header

On Building and Publishing Linked Open Schema from Social Web Sites

30 Pages Posted: 12 Sep 2018 First Look: Accepted

See all articles by Tianxing Wu

Tianxing Wu

Southeast University (China)

Haofen Wang

Shanghai Jiao Tong University (SJTU); Gowild Robotics Co. Ltd

Guilin Qi

Southeast University (China) - School of Computer Science

Jiangang Zhu

Microsoft Corporation - Microsoft China

Tong Ruan

East China University of Science and Technology (ECUST)

Abstract

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 schema-level 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.

Keywords: Linked Data, Linked Open Schema, Schema-Level Knowledge, Social Web Sites

Suggested Citation

Wu, Tianxing and Wang, Haofen and Qi, Guilin and Zhu, Jiangang and Ruan, Tong, On Building and Publishing Linked Open Schema from Social Web Sites (September 12, 2018). Journal of Web Semantics First Look . Available at SSRN: https://ssrn.com/abstract=3248495 or http://dx.doi.org/10.2139/ssrn.3248495

Tianxing Wu (Contact Author)

Southeast University (China) ( email )

Sipailou 2#
Nanjing, Jiangsu Province 210096
China

Haofen Wang

Shanghai Jiao Tong University (SJTU) ( email )

KoGuan Law School
Shanghai 200030, Shanghai 200052
China

Gowild Robotics Co. Ltd ( email )

Shenzhen, 518057
China

Guilin Qi

Southeast University (China) - School of Computer Science ( email )

Sipailou 2#
Nanjing, Jiangsu Province 210096
China

Jiangang Zhu

Microsoft Corporation - Microsoft China ( email )

Suzhou, 215123
China

Tong Ruan

East China University of Science and Technology (ECUST) ( email )

Shanghai
China

Register to save articles to
your library

Register

Paper statistics

Abstract Views
134
PlumX Metrics
Downloads
7