Des Données aux Connaissances : Modèles et Algorithmes

My research work is at the intersection of several fields, including Symbolic Artificial Intelligence (AI), data mining, graphs, and information systems. They revolve around the following keywords: constraint reasoning, knowledge representation, reasoning modeling, constraint-based pattern mining, clustering, community detection, data compression, and web service composition.The first part of my HDR provides an overview of my algorithmic contributions to the propositional satisfiability problem, including clause learning, parallel SAT solvers of the portfolio type, model and prime implicant enumeration, and the transformation of (conditional) cardinality constraints into conjunctive normal form.The second part addresses issues in data mining and clustering. My contributions focus on declarative approaches for various data mining tasks: mining frequent itemsets and their various condensed forms, extracting association rules and its numerous variants, sequential pattern mining, Top-k pattern enumeration modulo a preference relation, pattern mining under uncertainty, parallel methods by decomposition, and symbolic clustering of propositional formulas. To highlight cross-fertilizations between symbolic AI and data mining, I demonstrate how the concept of symmetry, widely explored in SAT/CP, is extended to the mining of set patterns. Additionally, I show how data mining can be used to compress boolean formulas and CSP constraints expressed in extension.The third part deals with my contributions to community detection and the compression of large graphs, using pseudo-Boolean constraints and propositional logic.The fourth part focuses on reasoning in the presence of inconsistencies and argumentation theory. I present different methods for conflict quantification in knowledge bases and also for reasoning in the presence of inconsistencies and uncertainty in ontologies.The last part is dedicated to my contributions to the composition of web services, proposing various logic-based models for this problem.This work concludes by discussing my current and future research projects

De la satisfiabilité propositionnelle aux formules booléennes quantifiées

LENS-BU Sciences (624982102) / SudocSudocFranceF

Towards a principle-based framework for repair selection in inconsistent knowledge bases

International audienc

Discovering Overlapping Communities based on Cohesive Subgraph Models over Graph Data

International audienc

Symmetry breaking in quantified boolean formulae

Many reasoning task and combinatorial problems exhibit symmetries. Exploiting such symmetries has been proved to be very important in reducing search efforts. Breaking symmetries using additional constraints is currently one of the most used approaches. Extending such symmetry breaking techniques to quantified boolean formulae (QBF) is a very challenging task. In this paper, an approach to break symmetries in quantified boolean formulae is proposed. It makes an original use of universally quantified auxiliary variables to generate new symmetry breaking predicates and a new ordering of the QBF prefix is then computed leading to a new equivalent QBF formula with respect to validity. Experimental evaluation of the state-of-the-art QBF solver semprop shows significant improvements (up to several orders of magnitude) on many QBFs instances.

Towards a Principle-based Framework for Repair Selection in Inconsistent Knowledge Bases

International audienc

Towards a principle-based framework for repair selection in inconsistent knowledge bases

International audienc