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Revealing Secrets in SPARQL Session Level: ISWC 2020 - Research Track

3 months ago by Dr. rer. nat. Muhammad Saleem

Based on Semantic Web technologies, knowledge graphs help users to discover information of interest by using live SPARQL services. Answer-seekers often examine intermediate results iteratively and modify SPARQL queries repeatedly in a search session. In this context, understanding user behaviors is critical for effective intention prediction and query optimization. However, these behaviors have not yet been researched systematically at the SPARQL session level. This paper reveals secrets of session-level user search behaviors by conducting a comprehensive investigation over massive real-world SPARQL query logs. In particular, we thoroughly assess query changes made by users w.r.t. structural and data-driven features of SPARQL queries. To illustrate the potentiality of our findings, we employ an application example of how to use our findings, which might be valuable to devise efficient SPARQL caching, auto-completion, query suggestion, approximation, and relaxation techniques in the future.

Our contributions can be summarized as follows:

– We port the concept of sessions to SPARQL queries and give a specification of SPARQL search sessions.

– We are the first, to the best of our knowledge, to investigate potential correlations between SPARQL queries and provide a comprehensive analysis of query reformulations in a given search session.

– We provide an application example of how our findings can be used to illustrate the potentiality of utilizing user behaviors in a search session.

Github repository: https://github.com/seu-kse/SparqlSession

Authors: Xinyue Zhang , Meng Wang , Muhammad Saleem , Axel-Cyrille Ngonga Ngomo , Guilin Qi, and Haofen Wang

Contact: saleem@informatik.uni-leipzig.de