LUKe and MIKe: learning from user knowledge and managing interactive knowledge extraction

Authors:
Steffen Metzger;Michael Stoll;Katja Hose;Ralf Schenkel
Affiliations:
Max-Planck-Institute for Informatics, Saarbrücken, Germany;University of Stuttgart, Stuttgart, Germany;Max-Planck-Institute for Informatics, Saarbrücken, Germany;Max-Planck-Institute for Informatics & Saarland University, Saarbrücken, Germany
Venue:
Proceedings of the 21st ACM international conference on Information and knowledge management
Year:
2012

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Abstract

Semantic recognition and annotation of unqiue enities and their relations is a key in understanding the essence contained in large text corpora. It typically requires a combination of efficient automatic methods and manual verification. Usually, both parts are seen as consecutive steps. In this demo we present MIKE, a user interface enabling the integration of user feedback into an iterative extraction process. We show how an extraction system can directly learn from such integrated user supervision. In general, this setup allows for stepwise training of the extraction system to a particular domain, while using user feedback early in the iterative extraction process improves extraction quality and reduces the overall human effort needed.