RDF-Analytics: Interactive Analytics over RDF Knowledge Graphs

Maria-Evangelia Papadaki, Yannis Tzitzikas
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Abstract

The formulation of structured queries in knowledge graphs is a challenging task that presupposes familiarity with the syntax of the query language and the contents of the knowledge graph. To alleviate this difficulty in this paper we introduce RDF-ANALYTICS , a novel system that enables plain users to formulate analytic queries over complex, i.e. not necessarily star-schema based, RDF knowledge graphs. To come up with an intuitive interface, we leverage the familiarity of users with Faceted Search (FS) systems, i.e. we extend FS with actions that enable users to formulate analytic queries, too. Distinctive characteristics of the approach is the ability to include arbitrarily long paths in the analytic query (accompanied with count information), interactive formulation of HAVING restrictions, the support of both Faceted Search (i.e. the locating of the desired resources in a faceted search manner) and analytic queries, and the ability to formulate nested analytic queries. Finally, we present the results of a preliminary task-based evaluation with users, which are very promising.
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RDF-Analytics:基于RDF知识图的交互式分析
知识图中结构化查询的表述是一项具有挑战性的任务,它以熟悉查询语言的语法和知识图的内容为前提。为了减轻这一困难,本文介绍了RDF- analytics,这是一个新颖的系统,它使普通用户能够在复杂的RDF知识图上制定分析查询,即不一定是基于星型模式的RDF知识图。为了提供一个直观的界面,我们利用了用户对分面搜索(FS)系统的熟悉程度,也就是说,我们扩展了FS,使用户也能够制定分析查询。该方法的显著特点是能够在分析查询中包含任意长的路径(附带计数信息),具有限制的交互式公式,支持分面搜索(即以分面搜索方式定位所需资源)和分析查询,以及制定嵌套分析查询的能力。最后,我们提出了一个初步的基于任务的用户评估结果,这是非常有希望的。
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