One of the field in which models of abstraction have been proposed is Artificial Intelligence
(AI). This chapter has two parts: one presents an overview of the formal models, either …

A model of learning from positive and negative examples is naturally described in terms of
Formal Concept Analysis (FCA). In these terms, result of learning consists of two sets of …

A crucial issue for Machine Learning and Data Mining is Feature Selection, selecting the
relevant features in order to focus the learning search. A relaxed setting for Feature …

Over the last decade Inductive Logic Programming systems have been dominated by use of
top-down refinement search techniques. In this paper we re-examine the use of bottom-up …

Abstract Relational learning and Inductive Logic Programming (ILP) commonly use as
covering test the θ-subsumption test defined by Plotkin. Based on a reformulation of θ …

In this paper we present a novel algorithm for discovering tree patterns in a tree database.
This algorithm uses a relaxed tree inclusion definition, making the problem more complex …

Given a finite set C:={C_1,...,C_n\} of description logic concepts, we are interested in
computing the subsumption hierarchy of all least common subsumers of subsets of C as well …

Inductive logic programming (ILP) is built on a foundation laid by research in machine
learning and computational logic. Armed with this strong foundation, ILP has been applied to …

The paper introduces LOGAN-H—a system for learning first-order function-free Horn
expressions from interpretations. The system is based on an algorithm that learns by asking …

The growth of machine-generated relational databases, both in the sciences and in industry,
is rapidly outpacing our ability to extract useful information from them by manual means. This …