A Formal Model of Hierarchical Concept-Learning

Bootstrapping in a language of thought: a formal model of numerical concept learning.

In acquiring number words, children exhibit a qualitative leap in which they transition from understanding a few number words, to possessing a rich system of interrelated numerical concepts. We present a computational framework for understanding this inductive leap as the consequence of statistical inference over a sufficiently powerful representational system. We provide an implemented model t...

Learning Thresholds in Formal Concept Analysis

This paper views Formal Concept Analysis (FCA) from an educational perspective. Novice users of FCA who are not mathematicians might find diagrams of concept lattices counter-intuitive and challenging to read. According to educational theory, learning thresholds are concepts that are difficult to learn and easy to be misunderstood. Experts of a domain are often not aware of such learning thresh...

Machine Learning and Formal Concept Analysis

Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis

We present a novel approach to the automatic acquisition of taxonomies or concept hierarchies from a text corpus. The approach is based on Formal Concept Analysis (FCA), a method mainly used for the analysis of data, i.e. for investigating and processing explicitly given information. We follow Harris’ distributional hypothesis and model the context of a certain term as a vector representing syn...

A Formal Characterization of Concept Learning in Description Logics

Among the inferences studied in Description Logics (DLs), induction has been paid increasing attention over the last decade. Indeed, useful non-standard reasoning tasks can be based on the inductive inference. Among them, Concept Learning is about the automated induction of a description for a given concept starting from classified instances of the concept. In this paper we present a formal cha...