Offre en lien avec l’Action/le Réseau : – — –/– — –
Laboratoire/Entreprise : Laboratoire Bordelais de Recherche en Informatique
Durée : 36 months
Contact : meghyn.bienvenu@labri.fr
Date limite de publication : 2021-05-16
Contexte :
Accessing the relevant information contained in real-world data to support informed decision making is difficult, time-consuming, and error-prone due to the need to integrate data across multiple heterogeneous sources. Moreover, even if this first hurdle is overcome, a perhaps even more daunting challenge arises: how to obtain reliable insights from imperfect data? It is widely acknowledged that real-world data is plagued with quality issues, such as incompleteness (missing information) and errors (false or outdated information).
The ontology-based data access (OBDA) paradigm addresses the first challenge by facilitating access to (potentially heterogeneous) data sources through the use of ontologies that specify a convenient user-friendly vocabulary for query formulation (which abstracts from the way the data is stored) and capture domain knowledge that can be exploited at query time, via automated reasoning, to obtain more complete query results.
While OBDA systems are growing in maturity, they too often fail to address the data quality issue, aside from issuing warnings when inconsistencies are discovered. It is therefore essential to equip OBDA systems with appropriate mechanisms for handling imperfect data: how to obtain meaningful answers to queries posed over imperfect data, and how best to generate a high-quality version of the data?
This position is part of the INTENDED Chair on Artificial Intelligence, whose aim is to develop intelligent, knowledge-based methods for handling imperfect data. For more information, see: https://intended.labri.fr/
Sujet :
The PhD position will focus on the development of a customized user-sensitive approach to data quality in OBDA, in which users can give direction on how errors are resolved, based upon their knowledge, preferences, and intended use of the data.
More precisely, the student will define one or more notions of a data quality policy, examine the formal properties of such policies, and develop and analyze algorithms for constructing and debugging such policies and using them to clean the data and/or produce reliable answers to queries.
Depending on the interests of the student, the thesis could also involve a more practical component (implementation and testing of the developed algorithms), but the thesis is primarily focused on foundational research.
Profil du candidat :
As ontologies are expressed using logic-based formalisms, candidates should be familiar and comfortable with first-order logic.
Prior knowledge in one or more of the following areas would be a plus: knowledge representation and reasoning (especially description logics), database theory, Semantic Web (ontologies), theoretical computer science (in particular, computational complexity).
Strong English language skills (reading, writing, & speaking) are expected, but knowledge of French is not required. The working language can be either French or English.
Formation et compétences requises :
At the start of the PhD, the candidate must hold a Master’s degree in computer science (or possibly mathematics, if accompanied by relevant computer science experience).
Adresse d’emploi :
LaBRI – Université de Bordeaux
351 cours de la Libération
F-33405 Talence cedex
Document attaché : 202105051156_phd1-intended-4.pdf