Knowledge and Information Systems

, Volume 11, Issue 2, pp 217–242

Inference of abduction theories for handling incompleteness in first-order learning

  • F. Esposito
  • S. Ferilli
  • T. M. A. Basile
  • N. Di Mauro
Regular Paper

DOI: 10.1007/s10115-006-0019-5

Cite this article as:
Esposito, F., Ferilli, S., Basile, T.M.A. et al. Knowl Inf Syst (2007) 11: 217. doi:10.1007/s10115-006-0019-5

Abstract

In real-life domains, learning systems often have to deal with various kinds of imperfections in data such as noise, incompleteness and inexactness. This problem seriously affects the knowledge discovery process, specifically in the case of traditional Machine Learning approaches that exploit simple or constrained knowledge representations and are based on single inference mechanisms. Indeed, this limits their capability of discovering fundamental knowledge in those situations. In order to broaden the investigation and the applicability of machine learning schemes in such particular situations, it is necessary to move on to more expressive representations which require more complex inference mechanisms. However, the applicability of such new and complex inference mechanisms, such as abductive reasoning, strongly relies on a deep background knowledge about the specific application domain. This work aims at automatically discovering the meta-knowledge needed to abduction inference strategy to complete the incoming information in order to handle cases of missing knowledge.

Keywords

Incomplete knowledge Inductive Logic Programming Abduction 

Copyright information

© Springer-Verlag London Limited 2006

Authors and Affiliations

  • F. Esposito
    • 1
  • S. Ferilli
    • 1
  • T. M. A. Basile
    • 1
  • N. Di Mauro
    • 1
  1. 1.Department of Computer ScienceUniversity of BariBariItaly