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Optimization of Kleene closure regular path query on large RDF graphs.

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Title: Optimization of Kleene closure regular path query on large RDF graphs.
Authors: Ren, Tenglong1 (AUTHOR), Zhang, Xiaowang1 (AUTHOR), Feng, Zhiyong1 (AUTHOR)
Source: Computer Journal. Nov2025, Vol. 68 Issue 11, p1595-1609. 15p.
Subjects: Computational complexity, SPARQL (Computer program language), Ontologies (Information retrieval), Data structures
Abstract: As the core operator of regular path query (RPQ), the Kleene closure is essentially a recursive operation based on predicates. It has more expressive power than first-order logic, but its algorithmic complexity is an NP-hard problem and expensive to execute. Most approaches implement Kleene closure via online recursive queries (inefficient for large-scale data), yet significant reusable intermediate results emerge during execution. This paper proposes an efficient query optimization method based on offline preprocessing and tree-structured strategies to address the challenges of NP-hard complexity and low execution efficiency caused by the recursive nature of Kleene closure RPQs (KRPQs). The core contributions of this work include: (i) recursive index tree : by precomputing and materializing intermediate results of predicate paths offline, Kleene closure queries are transformed into non-recursive SPARQL queries. This allows direct retrieval of answer branches from the preconstructed index tree, eliminating redundant computations and reducing online complexity. (ii) Selectivity-driven dynamic optimization : a heuristic cost model estimates intermediate result sizes to guide query plan generation, minimizing redundant operations. (3) Query decomposition tree for nested KRPQs : a hierarchical decomposition algorithm processes nested Kleene closures layer-by-layer from inner to outer expressions, overcoming limitations of existing methods in handling complex nested structures. Experiments on large-scale RDF datasets demonstrate that the method reduces average response times by 50%–70% for single-predicate and expression-based Kleene closure queries compared to systems like Virtuoso, Jena, and KRPQ, with over 60% improvement for complex nested queries. Additionally, multithreaded preprocessing and path pool optimizations achieve up to a 76 |$\times $| reduction in index construction time. Experiments show that the method can significantly reduce the time of KRPQs based on large RDF graphs. [ABSTRACT FROM AUTHOR]
Copyright of Computer Journal is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Optimization of Kleene closure regular path query on large RDF graphs.
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  Data: <searchLink fieldCode="AR" term="%22Ren%2C+Tenglong%22">Ren, Tenglong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Xiaowang%22">Zhang, Xiaowang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Feng%2C+Zhiyong%22">Feng, Zhiyong</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Computer+Journal%22">Computer Journal</searchLink>. Nov2025, Vol. 68 Issue 11, p1595-1609. 15p.
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  Data: As the core operator of regular path query (RPQ), the Kleene closure is essentially a recursive operation based on predicates. It has more expressive power than first-order logic, but its algorithmic complexity is an NP-hard problem and expensive to execute. Most approaches implement Kleene closure via online recursive queries (inefficient for large-scale data), yet significant reusable intermediate results emerge during execution. This paper proposes an efficient query optimization method based on offline preprocessing and tree-structured strategies to address the challenges of NP-hard complexity and low execution efficiency caused by the recursive nature of Kleene closure RPQs (KRPQs). The core contributions of this work include: (i) recursive index tree : by precomputing and materializing intermediate results of predicate paths offline, Kleene closure queries are transformed into non-recursive SPARQL queries. This allows direct retrieval of answer branches from the preconstructed index tree, eliminating redundant computations and reducing online complexity. (ii) Selectivity-driven dynamic optimization : a heuristic cost model estimates intermediate result sizes to guide query plan generation, minimizing redundant operations. (3) Query decomposition tree for nested KRPQs : a hierarchical decomposition algorithm processes nested Kleene closures layer-by-layer from inner to outer expressions, overcoming limitations of existing methods in handling complex nested structures. Experiments on large-scale RDF datasets demonstrate that the method reduces average response times by 50%–70% for single-predicate and expression-based Kleene closure queries compared to systems like Virtuoso, Jena, and KRPQ, with over 60% improvement for complex nested queries. Additionally, multithreaded preprocessing and path pool optimizations achieve up to a 76 |$\times $| reduction in index construction time. Experiments show that the method can significantly reduce the time of KRPQs based on large RDF graphs. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Computer Journal is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1093/comjnl/bxaf061
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        Text: English
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      – SubjectFull: Ontologies (Information retrieval)
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              M: 11
              Text: Nov2025
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