{"url":"/method/mbs","slug":"mbs","name":"MBS","full_name":"Model-based Subsampling","full_name_withheld":false,"description_markdown":"To avoid the problem caused by low-frequent entity-relation pairs, our MBS uses the estimated probabilities from a trained model $\\mathbf{\\theta}'$ to calculate frequencies for each triplet and query. By using $\\mathbf{\\theta}'$, the NS loss in KGE with MBS is represented as follows:\r\n\\begin{align}\r\n    &\\ell_{mbs}(\\mathbf{\\theta};\\mathbf{\\theta}') \\nonumber \\\\\r\n=&-\\frac{1}{|D|}\\sum_{(x,y) \\in D} \\Bigl[A_{mbs}(\\mathbf{\\theta}')\\log(\\sigma(s_{\\mathbf{\\theta}}(x,y)+\\gamma))\\nonumber\\\\\r\n    &+\\frac{1}{\\nu}sum_{y_{i}\\sim p_n(y_{i}|x)}^{\\nu}B_{mbs}(\\mathbf{\\theta}')\\log(\\sigma(-s_{\\mathbf{\\theta}}(x,y_i)-\\gamma))\\Bigr],\r\n\\end{align}","description_state":"present","introduced_year":null,"introduced_by":{"title":"Model-based Subsampling for Knowledge Graph Completion","paper":"/paper/model-based-subsampling-for-knowledge-graph","first_author":"Xincan Feng","n_authors":4,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/model-based-subsampling-for-knowledge-graph"},"source":{"url":"https://arxiv.org/abs/2309.09296v1","title":"Model-based Subsampling for Knowledge Graph 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