2021/05/02 by Wen Zhang, Zhang, Wen, Chi-Man Wong +9 · 2 citations
Computer Science · #Advanced Graph Neural Networks #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Recommender Systems and Techniques #Topic Modeling #cs.AI
paper · pdf · doi:10.48550/arxiv.2105.00388
Paper accepted by ICDE2021
arxiv created 2021/05/02 · openalex publication_date 2021/05/02 · arxiv updated 2021/05/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In recent years, knowledge graphs have been widely applied to organize data in a uniform way and enhance many tasks that require knowledge, for example, online shopping which has greatly facilitated people's life. As a backbone for online shopping platforms, we built a billion-scale e-commerce product knowledge graph for various item knowledge services such as item recommendation. However, such knowledge services usually include tedious data selection and model design for knowledge infusion, which might bring inappropriate results. Thus, to avoid this problem, we propose a Pre-trained Knowledge Graph Model (PKGM) for our billion-scale e-commerce product knowledge graph, providing item knowledge services in a uniform way for embedding-based models without accessing triple data in the knowledge graph. Notably, PKGM could also complete knowledge graphs during servicing, thereby overcoming the common incompleteness issue in knowledge graphs. We test PKGM in three knowledge-related tasks including item classification, same item identification, and recommendation. Experimental results show PKGM successfully improves the performance of each task.