Abstract
Semantic data analysis tasks benefit much from rule inference, which derives implicit knowledge from explicit information. Recently, available semantic data from the Web, sensor readings, semantic databases and ontologies exploded drastically. However, most of the existing approaches for semantic rule inference are either centralized, which cannot scale out to infer big semantic data; or rule-specific, which hinder their wildly use. In this paper, we propose a scalable approach for Horn-like rule inference of semantic data based on MapReduce, which can evaluate domain- and application-specific rules, and can be easily extended to evaluate RDFS and OWL ter Horst semantic rules. We first introduce a general rule-evaluation mechanism, which translates a Horn-like rule to one or more MapReduce jobs. To improve rule-evaluation performance, two optimization policies job-parallelization and job-reusing are then introduced. Using a large semantic data set generated by the LUBM benchmark, we give a detailed experimental analysis of the scalability and efficiency of our approaches.
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References
Kalyanpur, A., et al.: Structured data and inference in DeepQA. IBM Journal of Research and Development 10, 1–10 (2012)
Baclawski, K., Kokar, M.M., Waldinger, R., Kogut, P.A.: Consistency checking of semantic web ontologies. In: Horrocks, I., Hendler, J. (eds.) ISWC 2002. LNCS, vol. 2342, pp. 454–459. Springer, Heidelberg (2002)
Ma, Y., Liu, L., Lu, K., Jin, B., Liu, X.: A Graph Derivation Based Approach for Measuring and Comparing Structural Semantics of Ontologies. IEEE Transactions on Knowledge and Data Engineering 26, 1039–1052 (2013)
Motik, B., Sattler, U.: A comparison of reasoning techniques for querying large description logic ABoxes. In: Proceedings of the 13th international conference on Logic for Programming. Artificial Intelligence, and Reasoning, pp. 227–241 (2006)
Urbani, J., Kotoulas, S., Maassen, J., van Harmelen, F., Bal, H.: WebPIE: a Webscale parallel inference engine using MapReduce. J. of Web Semantics. 10, 59–75 (2012)
Horn, A.: On sentences which are true of direct unions of algebras. Journal of Symbolic Logic 16, 14–21 (1951)
Dean, J., Ghemawat, S.: Mapreduce: Simplied data processing on large clusters. In: Proceedings of the USENIX Symposium on Operating Systems Design & Implementation (OSDI), pp. 137–147 (2004)
Hitzler, P., Krotzsch, M., Parsia, B., Patel, P.F., Rudolph, S.: OWL 2 Web Ontology Language Primer, W3C recommendation (2012)
Urbani, J., Kotoulas, S., Oren, E., van Harmelen, F.: Scalable distributed reasoning using mapReduce. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 634–649. Springer, Heidelberg (2009)
Horrocks, I., et al.: SWRL: A semantic web rule language combining OWL and RuleML. W3C Member submission (2004)
Liu, C., Qi, G.: Toward scalable reasoning over annotated RDF data using mapReduce. In: Krötzsch, M., Straccia, U. (eds.) RR 2012. LNCS, vol. 7497, pp. 238–241. Springer, Heidelberg (2012)
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Wu, H., Liu, J., Ye, D., Wei, J., Zhong, H. (2014). Scalable Horn-Like Rule Inference of Semantic Data Using MapReduce. In: Buchmann, R., Kifor, C.V., Yu, J. (eds) Knowledge Science, Engineering and Management. KSEM 2014. Lecture Notes in Computer Science(), vol 8793. Springer, Cham. https://doi.org/10.1007/978-3-319-12096-6_24
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DOI: https://doi.org/10.1007/978-3-319-12096-6_24
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12095-9
Online ISBN: 978-3-319-12096-6
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