A Scalable Parallel Semantic Reasoning Algorithm-Based on RDFS Rules on Hadoop

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• 关键词：Ontology reasoning ; RDF ; Semantic web ; MapReduce ; Big data
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：10041
• 期：1
• 页码：447-456
• 全文大小：508 KB
• 参考文献：1.Manola, F., Miller, E.: RDF Primer [EB/OL]. In: W3C Recommendation (2004). http://​www.​w3.​org/​TR/​RDFSyntax/​
  2.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)CrossRef
  3.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 CrossRef
  4.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)
  5.The Linked Open Data Project (LOD) (2015). http://​www.​w3.​org/​wiki/​SweoIG/​TaskForces/​CommunityProject​s/​LinkingOpenData
  6.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)
  7.Hermit [EB/OL]. http://​hermit-reasoner.​com/​
  8.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)CrossRef
  9.Bechhofer, S., Harmelen, F.V., Hendler, J., et al.: OWL web ontology language reference. In: W3C Recommendation (2004)
  10.Hayes, P., (Ed.) RDF Semantics, W3C Recommendation (2004)
  11.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 CrossRef
  12.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 CrossRef
  13.Muhleisen, H., Dentler, K.: Large-scale storage and reasoning for semantic data using swarms. IEEE Comput. Intell. Mag. 7(2), 32–44 (2012)CrossRef
  14.Soma, R., Prasanna, V.: Parallel inferencing for OWL knowledge bases. In: Proceedings of the 37th International Conference on Parallel Processing, pp. 75–82 (2008)
  15.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 CrossRef
  16.Mika, P., Tummarello, G.: Web semantics in the clouds. IEEE Intell. Syst. 23(5), 82–87 (2008)CrossRef
  17.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)
  18.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)CrossRef
  19.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)CrossRef
  
• 作者单位：Liu Yang (19)
  Xiao Wen (19)
  Zhigang Hu (19)
  Chang Liu (19)
  Jun Long (20)
  Meiguang Zheng (19)
  
  19. School of Software, Center South University, Changsha, 410073, China
  20. School of Information Science and Engineering, Center South University, Changsha, 410073, China
  
• 丛书名：Web Information Systems Engineering ¨C WISE 2016
• ISBN：978-3-319-48740-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：10041

文摘

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.