An integrated development environment for probabilistic relational reasoning
An integrated development environment for probabilistic relational reasoning
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DOI：
10.1093/jigpal/jzs009
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发表时间：
2012-10
期刊：
影响因子：
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通讯作者：
Marc Finthammer;Matthias Thimm
中科院分区：
文献类型：
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作者：
Marc Finthammer;Matthias Thimm
本文介绍了KReator，一个通用的概率归纳逻辑编程集成开发环境，目前正在开发中。概率归纳逻辑编程（或统计关系学习）领域旨在将概率推理和学习方法应用于知识的关系或一阶表示。在过去的十年里，社会上提出了许多建议来处理这方面的问题，主要是扩展现有的命题概率方法，如贝叶斯网和马尔可夫网络的关系设置。只有少数开发人员提供了他们的方法的原型实现，现有的应用程序通常很难安装和使用。此外，由于用于开发不同系统的不同语言和框架，比较各种方法的任务变得困难和乏味。KReator旨在提供一个通用且简单的接口，用于使用不同的关系概率方法进行表示，推理和学习。它是一个通用的集成开发环境，可以集成概率归纳逻辑编程和统计关系学习领域内的各种框架。目前，KReator实现了贝叶斯逻辑程序，马尔可夫逻辑网络和基于语义的关系最大熵。更多的方法将在不久的将来实现，或者可以由研究人员自己实现，因为KReator是开源的，可以在公共许可下使用。在本文中，我们提供了概率归纳逻辑编程和统计关系学习的一些背景知识，并使用KReator中当前实现的三种方法在几个示例中说明了KReator的使用。此外，我们给出了一个概述其系统架构。
This paper 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 paper, 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.