Abstract
In this demo paper, we present the Product Knowledge Retrieve System (PKRS), which can retrieve the large-scale product knowledge efficiently. The PKRS has three features. Firstly, PKRS can retrieve not only the objective knowledge (e.g. categories) but also the subjective knowledge (e.g. users’ opinion). Secondly, a learned mapping dictionary (LMD) is devised to accelerate the query parsing. Thirdly, PKRS adopts optimized join strategy to improve the retrieval effectiveness. For demonstration, we compare the performance of our PKRS with a state-of-the-art knowledge management system. The experimental results show that the PKRS can process the queries on product knowledge more effectively.
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Acknowledgment
The work is supported by National Natural Science Foundation of China (61562014, U1711263), the Project of Guangxi Natural Science Foundation (2018GXNSFDA281049), the Research Project of Guangxi Key Laboratory of Trusted Software (KX201916), the Innovation Project of GUET Graduate Education (2018YJCX48).
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Huang, T., Lin, Y., Tang, H., Li, Y., Zhang, H. (2019). PKRS: A Product Knowledge Retrieve System. In: Shao, J., Yiu, M., Toyoda, M., Zhang, D., Wang, W., Cui, B. (eds) Web and Big Data. APWeb-WAIM 2019. Lecture Notes in Computer Science(), vol 11642. Springer, Cham. https://doi.org/10.1007/978-3-030-26075-0_34
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DOI: https://doi.org/10.1007/978-3-030-26075-0_34
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