Reference: Shahar, Y. Dynamic Induction of Temporal Interpretation Contexts. Knowledge Systems Laboratory, Medical Computer Science, January, 1996.
Abstract: The temporal-abstraction task is the task of abstracting higher-level concepts from time-stamped data in a context-sensitive manner. We have developed a knowledge-based framework for solving that task. The framework includes a model of time, parameters, events, and interpretation contexts. A formal specification of a domain's temporal-abstraction knowledge supports acquisition, maintenance, reuse, and sharing of that knowledge. We have defined the knowledge-based temporal-abstraction method, a problem-solving method that decomposes the temporal-abstraction task into five subtasks. These subtasks are solved by five domain-independent temporal-abstraction mechanisms. We present the logical model underlying the representation and runtime formation of interpretation contexts. Interpretation contexts are relevant for abstraction of time-oriented data and are induced dynamically by input data, concluded abstractions, external events, goals of the temporal-abstraction process, and certain combinations of interpretation contexts. Knowledge about interpretation contexts is represented as a context ontology and as a dynamic induction relation over interpretation contexts and other proposition types. Induced interpretation contexts are either basic, composite, generalized, or nonconvex. We discuss several significant advantages of separating explicitly interpretation-context propositions from the propositions inducing them and from the abstractions created within their scope.