In this paper we present a labelled proof method for computing nonmonotonic consequence relations in a conditional logic setting. The method exploits the strong connection between...
Alberto Artosi, Guido Governatori, Antonino Rotolo
When mining large databases, the data extraction problem and the interface between the database and data mining algorithm become important issues. Rather than giving a mining algo...
Abstract. In this paper we present a labelled proof method for computing nonmonotonic consequence relations in a conditional logic setting. The method is based on the usual possibl...
Alberto Artosi, Guido Governatori, Antonino Rotolo
This paper presents a new approach to improving relation extraction based on minimally supervised learning. By adding some limited closed-world knowledge for confidence estimation...
Feiyu Xu, Hans Uszkoreit, Sebastian Krause, Hong L...
We logically model uncertainty by expanding language without changing logical reasoning rules. We expand the language of set theory by adding new predicate symbols, uncertain membe...