{"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/one-shot-relational-learning-for-knowledge","title":"One-Shot Relational Learning for Knowledge Graphs","arxiv_id":"1808.09040","date":"2018-08-27","proceeding":"EMNLP 2018 10","authors":["Wenhan Xiong","Mo Yu","Shiyu Chang","Xiaoxiao Guo","William Yang Wang"],"abstract":"Knowledge graphs (KGs) are the key components of various natural language\nprocessing applications. To further expand KGs' coverage, previous studies on\nknowledge graph completion usually require a large number of training instances\nfor each relation. However, we observe that long-tail relations are actually\nmore common in KGs and those newly added relations often do not have many known\ntriples for training. In this work, we aim at predicting new facts under a\nchallenging setting where only one training instance is available. We propose a\none-shot relational learning framework, which utilizes the knowledge extracted\nby embedding models and learns a matching metric by considering both the\nlearned embeddings and one-hop graph structures. Empirically, our model yields\nconsiderable performance improvements over existing embedding models, and also\neliminates the need of re-training the embedding models when dealing with newly\nadded relations.","url_abs":"http://arxiv.org/abs/1808.09040v1","url_pdf":"http://arxiv.org/pdf/1808.09040v1.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":"one-shot-relational-learning-for-knowledge","repo_url":"https://github.com/xwhan/One-shot-Relational-Learning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"knowledge-graph-completion","task_name":"Knowledge Graph Completion"},{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"},{"task_slug":"relational-reasoning","task_name":"Relational Reasoning"}],"methods":[],"datasets_introduced":[{"slug":"wiki-one","name":"Wiki-One","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1808.09040","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}