A Relational Tucker Decomposition for Multi-Relational Link Prediction.pdf

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文件名称：A Relational Tucker Decomposition for Multi-Relational Link Prediction.pdf
文件大小：888KB
文件格式：PDF
更新时间：2022-08-29 08:42:54
KG We propose the Relational Tucker3 (RT) decomposition for multi-relational link prediction in knowledge graphs. We show that many existing knowledge graph embedding models are special cases of the RT decomposition with certain predefined sparsity patterns in its components. In contrast to these prior models, RT decouples the sizes of entity and relation embeddings, allows parameter sharing across relations, and does not make use of a predefined sparsity pattern. We use theRTdecompositionasatooltoexplorewhether it is possible and beneficial to automatically learn sparsity patterns, and whether dense models can outperformsparsemodels(usingthesamenumber of parameters). Our experiments indicate that— depending on the dataset–both questions can be answered affirmatively.