Acquisition of a Knowledge Dictionary from Training Examples Including Multiple Values

Abstract

A text mining system uses two kinds of background knowledge: a concept relation dictionary and a key concept dictionary. The concept relation dictionary consists of a set of rules. We can automatically acquire it by using an inductive learning algorithm. The algorithm uses training examples including concepts that are generated by using both lexical analysis and the key concept dictionary. The algorithm cannot deal with a training example with more than one concept in the same attribute. Such a training example is apt to generate from a report, when the concept dictionary is not well defined. It is necessary to extend an inductive learning algorithm, because the dictionary is usually not completed. This paper proposes an inductive learning method that deals with the report. Also, the paper shows the efficiency of the method through some numerical experiments using business reports about retailing.