Logic Programming Applications: What Are the Abstractions and Implementations?

This article presents an overview of applications of logic programming, classifying them based on the abstractions and implementations of logic languages that support the applications. The three key abstractions are join, recursion, and constraint. Their essential implementations are for-loops, fixed points, and backtracking, respectively. The corresponding kinds of applications are database queries, inductive analysis, and combinatorial search, respectively. We also discuss language extensions and programming paradigms, summarize example application problems by application areas, and touch on example systems that support variants of the abstractions with different implementations

Declarative Rules for Annotated Expert Knowledge in Change Management

In this paper, we use declarative and domain-specific languages for representing expert knowledge in the field of change management in organisational psychology. Expert rules obtained in practical case studies are represented as declarative rules in a deductive database. The expert rules are annotated by information describing their provenance and confidence. Additional provenance information for the whole - or parts of the - rule base can be given by ontologies. Deductive databases allow for declaratively defining the semantics of the expert knowledge with rules; the evaluation of the rules can be optimised and the inference mechanisms could be changed, since they are specified in an abstract way. As the logical syntax of rules had been a problem in previous applications of deductive databases, we use specially designed domain-specific languages to make the rule syntax easier for non-programmers. The semantics of the whole knowledge base is declarative. The rules are written declaratively in an extension datalogs of the well-known deductive database language datalog on the data level, and additional datalogs rules can configure the processing of the annotated rules and the ontologies

Probabilistic Data with Continuous Distributions

Statistical models of real world data typically involve continuous probability distributions such as normal, Laplace, or exponential distributions. Such distributions are supported by many probabilistic modelling formalisms, including probabilistic database systems. Yet, the traditional theoretical framework of probabilistic databases focusses entirely on finite probabilistic databases. Only recently, we set out to develop the mathematical theory of infinite probabilistic databases. The present paper is an exposition of two recent papers which are cornerstones of this theory. In (Grohe, Lindner; ICDT 2020) we propose a very general framework for probabilistic databases, possibly involving continuous probability distributions, and show that queries have a well-defined semantics in this framework. In (Grohe, Kaminski, Katoen, Lindner; PODS 2020) we extend the declarative probabilistic programming language Generative Datalog, proposed by (B\'ar\'any et al.~2017) for discrete probability distributions, to continuous probability distributions and show that such programs yield generative models of continuous probabilistic databases