{"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/mag-a-multilingual-knowledge-base-agnostic","title":"MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach","arxiv_id":"1707.05288","date":"2017-07-17","proceeding":null,"authors":["Diego Moussallem","Ricardo Usbeck","Michael Röder","Axel-Cyrille Ngonga Ngomo"],"abstract":"Entity linking has recently been the subject of a significant body of\nresearch. Currently, the best performing approaches rely on trained\nmono-lingual models. Porting these approaches to other languages is\nconsequently a difficult endeavor as it requires corresponding training data\nand retraining of the models. We address this drawback by presenting a novel\nmultilingual, knowledge-based agnostic and deterministic approach to entity\nlinking, dubbed MAG. MAG is based on a combination of context-based retrieval\non structured knowledge bases and graph algorithms. We evaluate MAG on 23 data\nsets and in 7 languages. Our results show that the best approach trained on\nEnglish datasets (PBOH) achieves a micro F-measure that is up to 4 times worse\non datasets in other languages. MAG, on the other hand, achieves\nstate-of-the-art performance on English datasets and reaches a micro F-measure\nthat is up to 0.6 higher than that of PBOH on non-English languages.","url_abs":"http://arxiv.org/abs/1707.05288v3","url_pdf":"http://arxiv.org/pdf/1707.05288v3.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":"mag-a-multilingual-knowledge-base-agnostic","repo_url":"https://github.com/AKSW/AGDISTIS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"entity-linking","task_name":"Entity Linking"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}