{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/greenkgc-a-lightweight-knowledge-graph","title":"GreenKGC: A Lightweight Knowledge Graph Completion Method","arxiv_id":"2208.09137","date":"2022-08-19","proceeding":null,"authors":["Yun-Cheng Wang","Xiou Ge","Bin Wang","C. -C. Jay Kuo"],"abstract":"Knowledge graph completion (KGC) aims to discover missing relationships between entities in knowledge graphs (KGs). Most prior KGC work focuses on learning embeddings for entities and relations through a simple scoring function. Yet, a higher-dimensional embedding space is usually required for a better reasoning capability, which leads to a larger model size and hinders applicability to real-world problems (e.g., large-scale KGs or mobile/edge computing). A lightweight modularized KGC solution, called GreenKGC, is proposed in this work to address this issue. GreenKGC consists of three modules: representation learning, feature pruning, and decision learning, to extract discriminant KG features and make accurate predictions on missing relationships using classifiers and negative sampling. Experimental results demonstrate that, in low dimensions, GreenKGC can outperform SOTA methods in most datasets. In addition, low-dimensional GreenKGC can achieve competitive or even better performance against high-dimensional models with a much smaller model size.","url_abs":"https://arxiv.org/abs/2208.09137v2","url_pdf":"https://arxiv.org/pdf/2208.09137v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"greenkgc-a-lightweight-knowledge-graph","repo_url":"https://github.com/yunchengwang/greenkgc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"edge-computing","task_name":"Edge-computing"},{"task_slug":"knowledge-graph-completion","task_name":"Knowledge Graph Completion"},{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"},{"task_slug":"link-prediction","task_name":"Link Prediction"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"triple-classification","task_name":"Triple Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/link-prediction-on-fb15k-237","task":"Link Prediction","dataset":"FB15k-237","model":"RotatE + GreenKGC (Ours)","rank_in_archive_order":23,"of":75,"metrics":{"Hits@1":"0.265","Hits@10":"0.507","Hits@3":"0.369","MRR":"0.345"},"uses_additional_data":false},{"leaderboard":"/sota/link-prediction-on-fb15k-237","task":"Link Prediction","dataset":"FB15k-237","model":"TransE + GreenKGC (Ours)","rank_in_archive_order":35,"of":75,"metrics":{"Hits@1":"0.251","Hits@10":"0.493","Hits@3":"0.356","MRR":"0.331"},"uses_additional_data":false},{"leaderboard":"/sota/link-prediction-on-wn18rr","task":"Link Prediction","dataset":"WN18RR","model":"RotatE + GreenKGC (Ours)","rank_in_archive_order":68,"of":75,"metrics":{"Hits@10":"0.491","Hits@3":"0.43","MRR":"0.411"},"uses_additional_data":false},{"leaderboard":"/sota/link-prediction-on-wn18rr","task":"Link Prediction","dataset":"WN18RR","model":"TransE + GreenKGC (Ours)","rank_in_archive_order":70,"of":75,"metrics":{"Hits@1":"0.3","Hits@10":"0.413","Hits@3":"0.365","MRR":"0.342"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2208.09137","atlas_url":"https://app.syntology.ai/?focus=2208.09137","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}