Abstract
The rapid growth of semantic web utilization in the cloud has resulted in massive amounts of RDF data, which is challenging large-scale RDF semantic reasoning. The traditional semantic reasoning process is very time-consuming and lacks scalability. In this paper, we present a scalable method for RDFS rule-based semantic reasoning using a distributed framework of Hadoop MapReduce, and propose an optimized semantic reasoning algorithm based on RDFS rules. The reasoning algorithm first classifies RDFS entailment rules to build different reasoning rule models, and then orders the rule execution sequences according to the relation of RDFS entailment rules to reduce reasoning time. During algorithm execution in MapReduce, the reasoning work handles RDFS rules in the Map process phase, and data duplication elimination is handled in the Reduce process phase. The experiment results on the LUBM benchmark show that our optimized reasoning algorithm outperforms Urbani’s reasoning method in efficiency, stability, and scalability. The average reasoning time of our algorithm is only 1/3 that of Urbani’s algorithm with different RDF dataset scales.
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Manola, F., Miller, E.: RDF Primer [EB/OL]. In: W3C Recommendation (2004). http://www.w3.org/TR/RDFSyntax/
Marshall, M.S., et al.: Emerging practices for mapping and linking life sciences data using RDF–a case series. J. Web Semant. 14, 2–13 (2012)
Kobilarov, G., Scott, T., Raimond, Y., Oliver, S., Sizemore, C., Smethurst, M., Bizer, C., Lee, R.: Media meets semantic web – how the BBC uses DBpedia and linked data to make connections. In: Aroyo, L., et al. (eds.) ESWC 2009. LNCS, vol. 5554, pp. 723–737. Springer, Heidelberg (2009). doi:10.1007/978-3-642-02121-3_53
Cheng, J., Liu, C., Zhou, M.C., Zeng, Q., Ylä-Jääski, A.: Automatic composition of semantic web services based on fuzzy predicate petrinets. IEEE Trans. Autom. Sci. Eng. (2013, to be published)
The Linked Open Data Project (LOD) (2015). http://www.w3.org/wiki/SweoIG/TaskForces/CommunityProjects/LinkingOpenData
Cure, O., Naacke, H., Randriamalala, T., et al.: LiteMat: a scalable, cost-efficient inference encoding scheme for large RDF graphs. IEEE International Conference on Big Data, pp. 1823–1830. IEEE (2015)
Hermit [EB/OL]. http://hermit-reasoner.com/
Xiao-yong, D.U., Yan, W.A.N.G., Bin, L.U.: Research and development on semantic web data management. J. Softw. 20(11), 2950–2964 (2009)
Bechhofer, S., Harmelen, F.V., Hendler, J., et al.: OWL web ontology language reference. In: W3C Recommendation (2004)
Hayes, P., (Ed.) RDF Semantics, W3C Recommendation (2004)
Zhou, J., Ma, L., Liu, Q., Zhang, L., Yu, Y., Pan, Y.: Minerva: a scalable OWL ontology storage and inference system. In: Mizoguchi, R., Shi, Z., Giunchiglia, F. (eds.) ASWC 2006. LNCS, vol. 4185, pp. 429–443. Springer, Heidelberg (2006). doi:10.1007/11836025_42
Kaoudi, Z., Miliaraki, I., Koubarakis, M.: RDFS reasoning and query answering on top of DHTs. In: Sheth, A., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 499–516. Springer, Heidelberg (2008). doi:10.1007/978-3-540-88564-1_32
Muhleisen, H., Dentler, K.: Large-scale storage and reasoning for semantic data using swarms. IEEE Comput. Intell. Mag. 7(2), 32–44 (2012)
Soma, R., Prasanna, V.: Parallel inferencing for OWL knowledge bases. In: Proceedings of the 37th International Conference on Parallel Processing, pp. 75–82 (2008)
Weaver, J., Hendler, J.A.: Parallel materialization of the finite RDFS closure for hundreds of millions of triples. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 682–697. Springer, Heidelberg (2009). doi:10.1007/978-3-642-04930-9_43
Mika, P., Tummarello, G.: Web semantics in the clouds. IEEE Intell. Syst. 23(5), 82–87 (2008)
Gu, R., Wang, S., Wang, F., Yuan, C., Huang, Y.: Cichlid: efficeinet large scale RDF/OWL reasong with spark. In: 2015 IEEE 29th International Parallel and Distributed Processing Symposium, pp. 700–709 (2015)
Urbani, J., Kotoulas, S., Maassen, J., et al.: WebPIE: a web-scale parallel inference engine using mapreduce. J. Web Semant. 17(2), 59–75 (2012)
Guo, Y., Pan, Z., Heflin, J.: LUBM: a benchmark for OWL knowledge base systems. Web Semant. Sci. Serv. Agents World Wide Web 3(2–3), 158–182 (2005)
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The National Natural Science Foundation of China under Grant No. 61301136, No. 61572525 and No. 61602525.
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Yang, L., Wen, X., Hu, Z., Liu, C., Long, J., Zheng, M. (2016). A Scalable Parallel Semantic Reasoning Algorithm-Based on RDFS Rules on Hadoop. In: Cellary, W., Mokbel, M., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2016. WISE 2016. Lecture Notes in Computer Science(), vol 10041. Springer, Cham. https://doi.org/10.1007/978-3-319-48740-3_33
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DOI: https://doi.org/10.1007/978-3-319-48740-3_33
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