@inproceedings{c13213c581c5425289ef1a5ecc8cadd5,
title = "Representation learning with entity topics for knowledge graphs",
abstract = "Knowledge representation learning which represents triples as semantic embeddings has achieved tremendous success these years. Recent work aims at integrating the information of triples with texts, which has shown great advantages in alleviating the data sparsity problem. However, most of these methods are based on word-level information such as co-occurrence in texts, while ignoring the latent semantics of entities. In this paper, we propose an entity topic based representation learning (ETRL) method, which enhances the triple representations with the entity topics learned by the topic model. We evaluate our proposed method knowledge graph completion task. The experimental results show that our method outperforms most state-of-the-art methods. Specifically, we achieve a maximum improvement of 7.9\% in terms of hits@10.",
keywords = "Entity topics, Knowledge graph completion, Knowledge representation, Topic model",
author = "Xin Ouyang and Yan Yang and Liang He and Qin Chen and Jiacheng Zhang",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 10th International Conference on Knowledge Science, Engineering and Management, KSEM 2017 ; Conference date: 19-08-2017 Through 20-08-2017",
year = "2017",
doi = "10.1007/978-3-319-63558-3\_45",
language = "英语",
isbn = "9783319635576",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "534--542",
editor = "Zili Zhang and Yong Ge and Zhi Jin and Gang Li and Michael Blumenstein",
booktitle = "Knowledge Science, Engineering and Management - 10th International Conference, KSEM 2017, Proceedings",
address = "德国",
}