{"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-representation-learning-a","title":"Knowledge Representation Learning: A Quantitative Review","arxiv_id":"1812.10901","date":"2018-12-28","proceeding":null,"authors":["Yankai Lin","Xu Han","Ruobing Xie","Zhiyuan Liu","Maosong Sun"],"abstract":"Knowledge representation learning (KRL) aims to represent entities and\nrelations in knowledge graph in low-dimensional semantic space, which have been\nwidely used in massive knowledge-driven tasks. In this article, we introduce\nthe reader to the motivations for KRL, and overview existing approaches for\nKRL. Afterwards, we extensively conduct and quantitative comparison and\nanalysis of several typical KRL methods on three evaluation tasks of knowledge\nacquisition including knowledge graph completion, triple classification, and\nrelation extraction. We also review the real-world applications of KRL, such as\nlanguage modeling, question answering, information retrieval, and recommender\nsystems. Finally, we discuss the remaining challenges and outlook the future\ndirections for KRL. The codes and datasets used in the experiments can be found\nin https://github.com/thunlp/OpenKE.","url_abs":"http://arxiv.org/abs/1812.10901v1","url_pdf":"http://arxiv.org/pdf/1812.10901v1.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-representation-learning-a","repo_url":"https://github.com/thunlp/OpenKE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"knowledge-representation-learning-a","repo_url":"https://github.com/shaoxiongji/awesome-knowledge-graph","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"information-retrieval","task_name":"Information Retrieval"},{"task_slug":"knowledge-graph-completion","task_name":"Knowledge Graph Completion"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"},{"task_slug":"relation-extraction","task_name":"Relation Extraction"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"triple-classification","task_name":"Triple Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1812.10901","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}