Semantic Annotation and Classification in Practice

被引:9
作者
Rocha, Oscar Rodriguez
Vagliano, Iacopo
Figueroa, Cristhian
Cairo, Federico
Futia, Giuseppe
Licciardi, Carlo Alberto
Marengo, Marco
Morando, Federico
机构
[1] Politecnico di Torino, United States
[2] Nexa Center for Internet and Society, United States
[3] Telecom Italia, United States
关键词
information extraction; linked data; semantic annotation; semantic classification;
D O I
10.1109/MITP.2015.29
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The Web's evolution into a Semantic Web and the continuous increase in the amount of data published as linked data open up new opportunities for annotation and categorization systems to reuse these data as semantic knowledge bases. Accordingly, information extraction systems use linked data to exploit semantic knowledge bases, which can be interconnected and structured to increase the precision and recall of annotation and categorization mechanisms. TellMeFirst classifies and enriches textual documents written in English and Italian. Although various works present solutions for text annotation and classification, this article describes and studies the use case of a telecommunications operator that has adopted TellMeFirst to add value to two services available to its users: FriendTV and Society. © 2015 IEEE.
引用
收藏
页码:33 / 39
页数:7
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