In Data Driven Solutions using CBR or Data Mining approaches the optimal results can be achieved if one can consider and use, besides available data and texts, all other available information sources like general and background knowledge. Formalization and integration of such kind of knowledge in the knowledge extracted from data and texts is not, however, a simple task. For this reason, a lot of approaches, among them Bayesian Networks and Inductive Logic Programming, have been suggested in the literature to solve this problem. In the talk, this topic is discussed pragmatically by reviewing the personal experiences of the speaker in the last 20 years using concrete examples from the automotive industry. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Nakhaeizadeh, G. (2006). Is consideration of background knowledge in Data Driven Solutions possible at all? In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4106 LNAI, p. 30). Springer Verlag. https://doi.org/10.1007/11805816_3
Mendeley helps you to discover research relevant for your work.