{"id":848998,"date":"2022-06-01T00:04:02","date_gmt":"2022-06-01T07:04:02","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/"},"modified":"2022-06-17T03:56:20","modified_gmt":"2022-06-17T10:56:20","slug":"house-knowledge-graph-embedding-with-householder-parameterization","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/house-knowledge-graph-embedding-with-householder-parameterization\/","title":{"rendered":"HousE: Knowledge Graph Embedding with Householder Parameterization"},"content":{"rendered":"
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The effectiveness of knowledge graph embedding (KGE) largely depends on the ability to model intrinsic relation patterns and map- ping properties. However, existing approaches can only capture some of them with insufficient modeling capacity. In this work, we propose a more powerful KGE framework named HousE, which in- volves a novel parameterization based on two kinds of Householder transformations: (1) Householder rotations to achieve superior ca- pacity of modeling relation patterns; (2) Householder projections to handle sophisticated relation mapping properties. Theoretically, HousE is capable of modeling crucial relation patterns and mapping properties simultaneously. Besides, HousE is a generalization of existing rotation-based models while extending the rotations to high-dimensional spaces. Empirically, HousE achieves new state- of-the-art performance on five benchmark datasets. Our code is available at https:\/\/github.com\/anrep\/HousE.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

The effectiveness of knowledge graph embedding (KGE) largely depends on the ability to model intrinsic relation patterns and map- ping properties. However, existing approaches can only capture some of them with insufficient modeling capacity. In this work, we propose a more powerful KGE framework named HousE, which in- volves a novel parameterization based on two 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