{"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/does-william-shakespeare-really-write-hamlet","title":"Does William Shakespeare REALLY Write Hamlet? Knowledge Representation Learning with Confidence","arxiv_id":"1705.03202","date":"2017-05-09","proceeding":null,"authors":["Ruobing Xie","Zhiyuan Liu","Fen Lin","Leyu Lin"],"abstract":"Knowledge graphs (KGs), which could provide essential relational information\nbetween entities, have been widely utilized in various knowledge-driven\napplications. Since the overall human knowledge is innumerable that still grows\nexplosively and changes frequently, knowledge construction and update\ninevitably involve automatic mechanisms with less human supervision, which\nusually bring in plenty of noises and conflicts to KGs. However, most\nconventional knowledge representation learning methods assume that all triple\nfacts in existing KGs share the same significance without any noises. To\naddress this problem, we propose a novel confidence-aware knowledge\nrepresentation learning framework (CKRL), which detects possible noises in KGs\nwhile learning knowledge representations with confidence simultaneously.\nSpecifically, we introduce the triple confidence to conventional\ntranslation-based methods for knowledge representation learning. To make triple\nconfidence more flexible and universal, we only utilize the internal structural\ninformation in KGs, and propose three kinds of triple confidences considering\nboth local and global structural information. In experiments, We evaluate our\nmodels on knowledge graph noise detection, knowledge graph completion and\ntriple classification. Experimental results demonstrate that our\nconfidence-aware models achieve significant and consistent improvements on all\ntasks, which confirms the capability of CKRL modeling confidence with\nstructural information in both KG noise detection and knowledge representation\nlearning.","url_abs":"http://arxiv.org/abs/1705.03202v2","url_pdf":"http://arxiv.org/pdf/1705.03202v2.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":"does-william-shakespeare-really-write-hamlet","repo_url":"https://github.com/thunlp/CKRL","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"knowledge-graph-completion","task_name":"Knowledge Graph Completion"},{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"triple-classification","task_name":"Triple Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}