Artículos

 

Optimizing Selection of Assessment Solutions for Completing Information Extraction Results

 

Optimización de selección de soluciones de evaluación para completar los resultados de recuperación de información

 

Christina Feilmayr

 

Johannes Kepler University Linz, Institute of Application Oriented Knowledge Processing, Altenberger Straße 69, 4040 Linz, Austria cfeilmayr@faw.jku.at

 

Article received on 14/12/2012
Accepted on 03/02/2013.

 

Abstract

Incomplete information produces serious consequences in information extraction: it increases costs and leads to problems in downstream processing. This work focuses on improving the completeness of extraction results by applying judiciously selected assessment methods to information extraction based on the principle of complementarity. Our recommendation model simplifies the selection of assessment methods which can overcome a specific incompleteness problem. This paper also focuses on the characterization of information extraction and assessment methods as well as on a rule-based approach that allows estimation of general processability, profitability in the complementarity approach, and the performance of an assessment method under evaluation.

Keywords: Information extraction, information quality, method selection, data and text mining.

 

Resumen

La información incompleta causa graves consecuencias en la extracción de la misma: aumenta los costos y propicia problemas para el procesamiento en cadena. El objetivo de este trabajo es presentar la mejora en los resultados de extracción con el fin de completarlos con métodos de evaluación juiciosamente selectos basados en el principio de complementariedad. El modelo propuesto simplifica la selección de los métodos de evaluación, los cuales pueden resolver un problema específico de información incompleta. Este artículo se enfoca también en la caracterización de la extracción de información y los métodos de evaluación con un enfoque basado en reglas que permita validar la capacidad de procesamiento general, la rentabilidad en el enfoque de complementariedad y el rendimiento de los métodos de evaluación.

Palabras clave: Extracción de información, calidad de información, selección del método, minería de datos y textos.

 

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Acknowledgements

This work is supported by an Austrian research grant (FIT-IT Semantic Systems Dissertation Fellowship Project) from BMVIT (project 829601).

 

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