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Conference Paper: Cross-platform Product Matching Based on Knowledge Graph

TitleCross-platform Product Matching Based on Knowledge Graph
Authors
KeywordsEntity alignment
Knowledge graph
Product matching
Issue Date2023
Citation
Communications in Computer and Information Science, 2023, v. 1784 CCIS, p. 45-48 How to Cite?
AbstractProduct matching aims to identify similar or identical products sold on different platforms, which is crucial for retailers to adjust investment strategies. 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. This paper introduces a two-stage pipeline consisted of rough filter and fine filter. Based on product names and categories, we roughly match products in rough filtering. For fine filtering, a new framework for Entity Alignment, Relation-aware, and Attribute-aware Graph Attention Networks for Entity Alignment (RAEA), is employed. Experiments on eBay-Amazon dataset indicated that the two-stage pipeline performs well on the problem of cross-platform product matching.
Persistent Identifierhttp://hdl.handle.net/10722/330303
ISSN
2023 SCImago Journal Rankings: 0.203
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLiu, Wenlong-
dc.contributor.authorPan, Jiahua-
dc.contributor.authorZhang, Xingyu-
dc.contributor.authorGong, Xinxin-
dc.contributor.authorYe, Yang-
dc.contributor.authorZhao, Xujin-
dc.contributor.authorWang, Xin-
dc.contributor.authorWu, Kent-
dc.contributor.authorXiang, Hua-
dc.contributor.authorZhang, Qingpeng-
dc.date.accessioned2023-09-05T12:09:24Z-
dc.date.available2023-09-05T12:09:24Z-
dc.date.issued2023-
dc.identifier.citationCommunications in Computer and Information Science, 2023, v. 1784 CCIS, p. 45-48-
dc.identifier.issn1865-0929-
dc.identifier.urihttp://hdl.handle.net/10722/330303-
dc.description.abstractProduct matching aims to identify similar or identical products sold on different platforms, which is crucial for retailers to adjust investment strategies. 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. This paper introduces a two-stage pipeline consisted of rough filter and fine filter. Based on product names and categories, we roughly match products in rough filtering. For fine filtering, a new framework for Entity Alignment, Relation-aware, and Attribute-aware Graph Attention Networks for Entity Alignment (RAEA), is employed. Experiments on eBay-Amazon dataset indicated that the two-stage pipeline performs well on the problem of cross-platform product matching.-
dc.languageeng-
dc.relation.ispartofCommunications in Computer and Information Science-
dc.subjectEntity alignment-
dc.subjectKnowledge graph-
dc.subjectProduct matching-
dc.titleCross-platform Product Matching Based on Knowledge Graph-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-981-99-1354-1_5-
dc.identifier.scopuseid_2-s2.0-85152514503-
dc.identifier.volume1784 CCIS-
dc.identifier.spage45-
dc.identifier.epage48-
dc.identifier.eissn1865-0937-
dc.identifier.isiWOS:001012156900005-

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