Practical Probabilistic Reasoning in Web Knowledge Graphs

从异构Web源生成所谓的“知识图”最近引起了学术界和工业界的极大关注，研究小组和大型搜索引擎提供商（如Google和Microsoft）已经创建了许多大型、高度可见的知识图。尽管所使用的提取方法的性能有了显着改进，但这些知识图仍然包含许多不一致的地方，这些不一致是由通过提取产生的不正确信息造成的。拟议的项目将研究检查从异质来源提取的真实的世界知识图的一致性的方法。经典的基于逻辑的一致性检查方法在这种情况下具有严重的局限性，因为它们无法解决许多关键问题，包括：（1）通常由提取方法提供的提取事实的确定性的概率度量，（2）知识图中描述的对象的数值属性和约束，以及（3）关于事实有效性的时间信息。在我们以前的工作中，我们已经开发了考虑到事实的概率确定性的方法，通过设计一种用于马尔可夫逻辑网络的模板语言，该模板语言模拟知识图的逻辑语义，并允许我们将知识图中的一致性检查减少到马尔可夫逻辑中的MAP状态推理。在拟议的项目过程中，我们希望扩展这项工作，以支持数值属性和约束，以及时间推理，以更好地解决在真实的世界知识graphs.As的一致性chekcing的挑战的第一步，我们将扩展马尔可夫逻辑网络与数值属性和约束，并开发有效的方法，在扩展的马尔可夫逻辑模型的推理。基于这个基本的扩展，我们将开发一个表达的模板语言，这个扩展模型，使我们能够编码的数字和时间信息，除了逻辑模型，并提供了一个减少一致性chekcing的MAP状态推断扩展马尔可夫逻辑模型。最后，我们要系统地评估模板语言和底层方法的基础上，众所周知的知识图，即DBpedia，YAGO和NELL。在项目的第二次较短的迭代中，将根据评估结果改进方法和语言。除了科学出版物，该项目的具体成果将是RockIT Reasoner for Markov Logic Networks的扩展，以及其现有在线界面的扩展，以将现有知识图映射到项目中开发的模板语言。

The generation of so-called 'Knowledge Graphs' from heterogeneous Web sources has recently attracted significant attention both by academia and industry and a number of large, highly visible knowledge graphs have been created by research groups and large search engine providers such as Google and Microsoft. Despite significant improvements in the performance of the extraction methods used, these knowledge graphs still contain a number of inconsistencies that are caused by incorrect information generation through the extraction. The proposed project will investigate methods for checking the consistency of real world knowledge graphs that have been extracted from heteriogeneous sources. Classical, logic-based methods for consistency checking have serious limitations in this scenrio as they fail to address a number of key issues including: (1) probabilistic measures of certainty for extracted facts that are often provided by teh extraction method, (2) numerical attributes and constraints of objects described in the knowledge graph and (3) temporal information about the validity of facts. In our previous work, we have already developed methods that take probabilistic certainty of facts into account by designing a template language for Markov-Logic Networks that simulates the logical semantics of knowledge graphs and allows us to reduce consistency checking in knowledge graphs to MAP-State inference in Markov Logic. In the course of the proposed project, we want to extend this work to support numerical attributes and constraints as well as temporal reasoning to better address the challenges of consistency chekcing in real world knowledge graphs.As a first step, we will extend Markov-Logic Networks with numerical attributes and -constraints and develop efficient methods for reasoning in the extended Markov-Logic model. Based on this fundamental extension, we will develop an expressive template language for this extended model that allows us to encode numerical and temporal information in addition to the logical model and provides a reduction of consistency chekcing to MAP-State inference in the extended Markov-Logic model. Finally, we want to systematically evaluate the template language and the underlying methods on the basis of well known knowledge graphs, i.e. DBpedia, YAGO and NELL. In a second shorter iteration of the project, the methods and the language will be improved based on the results of this evaluation. Besides scientific publications, concrete results of the project will be an extension of the RockIT Reasoner for Markov Logic Networks and an extension of its existing online interface to map existing knowledge graphs to the template language developed in the proejct.