based temporal data, indirect imperfections that can be deduced from the direct ones and granularity (i.e., context - dependent temporal data) which is related to several factors that can interfer in specifying the imperfection type such as person’s profile and multiculturalism. We finish by representing an example of imprecise temporal data in PersonLink ontology

Text2Onto - A Framework for Ontology Learning and Data-driven Change Discovery

Cimiano P, Völker J. Text2Onto - A Framework for Ontology Learning and Data-driven Change Discovery. In: Montoyo A, Munoz R, Metais E, eds. Natural language processing and information systems : 10th International Conference on Applications of Natural Language to Information Systems, NLDB 2005, Alicante, Spain, June 15 - 17, 2005 ; proceedings. Lecture notes in computer science, 3513. Springer; 2005: 227-238

Representing Imprecise Time Intervals in OWL 2

International audienceRepresenting and reasoning on imprecise temporal information is a common requirement in the field of Semantic Web. Many works exist to represent and reason on precise temporal information in OWL; however, to the best of our knowledge, none of these works is devoted to imprecise temporal time intervals. To address this problem, we propose two approaches: a crisp-based approach and a fuzzy-based approach. (1) The first approach uses only crisp standards and tools and is modelled in OWL 2. We extend the 4D-fluents model, with new crisp components, to represent imprecise time intervals and qualitative crisp interval relations. Then, we extend the Allen’s interval algebra to compare imprecise time intervals in a crisp way and inferences are done via a set of SWRL rules. (2) The second approach is based on fuzzy sets theory and fuzzy tools and is modelled in Fuzzy-OWL 2. The 4D-fluents approach is extended, with new fuzzy components, in order to represent imprecise time intervals and qualitative fuzzy interval relations. The Allen’s interval algebra is extended in order to compare imprecise time intervals in a fuzzy gradual personalized way. Inferences are done via a set of Mamdani IF-THEN rules

Les mecanismes d'inference et l'explication du raisonnement dans le systeme expert SECSI

SIGLECNRS T Bordereau / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Systèmes d'aide à la décision et entrepôts de données

Systèmes d'aide à la décision et entrepôts de données

Le potentiel de la technologie

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Elaboration des entrepôts de données

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