Understanding Data Exploration Search A brief Study of User’s Exploratory Search facets

Kumar, Samit; Singh, Vikram

doi:10.1109/ccaa.2018.8777585

Cited by 1 publication

(1 citation statement)

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“…Finally, some users may find it difficult to master ontological languages [38]. Some of these limitations can be mitigated by exploratory search [43], i.e., by formulating queries and retrieving results iteratively. However, this method is effort-consuming and can even fail when some of the exploratory queries match too many results (e.g., find all the properties where the triple subject is a movie) [65].…”

Section: Introductionmentioning

confidence: 99%

Understanding the structure of knowledge graphs with ABSTAT profiles

Spahiu

Palmonari

Principe

et al. 2023

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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.

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Section: Introductionmentioning

confidence: 99%