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Cross-platform product matching based on entity alignment of knowledge graph with raea model

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Abstract

Product matching aims to identify identical or similar products sold on different platforms. 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. The existing EA methods inadequately utilize both attribute triples and relation triples simultaneously, especially the interactions between them. This paper introduces a two-stage pipeline consisting of rough filter and fine filter to match products from eBay and Amazon. For fine filtering, a new framework for Entity Alignment, R elation-aware and A ttribute-aware Graph Attention Networks for E ntity A lignment (RAEA), is employed. RAEA focuses on the interactions between attribute triples and relation triples, where the entity representation aggregates the alignment signals from attributes and relations with Attribute-aware Entity Encoder and Relation-aware Graph Attention Networks. The experimental results indicate that the RAEA model achieves significant improvements over 12 baselines on EA task in the cross-lingual dataset DBP15K (6.59% on average Hits@1) and delivers competitive results in the monolingual dataset DWY100K. The source code for experiments on DBP15K and DWY100K is available at github (https://github.com/Mockingjay-liu/RAEA-model-for-Entity-Alignment).

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Data Availability

All datasets are open-source and the sources are cited, except for the eBay product data due to the business interest.

Notes

  1. https://nijianmo.github.io/amazon/index.html

  2. https://kgma.github.io/

  3. https://github.com/PaddlePaddle/PaddleNLP/tree/develop/model_zoo/uie

  4. https://huggingface.co/sentence-transformers/paraphrase-multilingual-MPnet-base-v2

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Funding

This study 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 the Laboratory for AI-Powered Financial Technologies.

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Authors and Affiliations

  1. The Laboratory for AI-Powered Financial Technologies, Hong Kong SAR, China

    Wenlong Liu, Xinxin Gong, Kent Wu, Hua Xiang, Houmin Yan & Qingpeng Zhang

  2. School of Data Science, City University of Hong Kong, Hong Kong SAR, China

    Wenlong Liu, Jiahua Pan, Xingyu Zhang, Xinxin Gong, Yang Ye, Xujin Zhao, Houmin Yan & Qingpeng Zhang

  3. College of Intelligence and Computing, Tianjin University, Tianjin, China

    Xin Wang

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  1. Wenlong Liu
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  2. Jiahua Pan
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  11. Qingpeng Zhang
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Contributions

QZ and HY initialized the project and managed the study. WL, JP, XZhang, XG, YY and XZhao collected the data and performed formal analysis. All authors analyzed the data. WL and QZ wrote the initial manuscript. All authors contributed to the editing of the paper.

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Correspondence to Qingpeng Zhang.

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Liu, W., Pan, J., Zhang, X. et al. Cross-platform product matching based on entity alignment of knowledge graph with raea model. World Wide Web 26, 2215–2235 (2023). https://doi.org/10.1007/s11280-022-01134-y

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