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- W2890216969 abstract "Concepts, which represent a group of different instances sharing common properties, are essential information in knowledge representation. Most conventional knowledge embedding methods encode both entities (concepts and instances) and relations as vectors in a low dimensional semantic space equally, ignoring the difference between concepts and instances. In this paper, we propose a novel knowledge graph embedding model named TransC by differentiating concepts and instances. Specifically, TransC encodes each concept in knowledge graph as a sphere and each instance as a vector in the same semantic space. We use the relative positions to model the relations between concepts and instances (i.e.,instanceOf), and the relations between concepts and sub-concepts (i.e., subClassOf). We evaluate our model on both link prediction and triple classification tasks on the dataset based on YAGO. Experimental results show that TransC outperforms state-of-the-art methods, and captures the semantic transitivity for instanceOf and subClassOf relation. Our codes and datasets can be obtained from https://github.com/davidlvxin/TransC." @default.
- W2890216969 created "2018-09-27" @default.
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- W2890216969 date "2018-01-01" @default.
- W2890216969 modified "2023-10-02" @default.
- W2890216969 title "Differentiating Concepts and Instances for Knowledge Graph Embedding" @default.
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- W2890216969 doi "https://doi.org/10.18653/v1/d18-1222" @default.
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