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A survey of RDF stores & SPARQL engines for querying knowledge graphs

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Abstract

RDF has seen increased adoption in recent years, prompting the standardization of the SPARQL query language for RDF, and the development of local and distributed engines for processing SPARQL queries. This survey paper provides a comprehensive review of techniques and systems for querying RDF knowledge graphs. While other reviews on this topic tend to focus on the distributed setting, the main focus of the work is on providing a comprehensive survey of state-of-the-art storage, indexing and query processing techniques for efficiently evaluating SPARQL queries in a local setting (on one machine). To keep the survey self-contained, we also provide a short discussion on graph partitioning techniques used in the distributed setting. We conclude by discussing contemporary research challenges for further improving SPARQL query engines. An extended version also provides a survey of over one hundred SPARQL query engines and the techniques they use, along with twelve benchmarks and their features.

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Notes

  1. In this paper, we abbreviate the union of sets \(M_1 \cup \ldots \cup M_n\) with \(M_1 \ldots M_n\). Hence, \({\varvec{I}}{\varvec{B}}\L \) stands for \({\varvec{I}} \cup {\varvec{B}} \cup \L \).

  2. We use the blank prefix (e.g., :DB) as an arbitrary example. Other prefixes used can be retrieved at http://prefix.cc/.

  3. SPARQL uses the syntax !(\(p_1\)|\(\ldots \)|\(p_k\)|\(p_{k+1}\)|\(\ldots \)|\(p_n\)) which can be written as , where \(P = \{p_1,\ldots ,p_k\}\) and \(P' = \{p_{k+1},\ldots ,p_n \}\) [50, 74].

  4. The definition of MINUS is slightly different from anti-join in that mappings with no overlapping variables on the right are ignored.

  5. We relax the typical requirement for a set partition that \(G_i \cap G_j = \emptyset \) for all \(1 \le i < j \le n\) to allow for the possibility of replication or other forms of redundancy.

  6. Given a graph, deciding if there is a k-way partition with fewer than n edges between partitions is NP-complete.

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Acknowledgements

This work was partially funded by a grant from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 860801. Hogan was supported by Fondecyt Grant No. 1181896 and ANID—Millennium Science Initiative Program—Code ICN17_002. Bin Yao was supported by the NSFC (61922054, 61872235, 61832017, 61729202, 61832013), the National Key Research and Development Program of China (2020YFB1710202, 2018YFC1504504), the Science and Technology Commission of Shanghai Municipality (STCSM) AI under Project 19511120300. This work was also supported by the German Federal Ministry of Education and Research (BMBF) within the EuroStars project E1114681 3DFed under the Grant No. 01QE2114 and project KnowGraphs (No. 860801).

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Authors and Affiliations

  1. SEIEE, Shanghai Jiao Tong University, Shanghai, China

    Waqas Ali & Bin Yao

  2. AKSW, University of Leipzig, Leipzig, Germany

    Muhammad Saleem

  3. Hangzhou Institute of Advanced Technology, Hangzhou, China

    Bin Yao

  4. DCC, University of Chile and IMFD, Santiago, Chile

    Aidan Hogan

  5. DICE, Paderborn University, Paderborn, Germany

    Axel-Cyrille Ngonga Ngomo

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  1. Waqas Ali
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Correspondence to Bin Yao.

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Ali, W., Saleem, M., Yao, B. et al. A survey of RDF stores & SPARQL engines for querying knowledge graphs. The VLDB Journal 31, 1–26 (2022). https://doi.org/10.1007/s00778-021-00711-3

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