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.

中文翻译：

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揭示 SPARQL 会话级别的秘密

知识图谱基于语义 Web 技术，通过实时 SPARQL 服务帮助用户发现感兴趣的信息。寻求答案的人经常迭代地检查中间结果并在搜索会话中反复修改 SPARQL 查询。在这种情况下，了解用户行为对于有效的意图预测和查询优化至关重要。但是，尚未在 SPARQL 会话级别系统地研究这些行为。本文通过对大量真实世界的 SPARQL 查询日志进行全面调查，揭示会话级用户搜索行为的秘密。特别是，我们彻底评估了用户对 SPARQL 查询的结构和数据驱动特性所做的查询更改。为了说明我们发现的潜力，我们使用了一个应用示例来说明如何使用我们的发现，