文献类型：期刊文献

英文题名：On building and publishing Linked Open Schema from social Web sites

作者：Wu, Tianxing[1];Wang, Haofen[2];Qi, Guilin[1];Zhu, Jiangang[3];Ruan, Tong[4]

机构：[1]Southeast Univ, Nanjing 211189, Jiangsu, Peoples R China;[2]Gowild Robot Co Ltd, Shenzhen 518057, Peoples R China;[3]Microsoft, Suzhou 215123, Peoples R China;[4]East China Univ Sci & Technol, Shanghai 200237, Peoples R China

年份：2018

卷号：51

起止页码：39

外文期刊名：JOURNAL OF WEB SEMANTICS

收录：;EI(收录号：20182305276992);WOS:【SCI-EXPANDED(收录号:WOS:000441293800003)】；

基金：This work is supported in part by the National Natural Science Foundation of China under Grant No. 61672153 and the 863 Program under Grant No. 2015AA015406. We also thank Qiu Ji and Yuncheng Hua for their valuable feedback and detailed comments during the proofreading process.

语种：英文

外文关键词：Linked data; Linked Open Schema; Schema-level knowledge; Social Web sites

摘要：Schema-level knowledge is important for different semantic applications, such as reasoning, data integration and question answering. Compared with billions of triples describing millions of instances, current Linking Open Data has only a limited number of triples representing schema-level knowledge. To facilitate multilingual schema-level knowledge mining, we propose a general approach to learn Linked Open Schema (LOS) in different languages from social Web sites, which contain rich sources (i. e. taxonomies composed of categories and folksonomies consisting of tags) for mining large-scale schemalevel knowledge. The core part of the proposed approach is a semi-supervised learning method integrating rules to capture equal, subClassOf and relate relations among the collected categories and tags. We respectively apply the proposed approach to the selected English social Web sites and the Chinese ones, resulting in an English LOS and a Chinese LOS. We publish the English LOS and the Chinese one as open data on the Web with three access levels, i. e. data dump, lookup service and SPARQL endpoint. Experimental results show the high accuracy of the relations in the English LOS and the Chinese one. Compared with DBpedia, Yago, BabelNet, and Freebase, both the English LOS and the Chinese one not only have large-scale concepts, but also contain the largest number of subClassOf relations. (C) 2018 Elsevier B.V. All rights reserved.