LUKe and MIKe: Learning from User Knowledge and Managing Interactive Knowledge 
Extraction

Metzger, Steffen; Stoll, Michael; Hose, Katja; Schenkel, Ralf

doi:10.1145/2396761.2398721

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LUKe and MIKe: Learning from User Knowledge and Managing Interactive Knowledge Extraction

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Metzger, Steffen
Databases and Information Systems, MPI for Informatics, Max Planck Society;
International Max Planck Research School, MPI for Informatics, Max Planck Society;

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Hose, Katja
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Schenkel, Ralf
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Citation

Metzger, S., Stoll, M., Hose, K., & Schenkel, R. (2012). LUKe and MIKe: Learning from User Knowledge and Managing Interactive Knowledge Extraction. In X.-W. Chen, G. Lebanon, H. Wang, & M. J. Zaki (Eds.), CIKM'12 (pp. 2671-2673). New York, NY: ACM.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0014-5960-A

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.