论文标题
在SPARQL会话级别揭示秘密
Revealing Secrets in SPARQL Session Level
论文作者
论文摘要
基于语义Web技术，知识图可帮助用户通过使用实时SPARQL服务来发现感兴趣的信息。寻求答案者经常检查中间结果，并在搜索过程中重复修改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，这些行为尚未在SPARQL会话级别进行系统的研究。本文通过对大规模现实世界的SPARQL查询日志进行全面调查来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W.R.T.进行的查询更改。 SPARQL查询的结构和数据驱动特征。为了说明我们发现的潜力，我们采用了一个应用程序的示例，说明如何使用我们的发现，这对于设计有效的SPARQL缓存，自动完成，查询建议，近似和放松技术可能很有价值。
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.
