@inproceedings{e3119006b86f4020b26fb4476d288e7c,
title = "Extraction of class attributes from online encyclopedias",
abstract = "Class attributes are important resources in question answering, knowledge base building and semantic retrieval. In this paper, we propose an approach extracting class attributes from online encyclopedias. This approach combines the tolerance rough set model and semantic relatedness computing. Firstly, the implementation of the tolerance rough set model ensures a high precision of top-k extracted class attributes, and then the semantic relatedness computing improves the coverage of top-k extracted class attributes in order to achieve higher accuracy. Finally experiments on the extracted class attributes show the effectiveness of our approach.",
keywords = "Class attribute extraction, Normalized google distance, Semantic relatedness computing, Tolerance rough set",
author = "Hongzhi Guo and Qingcai Chen and Chunxiao Sun",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2014.; 13th International Conference on Machine Learning and Cybernetics, ICMLC 2014 ; Conference date: 13-07-2014 Through 16-07-2014",
year = "2014",
doi = "10.1007/978-3-662-45652-1\_30",
language = "英语",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "298--307",
editor = "Xizhao Wang and Qiang He and Chan, \{Patrick P.K.\} and Witold Pedrycz",
booktitle = "Machine Learning and Cybernetics - 13th International Conference, Proceedings",
address = "德国",
}