Knowledge-Based Temporal Interpolation

Temporal interpolation is the task of bridging gaps between time-oriented concepts in a context-sensitive manner. It is a subtask important for solving the temporal-abstraction task-abstraction of interval-based, 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 the quantitative and qualitative properties implied by both types of persistence functions. We have implemented our approach and evaluated it in several different domains. We discuss its implications for acquisition, maintenance, reuse, and sharing of temporal-abstraction knowledge.

Authors

• Yuval Shahar

Keywords

• Interpolation
• Medical treatment
• Protocols
• Biomedical informatics
• Knowledge based systems
• Laboratories
• Bridges
• Time measurement
• Computational modeling
• Ontologies
• Temporal Interpolation
• Local Function
• Time-stamped Data
• Bridging Gaps
• Time Interval
• Ontology
• Special Case
• Decay Rate
• Similar Mean
• Domain Experts
• Myelosuppression
• Interval Length
• Decay Function
• National Library Of Medicine
• Temporal Gap
• Intervals For Parameters
• Temporal Constraints
• Maximum Gap
• Parameter Point
• Interpolation Operator
• Local Persistence
• True Propositions
• Abstract Setting