Abstract
In this demonstration, we present a GPU-enhanced product knowledge retrieve system called GPKRS, which stores product knowledge based on the sparse matrix compression, and introduces a query transformation module to transform the query operation into the corresponding matrix operations. By this way, we can take advantage of the powerful parallel computing power of GPU to accelerate the processing of SPARQL query. Further, GPKRS adopts an optimized pipeline query strategy to speed up the query execution. The experiments show that the GPKRS achieves state-of-the-art query performances on the LUMB dataset and a synthetic product knowledge dataset.
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Acknowledgment
This work was supported by National Natural Science Foundation of China (62062027, U1711263), Guangxi Natural Science Foundations (2018GXNSFDA281049, 2020GXNSFAA159012, 2018GXNSFAA281326), Science and Technology Major Project of Guangxi Province (AA19046004), Innovation Project of GUET Graduate Education (2020YCXS046) and the project of Guangxi Key Laboratory of Trusted Software (kx202021).
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Lin, Y., Song, H., Fang, C., Li, Y. (2021). GPKRS: A GPU-Enhanced Product Knowledge Retrieval System. In: U, L.H., Spaniol, M., Sakurai, Y., Chen, J. (eds) Web and Big Data. APWeb-WAIM 2021. Lecture Notes in Computer Science(), vol 12859. Springer, Cham. https://doi.org/10.1007/978-3-030-85899-5_35
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DOI: https://doi.org/10.1007/978-3-030-85899-5_35
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