{"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/joint-matrix-tensor-factorization-for","title":"Joint Matrix-Tensor Factorization for Knowledge Base Inference","arxiv_id":"1706.00637","date":"2017-06-02","proceeding":null,"authors":["Prachi Jain","Shikhar Murty","Mausam","Soumen Chakrabarti"],"abstract":"While several matrix factorization (MF) and tensor factorization (TF) models\nhave been proposed for knowledge base (KB) inference, they have rarely been\ncompared across various datasets. Is there a single model that performs well\nacross datasets? If not, what characteristics of a dataset determine the\nperformance of MF and TF models? Is there a joint TF+MF model that performs\nrobustly on all datasets? We perform an extensive evaluation to compare popular\nKB inference models across popular datasets in the literature. In addition to\nanswering the questions above, we remove a limitation in the standard\nevaluation protocol for MF models, propose an extension to MF models so that\nthey can better handle out-of-vocabulary (OOV) entity pairs, and develop a\nnovel combination of TF and MF models. We also analyze and explain the results\nbased on models and dataset characteristics. Our best model is robust, and\nobtains strong results across all datasets.","url_abs":"http://arxiv.org/abs/1706.00637v1","url_pdf":"http://arxiv.org/pdf/1706.00637v1.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":"joint-matrix-tensor-factorization-for","repo_url":"https://github.com/dair-iitd/kbi","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"joint-matrix-tensor-factorization-for","repo_url":"https://github.com/MurtyShikhar/KBI","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"knowledge-base-completion","task_name":"Knowledge Base Completion"},{"task_slug":"knowledge-base-population","task_name":"Knowledge Base Population"},{"task_slug":"knowledge-graph-completion","task_name":"Knowledge Graph Completion"},{"task_slug":"knowledge-graph-embedding","task_name":"Knowledge Graph Embedding"},{"task_slug":"matrix-factorization-decomposition","task_name":"Matrix Factorization / Decomposition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}