Abstract
Resource Description Framework (RDF) is a standard data model of the Semantic Web, and it has been widely adopted in various domains in recent years for data and knowledge representation. Unlike queries on relational databases, most of queries applied on RDF data are known as graph queries, expressed in the SPARQL language. Subgraph matching, a basic SPARQL operation, is known to be NP-complete. Coupled with the rapidly increasing volumes of RDF data, it makes efficient graph query processing a very challenging problem. This paper primarily focuses on providing an index scheme and corresponding algorithms that support the efficient solution of such queries. We present a subgraph matching query engine based on the FFD-index which is an indexing mechanism encoding a star subgraph into a bit string. A SPARQL query graph is decomposed into several star query subgraphs which can be efficiently processed benefiting from succinct FFD-index data structure. Extensive evaluation shows that our approach outperforms RDF-3X and gStore on solving subgraph matching.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bizer, C., Heath, T., Berners-Lee, T.: Linked data - the story so far. Int. J. Semant. Web Inf. Syst. 5(3), 1–22 (2009)
Bunke, H.: Graph matching: theoretical foundations, algorithms, and applications. In: Proceedings of Vision Interface, pp. 82–88 (2000)
Cheng, J., Ke, Y., Ng, W., Lu, A.: Fg-index: towards verification-free query processing on graph databases. In: Proceedings of SIGMOD, pp. 857–872. ACM (2007)
Conte, D., Foggia, P., Sansone, C., Vento, M.: Thirty years of graph matching in pattern recognition. Int. J. Pattern Recogn. Artif. Intell. 18(03), 265–298 (2004)
Cordella, L.P., Foggia, P., Sansone, C., Vento, M.: A (sub) graph isomorphism algorithm for matching large graphs. IEEE Trans. Pattern Anal. Mach. Intell. 26(10), 1367–1372 (2004)
Fortin, S.: The Graph Isomorphism Problem. Technical Report, University of Alberta, Canada (1996)
Harris, S., Seaborne, A.: SPARQL 1.1 Query Language. W3C Recommendation (2013)
Kim, H., Ravindra, P., Anyanwu, K.: From SPARQL to mapreduce: the journey using a nested triplegroup algebra. Proc. VLDB Endow. 4(12), 1426–1429 (2011)
Klyne, G., Carroll, J.J., McBride, B.: RDF 1.1 Concepts and Abstract Syntax. W3C Recommendation (2014)
Michael, R.G., David, S.J.: Computers and Intractability: A Guide to the Theory of NP-Completeness. WH Freeman Co., San Francisco (1979)
Neumann, T., Weikum, G.: RDF-3X: a RISC-style engine for RDF. Proc. VLDB Endow. 1(1), 647–659 (2008)
Papailiou, N., Tsoumakos, D., Konstantinou, I., Karras, P., Koziris, N.: H2RDF+: An efficient data management system for big RDF graphs. In: Proceedings of SIGMOD, pp. 909–912. ACM (2014)
Shasha, D., Wang, J.T., Giugno, R.: Algorithmics and applications of tree and graph searching. In: Proceedings of PODS, pp. 39–52. ACM (2002)
Udrea, O., Pugliese, A., Subrahmanian, V.S.: GRIN: a graph based RDF index. In: Proceedings of the National Conference on Artificial Intelligence, vol. 22 (2007)
Ullmann, J.R.: An algorithm for subgraph isomorphism. J. ACM 23(1), 31–42 (1976)
Weiss, C., Karras, P., Bernstein, A.: Hexastore: sextuple indexing for semantic web data management. Proc. VLDB Endow. 1(1), 1008–1019 (2008)
Lyu, X., Wang, X., Li, Y.-F., Feng, Z.: FFD-Index: an efficient indexing scheme for star subgraph matching on large RDF graphs. In: Liu, A., Ishikawa, Y., Qian, T., Nutanong, S., Cheema, M.A. (eds.) DASFAA 2015 Workshops. LNCS, vol. 9052, pp. 240–245. Springer, Heidelberg (2015)
Yan, X., Yu, P.S., Han, J.: Graph indexing: a frequent structure-based approach. In: Proceedings of SIGMOD, pp. 335–346. ACM (2004)
Zhang, S., Li, S., Yang, J.: GADDI: Distance index based subgraph matching in biological networks. In: Proceedings of EDBT, pp. 192–203. ACM (2009)
Zou, L., Chen, L., Yu, J. X., Lu, Y.: A novel spectral coding in a large graph database. In: Proceedings of EDBT, pp. 181–192. ACM (2008)
Zou, L., Mo, J., Chen, L., Özsu, M.T., Zhao, D.: gStore: answering SPARQl queries via subgraph matching. Proc. VLDB Endow. 4(8), 482–493 (2011)
Acknowledgement
This work is supported by the National Natural Science Foundation of China (61100049), the National High-tech R&D Program of China (863 Program) (2013AA013204), and the Australia Research Council (ARC) Discovery grants DP130103051.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Lyu, X., Wang, X., Li, YF., Feng, Z., Wang, J. (2015). GraSS: An Efficient Method for RDF Subgraph Matching. In: Wang, J., et al. Web Information Systems Engineering – WISE 2015. WISE 2015. Lecture Notes in Computer Science(), vol 9418. Springer, Cham. https://doi.org/10.1007/978-3-319-26190-4_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-26190-4_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26189-8
Online ISBN: 978-3-319-26190-4
eBook Packages: Computer ScienceComputer Science (R0)