Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Logic programming and databases
Logic programming and databases
Beyond inversion of resolution
Proceedings of the seventh international conference (1990) on Machine learning
Elements of relational database theory
Handbook of theoretical computer science (vol. B)
Interactive theory revision: an inductive logic programming approach
Interactive theory revision: an inductive logic programming approach
Induction in an abstraction space: a form of constructive induction
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
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The most common methodology in symbolic learning consists in inducing, given a set of observations, a general concept definition. It is widely known that the choice of the proper description language for a learning problem can affect the efficacy and effectiveness of the learning task. Furthermore, most real-world domains are affected by various kinds of imperfections in data, such as inappropriateness of the description language which does not contain/facilitate an exact representation of the target concept. To deal with such kind of situations, Machine Learning approaches moved from a framework exploiting a single inference mechanism, such as induction, towards one integrating multiple inference strategies such as abstraction. The literature so far assumed that the information needed to the learning systems to apply additional inference strategies is provided by a domain expert. The goal of this work is the automatic inference of such information. The effectiveness of the proposed method was tested by providing the generated abstraction theories to the learning system INTHELEX as a background knowledge to exploit its abstraction capabilities. Various experiments were carried out on the real-world application domain of scientific paper documents, showing the validity of the approach.