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

• URL: http://arxiv.org/abs/2512.07232v1
• Date: Mon, 08 Dec 2025 07:23:41 GMT
• Title: Cross-platform Product Matching Based on Entity Alignment of Knowledge Graph with RAEA model
• Authors: Wenlong Liu, Jiahua Pan, Xingyu Zhang, Xinxin Gong, Yang Ye, Xujin Zhao, Xin Wang, Kent Wu, Hua Xiang, Houmin Yan, Qingpeng Zhang,
• Abstract summary: This paper introduces a two-stage pipeline consisting of rough filter and fine filter to match products from eBay and Amazon.<n>For fine filtering, a new framework for Entity Alignment, Relation-aware and Attribute-aware Graph Attention Networks for Entity Alignment (RAEA) is employed.<n>RAEA focuses on the interactions between attribute triples and relation triples, where the entity representation aggregates the alignment signals from attributes and relations.
• Score: 11.750769565213062
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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).

Related papers

• Entity Disambiguation via Fusion Entity Decoding [68.77265315142296]
  We propose an encoder-decoder model to disambiguate entities with more detailed entity descriptions. We observe +1.5% improvements in end-to-end entity linking in the GERBIL benchmark compared with EntQA.
  arXiv  Detail & Related papers  (2024-04-02T04:27:54Z)
• Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks [58.43972540643903]
  We propose HyperGraph neural network for ERE ($hgnn$), which is built upon the PL-marker (a state-of-the-art marker-based pipleline model) To alleviate error propagation,we use a high-recall pruner mechanism to transfer the burden of entity identification and labeling from the NER module to the joint module of our model. Experiments on three widely used benchmarks for ERE task show significant improvements over the previous state-of-the-art PL-marker.
  arXiv  Detail & Related papers  (2023-10-26T08:36:39Z)
• From Alignment to Entailment: A Unified Textual Entailment Framework for Entity Alignment [17.70562397382911]
  Existing methods usually encode the triples of entities as embeddings and learn to align the embeddings. We transform both triples into unified textual sequences, and model the EA task as a bi-directional textual entailment task. Our approach captures the unified correlation pattern of two kinds of information between entities, and explicitly models the fine-grained interaction between original entity information.
  arXiv  Detail & Related papers  (2023-05-19T08:06:50Z)
• Guiding Neural Entity Alignment with Compatibility [32.22210683891481]
  We argue that different entities within one Knowledge Graph should have compatible counterparts in the other KG. Making compatible predictions should be one of the goals of training an EA model along with fitting the labelled data. We devise a training framework by addressing three problems: (1) how to measure the compatibility of an EA model; (2) how to inject the property of being compatible into an EA model; and (3) how to optimise parameters of the compatibility model.
  arXiv  Detail & Related papers  (2022-11-29T00:05:08Z)
• Entity-Graph Enhanced Cross-Modal Pretraining for Instance-level Product Retrieval [152.3504607706575]
  This research aims to conduct weakly-supervised multi-modal instance-level product retrieval for fine-grained product categories. We first contribute the Product1M datasets, and define two real practical instance-level retrieval tasks. We exploit to train a more effective cross-modal model which is adaptively capable of incorporating key concept information from the multi-modal data.
  arXiv  Detail & Related papers  (2022-06-17T15:40:45Z)
• Pack Together: Entity and Relation Extraction with Levitated Marker [61.232174424421025]
  We propose a novel span representation approach, named Packed Levitated Markers, to consider the dependencies between the spans (pairs) by strategically packing the markers in the encoder. Our experiments show that our model with packed levitated markers outperforms the sequence labeling model by 0.4%-1.9% F1 on three flat NER tasks, and beats the token concat model on six NER benchmarks.
  arXiv  Detail & Related papers  (2021-09-13T15:38:13Z)
• EchoEA: Echo Information between Entities and Relations for Entity Alignment [1.1470070927586016]
  We propose a novel framework, Echo Entity Alignment (EchoEA), which leverages self-attention mechanism to spread entity information to relations and echo back to entities. The experimental results on three real-world cross-lingual datasets are stable at around 96% at hits@1 on average.
  arXiv  Detail & Related papers  (2021-07-07T07:34:21Z)
• RAGA: Relation-aware Graph Attention Networks for Global Entity Alignment [14.287681294725438]
  We propose a novel framework based on Relation-aware Graph Attention Networks to capture the interactions between entities and relations. Our framework adopts the self-attention mechanism to spread entity information to the relations and then aggregate relation information back to entities.
  arXiv  Detail & Related papers  (2021-03-01T06:30:51Z)
• Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment [100.19568734815732]
  Entity alignment (EA) aims at building a unified Knowledge Graph (KG) of rich content by linking the equivalent entities from various KGs. attribute triples can also provide crucial alignment signal but have not been well explored yet. We propose to utilize an attributed value encoder and partition the KG into subgraphs to model the various types of attribute triples efficiently.
  arXiv  Detail & Related papers  (2020-10-07T08:03:58Z)
• HittER: Hierarchical Transformers for Knowledge Graph Embeddings [85.93509934018499]
  We propose Hitt to learn representations of entities and relations in a complex knowledge graph. Experimental results show that Hitt achieves new state-of-the-art results on multiple link prediction. We additionally propose a simple approach to integrate Hitt into BERT and demonstrate its effectiveness on two Freebase factoid answering datasets.
  arXiv  Detail & Related papers  (2020-08-28T18:58:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.