Reference: Shahar, Y. Knowledge-Based Temporal Interpolation. Knowledge Systems Laboratory, Medical Computer Science, January, 1996.
Abstract: Temporal interpolation is the task of bridging gaps between time-oriented data or abstracted concepts in a context-sensitive manner. It is one of the subtasks important for solving the temporal-abstraction task_abstraction of higher-level concepts from time-stamped data. We present a knowledge-based approach to the temporal-interpolation task and discuss in detail the precise knowledge required by that approach, its theoretical foundations, and the implications of the approach. The temporal-interpolation computational mechanism we discuss relies, among other knowledge types, on a temporal-persistence model. The temporal-persistence model employs local temporal-persistence functions that are temporally bidirectional (i.e., extend a belief measure in a predicate both into the future and into the past) and global, maximal-gap temporal-persistence functions that bridge gaps between interval-based predicates. We investigate quantitative and qualitative properties of persistence functions. Our goal is to formulate the knowledge required for solving the temporal-abstraction task, and in particular the temporal-interpolation subtask, so as to facilitate the acquisition of that knowledge, its maintenance, its reuse for the same task in different domains, and its sharing among different applications in the same domain.