Semantic Annotation and Classification in Practice
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...
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| Veröffentlicht in: | IT professional 2015-03, Vol.17 (2), p.33-39 |
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| container_title | IT professional |
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| creator | Rocha, Oscar Rodriguez Vagliano, Iacopo Figueroa, Cristhian Cairo, Federico Futia, Giuseppe Licciardi, Carlo Alberto Marengo, Marco Morando, Federico |
| description | 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. |
| doi_str_mv | 10.1109/MITP.2015.29 |
| format | Article |
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| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Annotations Classification Classification algorithms Electronic publishing Evolution information extraction Information retrieval Information science Information services Information technology Knowledge based systems Knowledge bases (artificial intelligence) Linked Data Recall semantic annotation semantic classification Semantic Web Semantics Studies Texts Training |
| title | Semantic Annotation and Classification in Practice |
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