{"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/knowledge-transfer-for-out-of-knowledge-base","title":"Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach","arxiv_id":"1706.05674","date":"2017-06-18","proceeding":null,"authors":["Takuo Hamaguchi","Hidekazu Oiwa","Masashi Shimbo","Yuji Matsumoto"],"abstract":"Knowledge base completion (KBC) aims to predict missing information in a\nknowledge base.In this paper, we address the out-of-knowledge-base (OOKB)\nentity problem in KBC:how to answer queries concerning test entities not\nobserved at training time. Existing embedding-based KBC models assume that all\ntest entities are available at training time, making it unclear how to obtain\nembeddings for new entities without costly retraining. To solve the OOKB entity\nproblem without retraining, we use graph neural networks (Graph-NNs) to compute\nthe embeddings of OOKB entities, exploiting the limited auxiliary knowledge\nprovided at test time.The experimental results show the effectiveness of our\nproposed model in the OOKB setting.Additionally, in the standard KBC setting in\nwhich OOKB entities are not involved, our model achieves state-of-the-art\nperformance on the WordNet dataset. The code and dataset are available at\nhttps://github.com/takuo-h/GNN-for-OOKB","url_abs":"http://arxiv.org/abs/1706.05674v2","url_pdf":"http://arxiv.org/pdf/1706.05674v2.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":"knowledge-transfer-for-out-of-knowledge-base","repo_url":"https://github.com/takuo-h/GNN-for-OOKB","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"},{"task_slug":"knowledge-base-completion","task_name":"Knowledge Base Completion"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1706.05674","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}