Semantic Relationship Discovery with Wikipedia Structure
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1770-1774.
Abstract
Thanks to the idea of social collaboration, Wikipedia has accumulated vast amount of semi-structured knowledge in which the link structure reflects human's cognition on semantic relationship to some extent. In this paper, we proposed a novel method RCRank to jointly compute concept-concept relatedness and concept-category relatedness base on the assumption that information carried in concept-concept links and concept-category links can mutually reinforce each other. Different from previous work, RCRank can not only find semantically related concepts but also interpret their relations by categories. Experimental results on concept recommendation and relation interpretation show that our method substantially outperforms classical methods.
@inproceedings{ijcai2011p297,
title = {Semantic Relationship Discovery with Wikipedia Structure},
author = {Bu, Fan and Hao, Yu and Zhu, Xiaoyan},
booktitle = {Proceedings of the Twenty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-11}},
publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
editor = {Toby Walsh},
pages = {1770--1774},
year = {2011},
month = {7},
doi = {10.5591/978-1-57735-516-8/IJCAI11-297},
url = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-297},
}
TY - CPAPER TI - Semantic Relationship Discovery with Wikipedia Structure AU - Bu, Fan AU - Hao, Yu AU - Zhu, Xiaoyan T2 - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11 PB - AAAI Press/International Joint Conferences on Artificial Intelligence PY - 2011 SP - 1770 EP - 1774 DO - 10.5591/978-1-57735-516-8/IJCAI11-297 UR - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-297 ER -Copied
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