学习在 PRISM 中排名,International Journal of Approximate Reasoning

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学习在 PRISM 中排名
International Journal of Approximate Reasoning ( IF 3.2 ) Pub Date : 2018-02-01 , DOI: 10.1016/j.ijar.2017.11.011
Ryosuke Kojima , Taisuke Sato

摘要与命题相关的学习参数是概率逻辑编程 (PLP) 的主要任务之一，PLP 的学习算法主要是基于最大似然估计或判别标准的优化而开发的。本文探索了另一种参数学习的创新方法，学习排序或排序学习，主要在偏好学习领域进行了研究。我们将学习排名与 PLP 中开发的技术相结合，使后者适用于各种排名问题，例如信息检索。我们在基于分布语义的 PLP 系统 PRISM 中实现了我们的方法。支持期望最大化算法等多种参数学习算法，变分贝叶斯算法和维特比训练算法通过将它们映射到称为解释图的单个数据结构来有效地训练。为了通过学习排序来确保与当前 PRISM 中的参数学习相同的效率，我们引入了一种基于梯度的学习方法，该方法利用了解释图上的动态规划。本文还介绍了三个实验结果。第一个是使用合成数据来检查所提出方法的学习行为。第二个使用知识库（知识图）并将等级学习应用于 DistMult 模型，以决定实体之间的关系是否存在。最后一个解决了通过概率上下文无关文法进行解析的问题，该文法的参数是通过等级学习从树语料库中学习的。这些实验成功地证明了学习在 PLP 中排名的潜力和有效性。我们计划在不久的将来发布增强学习排名能力的新版 PRISM。

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Learning to rank in PRISM

Abstract Learning parameters associated with propositions is one of the main tasks of probabilistic logic programming (PLP), and learning algorithms for PLP have been primarily developed based on maximum likelihood estimation or the optimization of discriminative criteria. This paper explores yet another innovative approach to parameter learning, learning to rank or rank learning, that has been studied mainly in the field of preference learning. We combine learning to rank with techniques developed in PLP to make the latter applicable to a variety of ranking problems such as information retrieval. We implement our approach in PRISM, a PLP system based on the distribution semantics. It supports many parameter learning algorithms such as the expectation maximization algorithm, the variational Bayes algorithm and an algorithm for Viterbi training efficiently by mapping them onto a single data structure called explanation graph. To ensure the same efficiency for parameter learning by learning to rank as in the current PRISM, we introduce a gradient-based learning method that takes advantage of dynamic programming on the explanation graph. This paper also presents three experimental results. The first one is with synthetic data to check the learning behaviors of the proposed approach. The second one uses a knowledge base (knowledge graph) and apply rank learning to a DistMult model for the task of deciding whether relations over entities exist or not. The last one tackles the problem of parsing by a probabilistic context free grammar whose parameters are learned from a tree corpus by rank learning. These experiments successfully demonstrated the potential and effectiveness of learning to rank in PLP. We plan to release a new version of PRISM augmented with the ability of learning to rank in the near future.

更新日期：2018-02-01

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