This article introduces an algorithm to automatically translate a user-specified keyword-based query K to a SPARQL query Q so that the answers Q returns are also answers for K. The algorithm does not rely on an RDF schema, but it synthesizes SPARQL queries by exploring the similarity between the property domains and ranges, and the class instance sets observed in the RDF dataset. It estimates set similarity based on set synopses, which can be efficiently pre-computed in a single pass over the RDF dataset. The article includes two sets of experiments with an implementation of the algorithm. The first set of experiments shows that the implementation outperforms a baseline RDF keyword search tool that explores the RDF schema, while the second set of experiments indicate that the implementation performs better than the state-of-the-art TSA+BM25 and TSA+VDP keyword search systems over RDF datasets based on the “virtual documents” approach.

本文介绍了一种算法，将用户指定的基于关键字的查询K自动转换为SPARQL 查询Q，以便Q返回的答案也是K 的答案. 该算法不依赖于 RDF 模式，但它通过探索属性域和范围之间的相似性以及在 RDF 数据集中观察到的类实例集来合成 SPARQL 查询。它基于集合概要估计集合相似性，可以在对 RDF 数据集的单次传递中有效地预先计算。这篇文章包括两组实验和算法的实现。第一组实验表明该实现优于探索 RDF 模式的基线 RDF 关键字搜索工具，而第二组实验表明该实现的性能优于最先进的 TSA+BM25 和 TSA+VDP基于“虚拟文档”方法的 RDF 数据集上的关键字搜索系统。