W3C recommendation, World Wide Web Consortium (2004)īontcheva, K., Tablan, V., Maynard, D., Cunningham, H.: Evolving GATE to meet new challenges in language engineering. ACM, New York (2005)īrickley, D., Guha, R.V.: RDF Vocabulary Description Language 1.0: RDF Schema. Ireson, N., Ciravegna, F., Califf, M.E., Freitag, D., Kushmerick, N., Lavelli, A.: Evaluating Machine Learning for Information Extraction. LWA 2008 (Special Track on Knowledge Discovery and Machine Learning) (2008) This process is experimental and the keywords may be updated as the learning algorithm improves.Ītzmüller, M., Klügl, P., Puppe, F.: Rule-Based Information Extraction for Structured Data Acquisition using TextMarker.

These keywords were added by machine and not by the authors. This work outlines iDocument’s ontology-based architecture, the use of SPARQL queries as extraction templates and an evaluation of iDocument in an automatic document annotation scenario.

iDocument provides a pipeline architecture, an extraction template interface and the ability of exchanging domain ontologies for performing information extraction tasks. Due to the huge amount of text data in the Ontology-based information extraction exploits domain ontologies with formalized and structured domain knowledge for extracting domain-relevant information from un-annotated and unstructured text.

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