Domain knowledge is one of crucial factors to get a great success in requirements elicitation of high quality, and only domain experts, not requirements analysts, have it. We prop...
We organized a challenge for IJCNN 2007 to assess the added value of prior domain knowledge in machine learning. Most commercial data mining programs accept data pre-formatted in ...
Isabelle Guyon, Amir Saffari, Gideon Dror, Gavin C...
An essential element in defining the semantic of Web services is the domain knowledge. Medical informatics is one of the few domains to have considerable domain knowledge exposed ...
Standard hybrid learners that use domain knowledge require stronger knowledge that is hard and expensive to acquire. However, weaker domain knowledge can benefit from prior knowle...
Preservation of privacy in micro-data release is a challenging task in data mining. The k-anonymity method has attracted much attention of researchers. Quasiidentifier is a key co...
Learning from experimentation allows a system to acquire planning domain knowledge by correcting its knowledge when an action execution fails. Experiments are designed and planned...
— Fundamental to the problem of lifelong machine learning is how to consolidate the knowledge of a learned task within a long-term memory structure (domain knowledge) without the...
In this paper we describe a system for semantic interpretation of noun compounds that relies on world and domain knowledge from a knowledge base. This architecture combines domain...
We propose a new approach for learning Bayesian classifiers from data. Although it relies on traditional Bayesian network (BN) learning algorithms, the effectiveness of our approa...
In this paper, we present research trends carried out in the Orpailleur team at loria, showing how knowledge discovery and knowledge processing may be combined. The knowledge disco...
Jean Lieber, Amedeo Napoli, Laszlo Szathmary, Yann...