[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85665-en":3,"doc-seo-85665-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},85665,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey","Graph Neural Networks (GNNs) are widely used in Knowledge Graphs (KGs) because they directly learn from graph-structured data. A systematic review spanning the full knowledge graph technologies pipeline is still missing. This survey introduces a two-level taxonomy covering the KG pipeline (construction, embedding, reasoning, and applications) and a GNN-based perspective (including GCN, GAT, and HGNN). It evaluates benefits by task characteristics, reviews models accordingly, and summarizes strengths, limitations, open challenges, and future directions.","arXiv :2607 .09666v1 [ cs .LG] 12 May 2026  \nKnowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey  \nCHENGCHENG SUN, School of Safety Engineering, China University of Mining and Technology, China JIAYUN TIAN*, School of Computer Science and Technology, China University of Mining and Technology, China CHENG ZHAI*, State Key Laboratory of Coal Mine Disaster Prevention and Control, China University of Mining and Technology, China  \nZHIXIAO WANG*, School of Computer Science and Technology, China University of Mining and Technology, China  \nYAJIE SONG, School of Computer Science and Technology, China University of Mining and Technology, China XIAOBIN RUI, School of Computer Science and Technology, China University of Mining and Technology, China JIAN ZHANG, School of Computer Science and Technology, China University of Mining and Technology, China PHILIP S. YU, Department of Computer Science, University of Illinois at Chicago, US  \nAbstract: Graph Neural Networks (GNNs) have emerged as a powerful paradigm in Knowledge Graphs (KGs) due to their intrinsic ability to model graph-structured data. However, there remains a lack of a systematic review about GNN-based methodologies across the entire knowledge graph technologies pipeline. To address this gap, we first propose a novel two-level taxonomy framework for GNN-based knowledge graph technologies: the KG technologies pipeline and GNN-based perspective. Specifically, the knowledge graph technologies pipeline covers knowledge graph construction, knowledge graph embedding, knowledge reasoning and knowledge graph applications. Meanwhile, the GNN-based perspective provides a new categorization of knowledge graph technologies with GNN models, such as GCN, GAT, and HGNN. Then, we analyze the advantages of GNN technology based on the characteristics of different tasks in the knowledge graph lifecycle. Furthermore, we detailed review various GNN-based models for knowledge graph following the proposed taxonomy, and summarize strengths and limitations. Finally, we discuss unresolved challenges and outline promising directions for future research.  \nCCS Concepts: • Computing methodologies → Artificial intelligence.  \nAdditional Key Words and Phrases: Knowledge Graphs, Graph Neural Networks, Knowledge Graph Construction, Knowledge Graph Embedding, Knowledge Reasoning  \nAuthors’ Contact Information: Chengcheng Sun, School of Safety Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, China, [scc@cumt.edu.cn](scc@cumt.edu.cn); Jiayun Tian*, School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu, China, [tianjy@cumt.edu.cn](tianjy@cumt.edu.cn); Cheng Zhai*, State Key Laboratory of Coal Mine Disaster Prevention and Control, China University of Mining and Technology, Xuzhou, Jiangsu, China, [greatzc@cumt.edu.cn](greatzc@cumt.edu.cn); Zhixiao Wang*, School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu, China, [zhxwang@cumt.edu.cn](zhxwang@cumt.edu.cn); Yajie Song, School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu, China, [songyajie@cumt.edu.cn](songyajie@cumt.edu.cn); Xiaobin Rui, School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu, China, [ruixiaobin@cumt.edu.cn](ruixiaobin@cumt.edu.cn); Jian Zhang, School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu, China, [zhangjian10231209@cumt.edu.cn](zhangjian10231209@cumt.edu.cn); Philip S. Yu, Department of Computer Science, University of Illinois at Chicago, Chicago, IL, US, [psyu@uic.edu](psyu@uic.edu).  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.","cbCaib1gtl2ndvIS","https://ap.wps.com/l/cbCaib1gtl2ndvIS","pdf",2150189,1,44,"English","en",105,"# Introduction\n# Knowledge Graphs and Knowledge Representation","[{\"question\":\"What gap does the survey address about GNN methods in knowledge graph technologies?\",\"answer\":\"A systematic review covering GNN-based methodologies across the entire knowledge graph technologies pipeline is still lacking, motivating the survey’s proposed framework.\"},{\"question\":\"How does the survey organize GNN-based knowledge graph technologies?\",\"answer\":\"It proposes a two-level taxonomy: the knowledge graph technologies pipeline (construction, embedding, reasoning, applications) and a GNN-based perspective categorized by GNN model families such as GCN, GAT, and HGNN.\"},{\"question\":\"What does the survey do after introducing its taxonomy?\",\"answer\":\"It analyzes advantages of GNN technology according to task characteristics, performs a detailed review of GNN-based knowledge graph models under the taxonomy, and summarizes strengths, limitations, unresolved challenges, and promising research 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gap does the survey address about GNN methods in knowledge graph technologies?","Question",{"text":75,"@type":76},"A systematic review covering GNN-based methodologies across the entire knowledge graph technologies pipeline is still lacking, motivating the survey’s proposed framework.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the survey organize GNN-based knowledge graph technologies?",{"text":80,"@type":76},"It proposes a two-level taxonomy: the knowledge graph technologies pipeline (construction, embedding, reasoning, applications) and a GNN-based perspective categorized by GNN model families such as GCN, GAT, and HGNN.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the survey do after introducing its taxonomy?",{"text":84,"@type":76},"It analyzes advantages of GNN technology according to task characteristics, performs a detailed review of GNN-based knowledge graph models under the taxonomy, and summarizes strengths, limitations, unresolved challenges, 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