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FPIRPQ: Accelerating regular path queries on knowledge graphs

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Abstract

With the growing popularity and application of knowledge-based artificial intelligence, the scale of knowledge graph data is dramatically increasing. As an essential type of query for RDF graphs, Regular Path Queries (RPQs) have attracted increasing research efforts, which explore RDF graphs in a navigational manner. Moreover, path indexes have proven successful for semi-structured data management. However, few techniques can be used effectively in practice for processing RPQ on large-scale knowledge graphs. In this paper, we propose a novel indexing solution named FPIRPQ (Frequent Path Index for Regular Path Queries) by leveraging Frequent Path Mining (FPM). Unlike the existing approaches to RPQs processing, FPIRPQ takes advantage of frequent paths, which are statistically derived from the data to accelerate RPQs. Furthermore, since there is no explicit benchmark targeted for RPQs over RDF graph yet, we design a micro-benchmark including 12 basic queries over synthetic and real-world datasets. The experimental results illustrate that FPIRPQ improves the query efficiency by up to orders of magnitude compared to the state-of-the-art RDF storage engine.

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Data availability

The queries (Q1 \(\sim\) Q12) designed on LUBM and DBpedia are are available in GitHub (https://github.com/haowq0417/FPIRPQ), and all the other data generated or analyzed during this study are included in this published article.

Notes

  1. https://github.com/haowq0417/FPIRPQ

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Acknowledgments

This work is expanded on the PAIRPQ: An Efficient Path Index for Regular Path Queries on Knowledge Graphs [12], and is supported by National Key Research and Development Program of China (2019YFE0198600); the National Natural Science Foundation of China (61972275).

Funding

This work is supported by National Key Research and Development Program of China (2019YFE0198600); the National Natural Science Foundation of China (61972275).

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Authors and Affiliations

  1. College of Intelligence and Computing, Tianjin University, Tianjin, China

    Xin Wang, Wenqi Hao, Yuzhou Qin, Baozhu Liu, Pengkai Liu, Yanyan Song & Xiaofei Wang

  2. School of Data Science, City University of Hong Kong, Hong Kong, China

    Qingpeng Zhang

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  1. Xin Wang
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Contributions

Xin Wang and Wenqi Hao are the major contributors in writing the manuscript and preparing the pictures. Yuzhou Qin and Baozhu Liu participate in the experiments and analyze the results. All authors read and approve the final manuscript.

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Correspondence to Xiaofei Wang.

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Wang, X., Hao, W., Qin, Y. et al. FPIRPQ: Accelerating regular path queries on knowledge graphs. World Wide Web 26, 661–681 (2023). https://doi.org/10.1007/s11280-022-01103-5

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