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Published in:
2010 | OriginalPaper | Chapter
KDTA: Automated Knowledge-Driven Text Annotation
Authors : Katerina Papantoniou, George Tsatsaronis, Georgios Paliouras
Published in: Machine Learning and Knowledge Discovery in Databases
Publisher: Springer Berlin Heidelberg
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In this paper we demonstrate a system that automatically annotates text documents with a given domain ontology’s concepts. The annotation process utilizes lexical and Web resources to analyze the semantic similarity of text components with any of the ontology concepts, and outputs a list with the proposed annotations, accompanied with appropriate confidence values. The demonstrated system is available online and free to use, and it constitutes one of the main components of the
KDTA
(
Knowledge-Driven Text Analysis
) module of the
CASAM
European research project.