Cross-platform Product Matching Based on Entity Alignment of Knowledge Graph with RAEA model
Not on the map. Why?
Abstract (arXiv)
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, Relation-aware and Attribute-aware Graph Attention Networks for Entity Alignment (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).
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 32 of its 56 references that are in the map. First posted on arXiv 8 Dec 2025.
Builds on
32 of 56 references.
References · 32
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
- 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,801
- 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,168
- 2020MPNet: Masked and Permuted Pre-training for Language Understandingcited by 973
- 2016Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignmentcited by 128
- 2017Cross-lingual Entity Alignment via Joint Attribute-Preserving Embeddingcited by 126
- 2019Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphscited by 106
- 2019Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregationcited by 101
- 2019Multi-Channel Graph Neural Network for Entity Alignmentcited by 86
- 2020On the Sentence Embeddings from Pre-trained Language Modelscited by 83
- 2019Multi-view Knowledge Graph Embedding for Entity Alignmentcited by 74
20 more
- 2019Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Networkcited by 68
- 2020Exploring and Evaluating Attributes, Values, and Structures for Entity Alignmentcited by 63
- 2015Modeling Relation Paths for Representation Learning of Knowledge Basescited by 56
- 2018Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignmentcited by 49
- 2020Neighborhood Matching Network for Entity Alignmentcited by 44
- 2020TransEdge: Translating Relation-contextualized Embeddings for Knowledge Graphscited by 41
- 2021Boosting the Speed of Entity Alignment 10*: Dual Attention Matching Network with Normalized Hard Sample Miningcited by 37
- 2022Multi-modal Contrastive Representation Learning for Entity Alignmentcited by 37
- 2019Product Knowledge Graph Embedding for E-commercecited by 36
- 2020AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Typescited by 31
- 2021RAGA: Relation-aware Graph Attention Networks for Global Entity Alignmentcited by 29
- 2021LargeEA: Aligning Entities for Large-scale Knowledge Graphscited by 26
- 2022ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch Similaritiescited by 20
- 2022LightEA: A Scalable, Robust, and Interpretable Entity Alignment Framework via Three-view Label Propagationcited by 18
- 2022Time-aware Graph Neural Networks for Entity Alignment between Temporal Knowledge Graphscited by 16
- 2021r-GAT: Relational Graph Attention Network for Multi-Relational Graphscited by 9
- 2022Large-scale Entity Alignment via Knowledge Graph Merging, Partitioning and Embeddingcited by 9
- 2022High-quality Task Division for Large-scale Entity Alignmentcited by 4
- 2021Cross-Language Learning for Entity Matchingcited by 3
- 2022Jointly Learning Knowledge Embedding and Neighborhood Consensus with Relational Knowledge Distillation for Entity Alignmentcited by 2
Built on by
Citing papers per year: 2026: 1.
1 paper.