@inproceedings{jin-etal-2018-attributed,
    title = "Attributed and Predictive Entity Embedding for Fine-Grained Entity Typing in Knowledge Bases",
    author = "Jin, Hailong  and
      Hou, Lei  and
      Li, Juanzi  and
      Dong, Tiansi",
    editor = "Bender, Emily M.  and
      Derczynski, Leon  and
      Isabelle, Pierre",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/C18-1024/",
    pages = "282--292",
    abstract = "Fine-grained entity typing aims at identifying the semantic type of an entity in KB. Type information is very important in knowledge bases, but are 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 two real DBpedia datasets show that our proposed method significantly outperforms 8 state-of-the-art methods, with 4.0{\%} and 5.2{\%} improvement in Mi-F1 and Ma-F1, respectively."
}