An integrated development environment for probabilistic relational reasoning

This article presents KReator, a versatile integrated development environment for probabilistic inductive logic programming currently under development. The area of probabilistic inductive logic programming (or statistical relational learning) aims at applying probabilistic methods of inference and learning in relational or first-order representations of knowledge. In the past ten years the community brought forth a lot of proposals to deal with problems in that area, which mostly extend existing propositional probabilistic methods like Bayes Nets and Markov Networks on relational settings. Only few developers provide prototypical implementations of their approaches and the existing applications are often difficult to install and to use. Furthermore, due to different languages and frameworks used for the development of different systems the task of comparing various approaches becomes hard and tedious. KReator aims at providing a common and simple interface for representing, reasoning and learning with different relational probabilistic approaches. It is a general integrated development environment which enables the integration of various frameworks within the area of probabilistic inductive logic programming and statistical relational learning. Currently, KReator implements Bayesian logic programs, Markov logic networks and relational maximum entropy under grounding semantics. More approaches will be implemented in the near future or can be implemented by researchers themselves as KReator is open-source and available under public license. In this article, we provide some background on probabilistic inductive logic programming and statistical relational learning and illustrate the usage of KReator on several examples using the three approaches currently implemented in KReator. Furthermore, we give an overview on its system architecture.