{"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/kblrn-end-to-end-learning-of-knowledge-base","title":"KBLRN : End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features","arxiv_id":"1709.04676","date":"2017-09-14","proceeding":null,"authors":["Alberto Garcia-Duran","Mathias Niepert"],"abstract":"We present KBLRN, a framework for end-to-end learning of knowledge base\nrepresentations from latent, relational, and numerical features. KBLRN\nintegrates feature types with a novel combination of neural representation\nlearning and probabilistic product of experts models. To the best of our\nknowledge, KBLRN is the first approach that learns representations of knowledge\nbases by integrating latent, relational, and numerical features. We show that\ninstances of KBLRN outperform existing methods on a range of knowledge base\ncompletion tasks. We contribute a novel data sets enriching commonly used\nknowledge base completion benchmarks with numerical features. The data sets are\navailable under a permissive BSD-3 license. We also investigate the impact\nnumerical features have on the KB completion performance of KBLRN.","url_abs":"http://arxiv.org/abs/1709.04676v3","url_pdf":"http://arxiv.org/pdf/1709.04676v3.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":"kblrn-end-to-end-learning-of-knowledge-base","repo_url":"https://github.com/nle-ml/mmkb","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"BSD-3-Clause"}},{"paper_slug":"kblrn-end-to-end-learning-of-knowledge-base","repo_url":"https://github.com/mniepert/mmkb","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"knowledge-base-completion","task_name":"Knowledge Base Completion"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1709.04676","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}