通过融合属性与关系信息，提升跨平台商品匹配准确率。
Cross-platform Product Matching Based on Entity Alignment of Knowledge Graph with RAEA model
- 设计双阶段匹配流程，先粗筛后精筛。
- 新模型RAEA在DBP15K上平均Hits@1达6.59%，优于12个基线。
- 适合做跨平台商品识别与知识图谱对齐的研究者参考。
商品匹配旨在识别不同平台上销售的相同或相似商品。通过构建知识图谱（KG），该问题可转化为实体对齐（EA）任务，即从不同知识图谱中发现等价实体。现有方法未能有效同时利用属性三元组与关系三元组，尤其忽视二者间的交互作用。本文提出两阶段流程：粗筛选与细筛选。在细筛选阶段，引入新型实体对齐框架RAEA（Relation-aware and Attribute-aware Graph Attention Networks for Entity Alignment），聚焦属性三元组与关系三元组之间的交互。该模型通过属性感知实体编码器和关系感知图注意力网络，聚合来自属性与关系的对齐信号以生成实体表示。实验表明，RAEA在跨语言数据集DBP15K上平均Hits@1达到6.59%，显著优于12个基线模型；在单语言数据集DWY100K上表现也具竞争力。DBP15K与DWY100K的实验代码已开源至GitHub。
原文摘要 · Abstract (English)
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).
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