论文标题
知识的步骤图质量评估
Steps to Knowledge Graphs Quality Assessment
论文作者
论文摘要
例如，在过去的十年中，知识图（kgs）被普及，例如，它们在网络的上下文中广泛使用。 2012年，Google介绍了Google的知识图，该图表用于改善其Web搜索服务。该网络还托管了不同的KGS，例如DBPEDIA和WIKIDATA，它们用于各种应用程序，例如个人助理和提问系统。各种Web应用程序依靠KGS为用户提供简洁，完整，准确和新的答案。但是，这些公里的质量是什么？在哪些情况下，应该使用知识图（kg）？如何评估它们？我们回顾了有关数据，信息，链接数据和kg的质量评估的文献。我们通过添加各种质量维度（QD）和质量指标（QMS）来扩展当前的最新框架。此外，我们提出了一种通用，可根据域或任务定制，以及用于评估KGS质量的实践质量评估框架。
Knowledge Graphs (KGs) have been popularized during the last decade, for instance, they are used widely in the context of the web. In 2012 Google has presented the Google's Knowledge Graph that is used to improve their web search services. The web also hosts different KGs, such as DBpedia and Wikidata, which are used in various applications like personal assistants and question-answering systems. Various web applications rely on KGs to provide concise, complete, accurate, and fresh answer to users. However, what is the quality of those KGs? In which cases should a Knowledge Graph (KG) be used? How might they be evaluated? We reviewed the literature on quality assessment of data, information, linked data, and KGs. We extended the current state-of-the-art frameworks by adding various quality dimensions (QDs) and quality metrics (QMs) that are specific to KGs. Furthermore, we propose a general-purpose, customizable to a domain or task, and practical quality assessment framework for assessing the quality of KGs.
