Abstract
With the rapid development of the Semantic Web, the scale of RDF graphs surges. To describe ontology information, RDFs and OWL are endorsed by W3C, which further enhances the expressiveness of RDF graphs. A great challenge of managing RDF graphs is how to store massive data and efficiently reason ontology information at query time. There are two main issues with the existing RDF graph storage systems: 1) the relational data model is mainly used as the underlying storage architecture, which not only leads to exceeding the storage capacity, but also may incur high overhead while performing complex queries or multi-join queries; 2) the ontology reasoning module is either relatively independent of storage layer or used as an upper-layer application of storage and query system, causing redundancy and inefficiency in query. To address these issues, we present LocRDF, a novel storage system for RDF graphs via key-value store supporting ontology reasoning. LocRDF integrates ontology information into the underlying storage scheme with the application of a fixed-length interval encoding, promoting the efficiency of ontology reasoning at runtime. Experimental results on LUBM datasets show that extended ontology reasoning on large-scale RDF graphs scarcely affects query performance which is even significantly better than the existing state-of-the-art RDF query engines.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Allegrograph. https://franz.com/agraph/allegrograph/
Carroll, J.J., Dickinson, I., Dollin, C., Reynolds, D., Seaborne, A., Wilkinson, K.: Jena: implementing the semantic web recommendations. In: Proceedings of the 13th International World Wide Web Conference on Alternate Track Papers & Posters, pp. 74–83 (2004)
Fu, X., Ren, X., Mengshoel, O.J., Wu, X.: Stochastic optimization for market return prediction using financial knowledge graph. In: 2018 IEEE International Conference on Big Knowledge (ICBK), pp. 25–32. IEEE (2018)
He, L., et al.: Stylus: a strongly-typed store for serving massive RDF data. Proc. VLDB Endow. 11(2), 203–216 (2017)
Li, Y.: Research and analysis of semantic search technology based on knowledge graph. In: 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), vol. 1, pp. 887–890. IEEE (2017)
Muñoz, S., Pérez, J., Gutierrez, C.: Minimal deductive systems for RDF. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519, pp. 53–67. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72667-8_6
The Nebula Graph Team: Nebula graph database manual 2.0. https://docs.nebula-graph.io/2.0/
The Neo4j Team: The neo4j manual v3.4 (2018). https://neo4j.com/docs/developermanual/current/
Urbani, J., van Harmelen, F., Schlobach, S., Bal, H.: QueryPIE: backward reasoning for OWL horst over very large knowledge bases. In: Aroyo, L., et al. (eds.) ISWC 2011. LNCS, vol. 7031, pp. 730–745. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25073-6_46
Urbani, J., Kotoulas, S., Maassen, J., Van Harmelen, F., Bal, H.: WebPIE: a web-scale parallel inference engine using mapreduce. J. Web Semant. 10, 59–75 (2012)
W3C: Resource description framework. https://www.w3.org/RDF/
Wang, H., Fang, Z., Zhang, L., Pan, J.Z., Ruan, T.: Effective online knowledge graph fusion. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9366, pp. 286–302. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25007-6_17
Wang, X., Zou, L., Wang, C., Peng, P., Feng, Z.: Research on knowledge graph data management: a survey. J. Softw. 30(7), 2139–2174 (2019)
Wylot, M., Hauswirth, M., Cudré-Mauroux, P., Sakr, S.: RDF data storage and query processing schemes: a survey. ACM Comput. Surv. (CSUR) 51(4), 1–36 (2018)
Zhang, R., Liu, P., Guo, X., Li, S., Wang, X.: A unified relational storage scheme for RDF and property graphs. In: Ni, W., Wang, X., Song, W., Li, Y. (eds.) WISA 2019. LNCS, vol. 11817, pp. 418–429. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30952-7_41
Acknowledgement
This work is supported by the National College Students’ Innovation and Entrepreneurship Training Program of China (202110056120).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, Y., Li, J., Liu, X., Cheng, R., Hu, Y., Wang, X. (2022). LocRDF: An Ontology-Aware Key-Value Store for Massive RDF Data. In: Zhao, X., Yang, S., Wang, X., Li, J. (eds) Web Information Systems and Applications. WISA 2022. Lecture Notes in Computer Science, vol 13579. Springer, Cham. https://doi.org/10.1007/978-3-031-20309-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-20309-1_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20308-4
Online ISBN: 978-3-031-20309-1
eBook Packages: Computer ScienceComputer Science (R0)