10 Years of Probabilistic Querying – What Next?

Abstract

Abstract. Over the past decade, the two research areas of probabilistic databases and probabilistic programming have intensively studied the problem of making structured probabilistic inference scalable, but-so far-both areas developed almost independently of one another. While probabilistic databases have focused on describing tractable query classes based on the structure of query plans and data lineage, probabilistic programming has contributed sophisticated inference techniques based on knowledge compilation and lifted (first-order) inference. Both fields have developed their own variants of-both exact and approximate-top-k algorithms for query evaluation, and both investigate query optimization techniques known from SQL, Datalog, and Prolog, which all calls for a more intensive study of the commonalities and integration of the two fields. Moreover, we believe that natural-language processing and information extraction will remain a driving factor and in fact a longstanding challenge for developing expressive representation models which can be combined with structured probabilistic inference-also for the next decades to come.

Rule-Based Conditioning of Probabilistic Data

“…In probabilistic programming and statistical relational learning, it is common to answer queries of the form P (Q|E), where E denotes evidence [10,11], whereas probabilistic databases typically focus on scalable answering of top-k queries without considering evidence [12]. A notable exception is [13] which accounts for "improbable worlds" during query processing.…”