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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Authors and Affiliations
University of Chinese Academy of Sciences, China
Haijiang Wu
State Key Laboratory of Computer Science, Institute of Software, China
Jie Liu & Jun Wei
Institute of Software, Chinese Academy of Sciences, China
Haijiang Wu, Jie Liu, Dan Ye, Jun Wei & Hua Zhong
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Faculty of Computer Science, Knowledge Engineering Research Group, University of Vienna, Währingerstr. 29, 1090, Vienna, Austria
Robert Buchmann
Faculty of Engineering, Research Center for Sustainable Products and Processes, Lucian Blaga University of Sibiu, 10 Victoriei Blv., 550024, Sibiu, Romania
Claudiu Vasile Kifor
Department of Computer Science, Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Haidian District, 100044, Beijing, China
Jian Yu
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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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