Abstract
Product matching aims to identify similar or identical products sold on different platforms, which is crucial for retailers to adjust investment strategies. By building knowledge graphs (KGs), the product matching problem can be converted to the Entity Alignment (EA) task, which aims to discover the equivalent entities from diverse KGs. This paper introduces a two-stage pipeline consisted of rough filter and fine filter. Based on product names and categories, we roughly match products in rough filtering. For fine filtering, a new framework for Entity Alignment, Relation-aware, and Attribute-aware Graph Attention Networks for Entity Alignment (RAEA), is employed. Experiments on eBay-Amazon dataset indicated that the two-stage pipeline performs well on the problem of cross-platform product matching.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Peeters, R., Bizer, C., Glavas, G.: Intermediate training of bert for product matching. small 745(722), 2–112 (2020)
Wang, Z., Lv, Q., Lan, X., Zhang, Y.: Cross-lingual knowledge graph alignment via graph convolutional networks. In: EMNLP, pp. 349–357 (2018) https://doi.org/10.18653/v1/D18-1032
Yu, D., Yang, Y., Zhang, R., Wu, Y.: Knowledge embedding based graph convolutional network. In: Proceedings of the Web Conference 2021, pp. 1619–1628 (2021)
Liu, Z., Cao, Y., Pan, L., Li, J., Chua, T.-S.: Exploring and evaluating attributes, values, and structures for entity alignment. In: EMNLP, p. 10 (2020)
Ni, J., Li, J., McAuley, J.: Justifying recommendations using distantly- labeled reviews and fine-grained aspects. In: EMNLP-IJCNLP, pp. 188–197 (2019)
Zhu, R., Ma, M., Wang, P.: RAGA: relation-aware graph attention networks for global entity alignment. In: Karlapalem, K., Cheng, H., Ramakrishnan, N., Agrawal, R.K., Reddy, P.K., Srivastava, J., Chakraborty, T. (eds.) PAKDD 2021. LNCS (LNAI), vol. 12712, pp. 501–513. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-75762-5_40
Paddle UIE Homepage. https://github.com/PaddlePaddle/PaddleNLP/tree/develop/model_zoo/uie. Accessed 1 Oct 2022
Acknowledgments
The study in this paper was supported partly by the National Key Research and Development Program of China (No. 2019YFE0198600), and partly by InnoHK initiative, The Government of the HKSAR, and Laboratory for AI-Powered Financial Technologies.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Liu, W. et al. (2023). Cross-platform Product Matching Based on Knowledge Graph. In: Yang, S., Islam, S. (eds) Web and Big Data. APWeb-WAIM 2022 International Workshops. APWeb-WAIM 2022. Communications in Computer and Information Science, vol 1784. Springer, Singapore. https://doi.org/10.1007/978-981-99-1354-1_5
Download citation
DOI: https://doi.org/10.1007/978-981-99-1354-1_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-1353-4
Online ISBN: 978-981-99-1354-1
eBook Packages: Computer ScienceComputer Science (R0)