Large-Scale Pattern-Based Information Extraction from the World Wide Web
Abstract:
Extracting information from text is the task of obtaining structured, machine-processable facts from information that is mentioned in an unstructured manner. It thus allows systems to automatically aggregate information for further analysis, efficient retrieval, automatic validation, or appropriate visualization.
This thesis explores the potential of using textual patterns for Information Extraction from the World Wide Web.
This thesis explores the potential of using textual patterns for Information Extraction from the World Wide Web.
| Zugehörige Institution(en) am KIT | Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB) |
| Publikationstyp | Hochschulschrift |
| Publikationsjahr | 2010 |
| Sprache | Englisch |
| Identifikator | urn:nbn:de:swb:90-154237 KITopen-ID: 1000015423 |
| Verlag | Karlsruher Institut für Technologie (KIT) |
| Art der Arbeit | Dissertation |
| Fakultät | Fakultät für Wirtschaftswissenschaften (WIWI) |
| Institut | Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB) |
| Prüfungsdaten | 22.01.2010 |
| Schlagwörter | Information Extraction, World Wide Web, Text Mining, Information Retrieval, Semantic Web, Machine Learning, Data Mining |
| Relationen in KITopen | |
| Referent/Betreuer | Studer, R. |