A Probabilistic Interpretation for a Geometric Similarity Measure

摘要

A Boolean logic-based evaluation of a database query returns true on match and false on mismatch. Unfortunately, there are many application scenarios where such an evaluation is not possible or does not adequately meet user expectations about vague and uncertain conditions. Consequently, there is a need for incorporating impreciseness and proximity into a logic-based query language. In this work we propose a probabilistic interpretation for our query language CQQL which is based on a geometric retrieval model. In detail, we show that the CQQL can evaluate arbitrary similarity conditions in a probabilistic fashion. Furthermore, we lay a theoretical foundation for the combination of CQQL with other probabilistic semantics.