Abstract
In this paper, we propose a plugin-based framework for massive RDF stream reasoning to support complicated tasks on RDF stream in an adaptive and flexible way. Within this framework, the problem of RDF stream reasoning can be equivalently reduced to the combination problem of SPARQL querying and rule-based reasoning. Take advantage of the plug-in method, we can apply various off-the-shelf SPARQL query engines and rule-based reasoners in a simple way. Moreover, to efficiently support real-time reasoning on massive RDF stream, we develop a multi-threaded batch processing approach to manage resources and an adaptive reasoning plan for dynamically managing inference rules in the stream reasoning. Finally, our experiments evaluate on dataset built on the benchmark LUBM and DBpedia. The experimental results show that our framework is effective and efficient.
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Acknowledgments
We would like to thank Qiong Li for constructive comments. This work is supported by the National Natural Science Foundation of China (61373165, 61672377), the National Key R&D Program of China (2016YFB1000603, 2017YFC0908401), and the Key Technology R&D Program of Tianjin (16YFZCGX00210).
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Rao, G., Zhao, B., Zhang, X., Feng, Z., Xiao, G. (2018). PRSPR: An Adaptive Framework for Massive RDF Stream Reasoning. In: Cai, Y., Ishikawa, Y., Xu, J. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 10987. Springer, Cham. https://doi.org/10.1007/978-3-319-96890-2_36
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DOI: https://doi.org/10.1007/978-3-319-96890-2_36
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