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Type information is very important in knowledge bases,but some large knowledge bases are lack of type information due to the incompleteness of knowledge bases.In this paper,we propose to use a well-defined taxonomy to help complete the type information in some knowledge bases.Particularly,we present a novel embedding based hierarchical entity typing framework which uses learning to rank algorithm to enhance the performance of word-entity-type network embedding.In this way,we can take full advantage of labeled and unlabeled data.Extensive experiments on two real-world datasets of DBpedia show that our proposed method significantly outperforms 4 state-of-the-art methods,with 2.8%and 4.2%improvement in Mi-F1 and Ma-F1 respectively.