Abstract
This paper proposes a new approach for mining novel patterns from textual databases which considers both the mining process itself, the evaluation of this knowledge, and the human assessment. This is achieved by integrating Information Extraction technology and Genetic Algorithms to produce high-level explanatory novel hypotheses. Experimental results using the model are discussed and the assessment by human experts are highlighted.
This research is sponsored by the National Council for Scientific and Technological Research (FONDECYT, Chile) under grant number 1040469 “Un Modelo Evolucionario de Descubrimiento de Conocimiento Explicativo desde Textos con Base Semántica con Implicaciones para el Aná lisis de Inteligencia.”
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© 2005 Springer-Verlag Berlin Heidelberg
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Atkinson-Abutridy, J.A. (2005). A Domain-Independent Approach to Discourse-Level Knowledge Discovery from Texts. In: Ali, M., Esposito, F. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2005. Lecture Notes in Computer Science(), vol 3533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11504894_65
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DOI: https://doi.org/10.1007/11504894_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26551-1
Online ISBN: 978-3-540-31893-4
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