{"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/a-review-of-relational-machine-learning-for","title":"A Review of Relational Machine Learning for Knowledge Graphs","arxiv_id":"1503.00759","date":"2015-03-02","proceeding":null,"authors":["Maximilian Nickel","Kevin Murphy","Volker Tresp","Evgeniy Gabrilovich"],"abstract":"Relational machine learning studies methods for the statistical analysis of\nrelational, or graph-structured, data. In this paper, we provide a review of\nhow such statistical models can be \"trained\" on large knowledge graphs, and\nthen used to predict new facts about the world (which is equivalent to\npredicting new edges in the graph). In particular, we discuss two fundamentally\ndifferent kinds of statistical relational models, both of which can scale to\nmassive datasets. The first is based on latent feature models such as tensor\nfactorization and multiway neural networks. The second is based on mining\nobservable patterns in the graph. We also show how to combine these latent and\nobservable models to get improved modeling power at decreased computational\ncost. Finally, we discuss how such statistical models of graphs can be combined\nwith text-based information extraction methods for automatically constructing\nknowledge graphs from the Web. To this end, we also discuss Google's Knowledge\nVault project as an example of such combination.","url_abs":"http://arxiv.org/abs/1503.00759v3","url_pdf":"http://arxiv.org/pdf/1503.00759v3.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":"a-review-of-relational-machine-learning-for","repo_url":"https://github.com/abhinavnagpal/KNOWLEDGE-GRAPH-PAPERS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"a-review-of-relational-machine-learning-for","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":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1503.00759","atlas_url":"https://app.syntology.ai/?focus=1503.00759","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}