Classifying Candidate Axioms via Dimensionality Reduction Techniques

We assess the role of similarity measures and learning methods in classifying candidate axioms for automated schema induction through kernel-based learning algorithms. The evaluation is based on (i) three different similarity measures between axioms, and (ii) two alternative dimensionality reduction techniques to check the extent to which the considered similarities allow to separate true axioms from false axioms. The result of the dimensionality reduction process is subsequently fed to several learning algorithms, comparing the accuracy of all combinations of similarity, dimensionality reduction technique, and classification method. As a result, it is observed that it is not necessary to use sophisticated semantics-based similarity measures to obtain accurate predictions, and furthermore that classification performance only marginally depends on the choice of the learning method. Our results open the way to implementing efficient surrogate models for axiom scoring to speed up ontology learning and schema induction methods

A Multi-Objective Evolutionary Approach to Class Disjointness Axiom Discovery

International audienceThe huge wealth of linked data available on the Web (also known as the Web of data), organized according to the standards of the Semantic Web, can be exploited to automatically discover new knowledge, expressed in the form of axioms, one of the essential components of ontologies. In order to overcome the limitations of existing methods for axiom discovery, we propose a two-objective grammar-based genetic programming approach that casts axiom discovery as a genetic programming problem involving the two independent criteria of axiom credibility and generality. We demonstrate the power of the proposed approach by applying it to the task of discovering class disjointness axioms involving complex class expression, a type of axioms that plays an important role in improving the quality of ontologies. We carry out experiments to determine the most appropriate parameter settings and we perform an empirical comparison of the proposed method with state-of-the-art methods proposed in the literature

Using Grammar-Based Genetic Programming for Mining Disjointness Axioms Involving Complex Class Expressions

International audienceIn the context of the Semantic Web, learning implicit knowledge in terms of axioms from Linked Open Data has been the object of much current research. In this paper, we propose a method based on grammar-based genetic programming to automatically discover disjoint-ness axioms between concepts from the Web of Data. A training-testing model is also implemented to overcome the lack of benchmarks and comparable research. The acquisition of axioms is performed on a small sample of DBpedia with the help of a Grammatical Evolution algorithm. The accuracy evaluation of mined axioms is carried out on the whole DBpe-dia. Experimental results show that the proposed method gives high accuracy in mining class disjointness axioms involving complex expressions

Développement d'une ontologie pour l'analyse d'observables de l'apprenant dans le contexte d'une tâche avec des robots modulaires

The aim of this document is to present the design of an ontology allowing to carry out a modeling of the learner, the task and the observables during a learning activity, in order to develop a model applicable to the observed learning analytics which can be exploited to analyze them with computational approaches.The challenge here is to work from a relatively small batch of data (a few dozen to compare with the thousands of data used with classic statistical methods), highly structured, therefore to introduce a maximum of a priori information upstream to the analysis in order the results to be meaningful.The learner is modeled on the basis of knowledge from the educational science and cognitive neurosciences, including machine learning formalisms, in the very precise framework of a task, named CreaCube, related to initiation to computational thinking presented as an open-ended problem, which involves solving a problem and appealing to creativity.This document presents these elements and discusses the exploration and exploitation issues, the different goals (for example of performance, speed or mastery of the task), before relating this to the different types of memory and discussing the basics of problem solving, including engaging in a learning activity.It then describes the very precise construction of an ontology which formalizes this process of task resolution and knowledge construction, taking into account the stimuli received, the discovery of affordances, the setting of hypotheses, clearly distinguished from the notion of belief, without forgetting contextual knowledge.The production is shared as a free and open resource, and both the implications and the perspectives of this pioneering work of formalizing such a human learning task are discussed in conclusion.This research report and ontology corresponds to the short Post Doc research work of Lisa Roux, who is also the main author of the document, supervised by Margarida Romero and Frédéric Alexandre and was carried out within the framework of the Aex AIDE project supported by the Otesia Observatory of Technological, Economic and Societal impacts of Artificial Intelligence and Digital Technology.Le but de ce document est de présenter la conception d'une ontologie permettant de réaliser une modélisation de la personne apprenante, de la tâche et des observables au cours de l'activité, ceci afin de développer un modèle applicable aux traces d'apprentissage qui puisse être exploité pour les analyser avec des approches computationnelles. L'enjeu est ici de travailler à partir d'un relativement petit lot de données (quelques dizaines à comparer aux milliers de données utilisées avec les méthodes statistiques classiques), fortement structurées, donc d'introduire un maximum d'informations a priori en amont de l'analyse pour permettre que les résultats soient significatifs.L'apprenant·e est modélisé·e à partir de connaissances issues des sciences de l'éducation et des neurosciences cognitives, y compris les formalismes d'apprentissage machine, dans le cadre très précis d'une tâche -dite « CreaCube »- d'initiation à la pensée informatique, présentée sous forme d'un problème ouvert, qui implique la résolution d'un problème et de faire appel à la créativité.Ce document présente ces éléments et discute les problématiques d'exploration et exploitation, les différents buts (par exemple de performance, de célérité ou de maîtrise de la tâche), avant de relier cela aux différents types de mémoire et de discuter les bases de la résolution de problèmes, et l'engagement dans une activité d'apprentissage.Il décrit ensuite la construction très précise d'une ontologie qui formalise ce processus de résolution de tâche et de construction de connaissances, prenant en compte les stimuli reçus, la découverte d'affordances, la pose d'hypothèses, bien distinguées de la notion de croyance, sans oublier les connaissances contextuelles.La production est mise en partage sous forme de ressource libre et ouverte, et on discute en conclusion à la fois les implications et les perspectives de ce travail pionnier de formalisation d'une telle tâche d'apprentissage humain.Ce rapport de recherche et l'ontologie correspond au travail de recherche de Lisa Roux, qui est aussi la principale autrice du document, encadrée par Margarida Romero et Frédéric Alexandre et a été réalisé dans le cadre du projet Aex AIDE soutenu par Otesia, l'Observatoire des impacts Technologiques, Économiques et Sociétaux de l'Intelligence Artificielle et du numérique

Possibilistic testing of OWL axioms against RDF data

International audienceWe develop the theory of a possibilistic framework for OWL 2 axiom testing against RDF datasets, as an alternative to statistics-based heuristics. The intuition behind it is to evaluate the credibility of OWL 2 axioms based on the evidence available in the form of a set of facts contained in a chosen RDF dataset. To achieve it, we first define the notions of development, content, support , confirmation and counterexample of an axiom. Then we use these notions to define the possibility and necessity of an axiom and its acceptance/rejection index combining both of them. Finally, we report a practical application of the proposed framework to test SubClassOf axioms against the DBpedia RDF dataset