Abstract
Knowledge graph is essential infrastructure of lots of intelligent Web applications. Recently, various types of knowledge graphs are designed and deployed to make the applications more smarter. However, the large amount and heterogeneity of product knowledge bring new challenges for managing such knowledge data. In this work, we propose a scalable framework for organizing large-scale product knowledge, which includes the objective product knowledge and the subject users’ opinion knowledge. In order to improve the efficiency of knowledge query, we design a hybrid index structure with a learned model and several B-Tree indexes. Finally, a join strategy based on the variable combination of aspect and opinion is proposed to implement the query optimization. The experimental results show that the proposed method can improved the query efficiency significantly on a large-scale product knowledge compared with a states-of-the-art knowledge management system.
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Acknowledgements
This work is supported by National Natural Science Foundation of China (61562014), Guangxi Natural Science Foundation (2018GXNSFDA281049), the project of Guangxi Key Laboratory of Automatic Detecting Technology and Instruments (YQ17111), and the general scientific research project of Guangxi Provincial Department of Education (2017KY0195).
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Li, Y., Huang, T., Song, H., Lin, Y. (2019). Organization and Query Optimization of Large-Scale Product Knowledge. In: Ni, W., Wang, X., Song, W., Li, Y. (eds) Web Information Systems and Applications. WISA 2019. Lecture Notes in Computer Science(), vol 11817. Springer, Cham. https://doi.org/10.1007/978-3-030-30952-7_48
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DOI: https://doi.org/10.1007/978-3-030-30952-7_48
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