{"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/retrofitting-distributional-embeddings-to","title":"Retrofitting Distributional Embeddings to Knowledge Graphs with Functional Relations","arxiv_id":"1708.00112","date":"2017-08-01","proceeding":"COLING 2018 8","authors":["Benjamin J. Lengerich","Andrew L. Maas","Christopher Potts"],"abstract":"Knowledge graphs are a versatile framework to encode richly structured data\nrelationships, but it can be challenging to combine these graphs with\nunstructured data. Methods for retrofitting pre-trained entity representations\nto the structure of a knowledge graph typically assume that entities are\nembedded in a connected space and that relations imply similarity. However,\nuseful knowledge graphs often contain diverse entities and relations (with\npotentially disjoint underlying corpora) which do not accord with these\nassumptions. To overcome these limitations, we present Functional Retrofitting,\na framework that generalizes current retrofitting methods by explicitly\nmodeling pairwise relations. Our framework can directly incorporate a variety\nof pairwise penalty functions previously developed for knowledge graph\ncompletion. Further, it allows users to encode, learn, and extract information\nabout relation semantics. We present both linear and neural instantiations of\nthe framework. Functional Retrofitting significantly outperforms existing\nretrofitting methods on complex knowledge graphs and loses no accuracy on\nsimpler graphs (in which relations do imply similarity). Finally, we\ndemonstrate the utility of the framework by predicting new drug--disease\ntreatment pairs in a large, complex health knowledge graph.","url_abs":"http://arxiv.org/abs/1708.00112v3","url_pdf":"http://arxiv.org/pdf/1708.00112v3.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":"retrofitting-distributional-embeddings-to","repo_url":"https://github.com/roaminsight/roamresearch","is_official":1,"mentioned_in_paper":0,"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"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1708.00112","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}