{"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/triple-trustworthiness-measurement-for","title":"Triple Trustworthiness Measurement for Knowledge Graph","arxiv_id":"1809.09414","date":"2018-09-25","proceeding":null,"authors":["Shengbin Jia","Yang Xiang","Xiaojun Chen"],"abstract":"The Knowledge graph (KG) uses the triples to describe the facts in the real\nworld. It has been widely used in intelligent analysis and applications.\nHowever, possible noises and conflicts are inevitably introduced in the process\nof constructing. And the KG based tasks or applications assume that the\nknowledge in the KG is completely correct and inevitably bring about potential\ndeviations. In this paper, we establish a knowledge graph triple\ntrustworthiness measurement model that quantify their semantic correctness and\nthe true degree of the facts expressed. The model is a crisscrossing neural\nnetwork structure. It synthesizes the internal semantic information in the\ntriples and the global inference information of the KG to achieve the\ntrustworthiness measurement and fusion in the three levels of entity level,\nrelationship level, and KG global level. We analyzed the validity of the model\noutput confidence values, and conducted experiments in the real-world dataset\nFB15K (from Freebase) for the knowledge graph error detection task. The\nexperimental results showed that compared with other models, our model achieved\nsignificant and consistent improvements.","url_abs":"http://arxiv.org/abs/1809.09414v3","url_pdf":"http://arxiv.org/pdf/1809.09414v3.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":"triple-trustworthiness-measurement-for","repo_url":"https://github.com/TJUNLP/TTMF","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}