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Answering why-not questions on SPARQL queries

机译:回答为什么 - 在SPARQL查询上的问题

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摘要

SPARQL, the W3C standard for RDF query languages, has gained significant popularity in recent years. An increasing amount of effort is currently being exerted to improve the functionality and usability of SPARQL-based search engines. However, explaining missing items in the results of SPARQL queries or the so-called why-not question has not received sufficient attention. In this study, we first formalize why-not questions on SPARQL queries and then propose a novel explanation model, called answering why-not questions on SPARQL (ANNA) to answer why-not questions using a divide-and-conquer strategy. ANNA adopts a graph-based approach and an operator-based approach to generate logical explanations at the triple pattern level and the query operator level, respectively, which helps users refine their initial queries. Extensive experimental results on two real-world RDF datasets show that the proposed model and algorithms can provide high-quality explanations in terms of both effectiveness and efficiency.
机译:RDF查询语言的W3C标准SPARQL,近年来取得了重大普及。目前正在努力提高基于SPARQL的搜索引擎的功能和可用性的越来越大。但是,在SPARQL查询结果中解释缺失的项目或所谓的为什么没有收到足够的注意力。在这项研究中,我们首先正式化为什么 - 在SPARQL查询中没有问题,然后提出一个新颖的解释模型,称为回答为什么 - 在SPARQL(Anna)上的问题,以回答为什么使用鸿沟和征服战略的原因。 Anna采用基于图形的方法和基于操作员的方法,可以分别在三重模式级别和查询操作员级别生成逻辑解释,帮助用户改进其初始查询。在两个现实世界RDF数据集中的广泛实验结果表明,所提出的模型和算法可以在效率和效率方面提供高质量的解释。

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