Fine-grained entity typing aims at identifying the semantic type of an entity in KB. Type information is very important in knowledge bases, but is unfortunately incomplete even in some large knowledge bases. Limitations of existing methods are either ignoring the structure and type information in KB or requiring large scale annotated corpus. To address these issues, we propose an attributed and predictive entity embedding method, which can fully utilize various kinds of information comprehensively. Extensive experiments on real DBpedia dataset show that our proposed method significantly outperforms 8 state-of-the-art methods, with 3.4% and 2.9% improvement in Mi-Fl and Ma-Fl, respectively.
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