@inproceedings{b05d9182e5c04176ae25c2ae1b7f3a2f,
title = "Knowledge graph based question routing for community question answering",
abstract = "Community-based question answering (CQA) such as Stack Overflow and Quora face the challenge of providing unsolved questions with high expertise users to obtain high quality answers, which is called question routing. Many existing methods try to tackle this by learning user model from structure and topic information, which suffer from the sparsity issue of CQA data. In this paper, we propose a novel question routing method from the viewpoint of knowledge graph embedding. We integrate topic representations with network structure into a unified Knowledge Graph Question Routing framework, named as KGQR. The extensive experiments carried out on Stack Overflow data suggest that KGQR outperforms other state-of-the-art methods.",
keywords = "Community question answering, Embedding, Knowledge graph, Question routing",
author = "Zhu Liu and Kan Li and Dacheng Qu",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 24th International Conference on Neural Information Processing, ICONIP 2017 ; Conference date: 14-11-2017 Through 18-11-2017",
year = "2017",
doi = "10.1007/978-3-319-70139-4\_73",
language = "English",
isbn = "9783319701387",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "721--730",
editor = "Dongbin Zhao and Yuanqing Li and El-Alfy, \{El-Sayed M.\} and Derong Liu and Shengli Xie",
booktitle = "Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings",
address = "Germany",
}