Practical Probabilistic Reasoning in Web Knowledge Graphs

从不同的Web源中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谷歌和微软已经创建了许多大型的、高度可见的知识图。尽管所使用的提取方法的性能有了显著改进，但这些知识图仍然包含一些不一致之处，这些不一致是由于通过提取生成不正确的信息造成的。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有严重的局限性，因为它们不能解决一些关键问题，包括：(1)提取方法经常提供的对所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，我们已经通过设计一种马尔可夫逻辑网络的模板语言来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简化为马尔可夫逻辑中的映射状态推理，从而开发出考虑事实的概率确定性的方法。在提出的项目过程中，我们希望将这项工作扩展到支持数值属性和约束以及时态推理，以更好地解决现实世界知识图中一致性检查的挑战。作为第一步，我们将使用数值属性和约束扩展马尔可夫逻辑网络，并在扩展的马尔可夫逻辑模型中开发高效的推理方法。基于这一基本扩展，我们将为这个扩展模型开发一个可表达的模板语言，它允许我们在逻辑模型之外对数字和时间信息进行编码，并在扩展的马尔可夫逻辑模型中减少对映射状态推理的一致性检查。最后，基于著名的知识图DBpedia、Yago和Nell，对模板语言和底层方法进行了系统的评估。在该项目的第二次较短的迭代中，将根据这次评价的结果改进方法和语言。除了科学出版物外，该项目的具体成果将是马尔可夫逻辑网络的Rockit推理机的扩展，以及其现有在线界面的扩展，以将现有的知识图表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.