Cross-platform Product Matching Based on Entity Alignment of Knowledge Graph with RAEA model

2025-12-08

AI Summary

This work addresses cross-platform product matching by formulating it as an entity alignment (EA) task between knowledge graphs (KGs). Existing approaches neglect fine-grained interaction modeling between attribute and relational triples, leading to insufficient semantic representation. To overcome this limitation, we propose RAEA—a two-stage EA model: (1) an attribute-aware encoder fuses attribute information into entity embeddings; (2) a relation-aware graph attention network captures structural dependencies, augmented by a dual-stage filtering mechanism that jointly refines alignment predictions. RAEA is the first EA method to explicitly model fine-grained interactions between attribute and relational triples in a unified framework. Extensive experiments demonstrate its effectiveness: on DBP15K, RAEA achieves an average 6.59% improvement in Hits@1 over 12 state-of-the-art baselines; on DWY100K, it attains new state-of-the-art performance.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Knowledge Engineering
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

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, 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).

Problem

• Identifies identical products across different e-commerce platforms.
• Converts product matching to entity alignment using knowledge graphs.
• Improves entity alignment by modeling attribute-relation interactions.

Innovation

• Two-stage pipeline with rough and fine filtering
• RAEA model integrates attribute and relation triples
• Graph attention networks enhance entity alignment signals