利用源对象网络解决关联数据中的对象冲突
Exploiting Source-Object Network to Resolve Object Conflicts in Linked Data
摘要：
已经做出了相当大的努力来增加关联数据的规模。然而，在处理多个数据源的数据集成时，不可避免的问题是多个不同的数据源经常为同一个实体的某个谓词提供冲突的对象，即对象冲突问题。目前，对象冲突问题在关联数据社区中没有得到足够的重视。在本文中，我们首先将对象冲突解决问题形式化为计算在称为源对象网络的异构信息网络上的变量的联合分布，该网络成功地捕获了来自对象和关联数据源的所有相关性。然后，我们介绍了一种新的方法，称为ObResolution（对象分辨率）的网络效应的基础上，从多个冲突的对象，以确定一个真正的对象。ObResolution采用成对马尔可夫随机场（pMRF）在一个统一的框架下对所有证据进行建模。在六个真实世界数据集上的大量实验结果表明，我们的方法具有更高的准确性比现有的方法，它是强大的，在各个领域的一致性。\keywords {关联数据、对象冲突、关联数据质量、真相发现
英文摘要：
Considerable effort has been made to increase the scale of Linked Data. However, an inevitable problem when dealing with data integration from multiple sources is that multiple different sources often provide conflicting objects for a certain predicate of the same real-world entity, so-called object conflicts problem. Currently, the object conflicts problem has not received sufficient attention in the Linked Data community. In this paper, we first formalize the object conflicts resolution problem as computing the joint distribution of variables on a heterogeneous information network called the Source-Object Network, which successfully captures the all correlations from objects and Linked Data sources. Then, we introduce a novel approach based on network effects called ObResolution(Object Resolution), to identify a true object from multiple conflicting objects. ObResolution adopts a pairwise Markov Random Field (pMRF) to model all evidences under a unified framework. Extensive experimental results on six real-world datasets show that our method exhibits higher accuracy than existing approaches and it is robust and consistent in various domains. \keywords{Linked Data, Object Conflicts, Linked Data Quality, Truth Discovery
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
