Understanding the Structure of Knowledge Graphs with ABSTAT Profiles

Tracking #: 3181-4395

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Blerina Spahiu
Matteo Palmonari
Renzo Arturo Alva Principe
Anisa Rula

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Guest Editors Interactive SW 2022

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Full Paper
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 daunting 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. We consider the ABSTAT framework, which 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, where we demonstrate that users who look at ABSTAT profiles formulate their queries better and faster than users browsing the ontology of the KGs, a pretty strong baseline considered that many KGs do not even come with a specific ontology that can 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 a content understanding with a user study.
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