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A new truth discovery method for resolving object conflicts over Linked Data with scale-free property

机译：解决对象的新真理发现方法，其与无尺度属性的链接数据冲突

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摘要

Considerable effort has been exerted to increase the scale of Linked Data. However, an inevitable problem arises when dealing with data integration from multiple sources. Various sources often provide conflicting objects for a certain predicate of the same real-world entity, thereby causing the so-called object conflict problem. Existing truth discovery methods cannot be trivially extended to resolve object conflict problems because Linked Data has a scale-free property, i.e., most of the sources provide few objects, whereas only a few sources have numerous objects. In this study, we propose a novel approach called TruthDiscover to determine the most trustworthy object in Linked Data with a scale-free property. More specifically, TruthDiscover consists of two core components: Priori Belief Estimation for smoothing the trustworthiness of sources by leveraging the topological properties of the Source Belief Graph, and Truth Computation for inferencing the trustworthiness of source and trust value of an object. Experimental results conducted on six datasets show that TruthDiscover achieves higher accuracy than existing approaches, and it is robust and consistent in various domains.
机译：已经施加了相当大的努力来增加链接数据的规模。但是，在处理来自多个来源的数据集成时出现不可避免的问题。各种来源通常为相同的真实实体的某个谓词提供冲突的对象，从而导致所谓的对象冲突问题。现有的真实性发现方法不能术语扩展以解决对象冲突问题，因为链接数据具有无垢的属性，即，大多数源提供少量对象，而只有少数源具有许多对象。在这项研究中，我们提出了一种名为“真实性的新方法，以确定具有无尺寸无垢的数据中的最值得信赖的对象。更具体地说，真实性发现由两个核心组成部分组成：通过利用源信仰图的拓扑特性和说明对象来源的可信度和信任价值的真实性计算来平滑来源的可信度来平滑来源的认可估计。在六个数据集上进行的实验结果表明，判断域名比现有方法达到更高的准确性，并且在各个域中具有稳健且一致。

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