Abstract

While there has been a trend in the last decades for publishing large-scale and highly-interconnected Knowledge Graphs (KGs), their users often get overwhelmed by the task of understanding their content as a result of their size and complexity. Data profiling approaches have been proposed to summarize large KGs into concise and meaningful representations, so that they can be better explored, processed, and managed. Profiles based on schema patterns represent each triple in a KG with its schema-level counterpart, thus covering the entire KG with profiles of considerable size. In this paper, we provide empirical evidence that profiles based on schema patterns, if explored with suitable mechanisms, can be useful to help users understand the content of big and complex KGs. ABSTAT provides concise pattern-based profiles and comes with faceted interfaces for profile exploration. Using this tool we present a user study based on query completion tasks. We demonstrate that users who look at ABSTAT profiles formulate their queries better and faster than users browsing the ontology of the KGs. The latter is a pretty strong baseline considering that many KGs do not even come with a specific ontology to be explored by the users. To the best of our knowledge, this is the first attempt to investigate the impact of profiling techniques on tasks related to knowledge graph understanding with a user study.

中文翻译：

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使用 ABSTAT 配置文件了解知识图的结构

摘要

尽管过去几十年出现了发布大规模且高度互连的知识图 (KG) 的趋势，但由于其规模和复杂性，其用户常常因理解其内容的任务而不知所措。人们提出了数据分析方法，将大型知识图谱总结为简洁且有意义的表示形式，以便更好地探索、处理和管理它们。基于模式模式的配置文件表示知识图谱中的每个三元组及其模式级对应项，从而用相当大的配置文件覆盖整个知识图谱。在本文中，我们提供的经验证据表明，如果使用适当的机制进行探索，基于模式模式的配置文件可以有助于帮助用户理解大型且复杂的知识图谱的内容。ABSTAT 提供简洁的基于模式的配置文件，并附带用于配置文件探索的多面界面。使用这个工具，我们提出了基于查询完成任务的用户研究。我们证明，查看 ABSTAT 配置文件的用户比浏览知识图谱本体的用户更好、更快地制定查询。考虑到许多知识图谱甚至没有提供用户探索的特定本体，后者是一个相当强大的基线。据我们所知，这是首次尝试通过用户研究来调查分析技术对与知识图理解相关的任务的影响。