TransH
Tags: #machine learning #KGEquation
$$f_{r}(h,t) =h_{\perp} + d_{r}  t_{\perp} ^{2}_{2}=(h  w_{r}hw_{r}) + d_{r}  (t  w_{r}tw_{r}) ^{2}_{2}$$Latex Code
f_{r}(h,t) =h_{\perp} + d_{r}  t_{\perp} ^{2}_{2}=(h  w_{r}hw_{r}) + d_{r}  (t  w_{r}tw_{r}) ^{2}_{2}
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Introduction
Equation
Latex Code
f_{r}(h,t) =h_{\perp} + d_{r}  t_{\perp} ^{2}_{2}=(h  w_{r}hw_{r}) + d_{r}  (t  w_{r}tw_{r}) ^{2}_{2}
Explanation
TransH model learns lowdimensional representations of knowledge graphs triples on the hyperplane of the entities and relations. See paper Knowledge Graph Embedding by Translating on Hyperplanes for more details.
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