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Konferenzbeitrag

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

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45032

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

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44645

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

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45380

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

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Zitation

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.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0014-5960-A
Zusammenfassung
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.