Knowledge Graph Refinement: A Survey of Approaches and Evaluation Methods
Knowledge Graph Refinement: A Survey of Approaches and Evaluation Methods
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DOI：
10.3233/sw-160218
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发表时间：
2017-01-01
期刊：
影响因子：
3
通讯作者：
Paulheim, Heiko
中科院分区：
文献类型：
--
作者：
Paulheim, Heiko
近年来，各种各样的网络知识图谱，包括免费的和商业的，已经被创建出来了。虽然谷歌在2012年创造了“知识图谱”这个术语，但也有一些公开可用的知识图谱，其中最突出的是DBpedia、YAGO和Freebase。这些图表通常是由半结构化的知识构成的，比如维基百科，或者是通过统计和语言方法的结合从网络上获取的。其结果是大规模的知识图，试图在完整性和正确性之间做出良好的权衡。为了进一步提高知识图的实用性，人们提出了各种改进方法，试图推断和添加图中缺失的知识，或识别错误的信息片段。在本文中，我们对这些知识图谱精化方法进行了调查，并对所提出的方法和所使用的评估方法进行了双重审视。
In the recent years, different Web knowledge graphs, both free and commercial, have been created. While Google coined the term "Knowledge Graph" in 2012, there are also a few openly available knowledge graphs, with DBpedia, YAGO, and Freebase being among the most prominent ones. Those graphs are often constructed from semi-structured knowledge, such as Wikipedia, or harvested from the web with a combination of statistical and linguistic methods. The result are large-scale knowledge graphs that try to make a good trade-off between completeness and correctness. In order to further increase the utility of such knowledge graphs, various refinement methods have been proposed, which try to infer and add missing knowledge to the graph, or identify erroneous pieces of information. In this article, we provide a survey of such knowledge graph refinement approaches, with a dual look at both the methods being proposed as well as the evaluation methodologies used.