2 research outputs found
Temporal Data Modeling and Reasoning for Information Systems
Temporal knowledge representation and reasoning is a major research field in Artificial
Intelligence, in Database Systems, and in Web and Semantic Web research. The ability to
model and process time and calendar data is essential for many applications like appointment
scheduling, planning, Web services, temporal and active database systems, adaptive
Web applications, and mobile computing applications. This article aims at three complementary
goals. First, to provide with a general background in temporal data modeling
and reasoning approaches. Second, to serve as an orientation guide for further specific
reading. Third, to point to new application fields and research perspectives on temporal
knowledge representation and reasoning in the Web and Semantic Web
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A theory of time and temporal incidence based on instants and periods
Time is fundamental in representing and reasoning about changing domains. A proper temporal representation requires characterizing two notions: (1) time itself, and (2) temporal incidence, i.e. the domain-independent properties for the truth-value of fluents and events through-out time. Formally defining them involves some problematic issues such as (i) the expression of instantaneous events and instantaneous holding of fluents, (ii) the dividing instant problem and (iii) the formalization of the properties for non-instantaneous holding of fluents.This paper discusses how previous attempts fail to address all these issues and presents a simple theory of time and temporal incidence which satisfactorily overcomes all of them.Our theory of time, called IP, is based on having instants and periods at equal level. Our theory of temporal incidence is defined upon IP. Its key insight is the distinction between continuous and discrete fluents