知识图精化:方法和评价方法综述Knowledge Graph Refinement: A Survey of Approaches and Evaluation Methods |
|
课程网址: | http://videolectures.net/iswc2017_paulheim_knowledge_graph_refine... |
主讲教师: | Heiko Paulheim |
开课单位: | 曼海姆大学 |
开课时间: | 2017-11-28 |
课程语种: | 英语 |
中文简介: | 近年来,已经创建了不同的网络知识图,包括免费的和商业的。虽然谷歌在2012年创造了“知识图”一词,但也有一些公开的知识图,其中最突出的是DBpedia、YAGO和Freebase。这些图表通常是根据维基百科等半结构化知识构建的,或者是通过统计和语言方法的结合从网络上获取的。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。为了进一步增加这种知识图的效用,已经提出了各种细化方法,试图推断并向图中添加缺失的知识,或者识别错误的信息。在本文中,我们对这种知识图精化方法进行了调查,同时对提出的方法和使用的评估方法进行了双重考察。 |
课程简介: | 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. |
关 键 词: | 知识图像; 维基百科; 图表效用 |
课程来源: | 视频讲座网 |
数据采集: | 2023-07-19:chenxin01 |
最后编审: | 2023-07-19:chenxin01 |
阅读次数: | 32 |