Offre en lien avec l’Action/le Réseau : RoD/– — –
Laboratoire/Entreprise : LaBRI – Laboratoire Bordelais de Recherche en Info
Durée : 3 ans
Contact : meghyn.bienvenu@labri.fr
Date limite de publication : 2020-09-01
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that real-world data is plagued with quality issues, such as incompleteness (missing information) and errors (false or outdated information).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. To widen the applicability of the OBDA approach, it is essential to equip OBDA systems with appropriate mechanisms for handling imperfect data.
The PhD position is part of the INTENDED Chair on Artificial Intelligence, whose aim is to develop intelligent, knowledge-based methods for handling imperfect data.
Sujet :
The PhD position will focus on the development of a customized user-sensitive approach to data quality in the setting of ontology-based data access (OBDA). The aim is to allow users to give direction on how to address data quality issues.
Inconsistency management policies have been introduced for relational databases to give users control over how errors are resolved, based upon their knowledge, preferences, and intended use of the data. It is appealing to consider such policies for the OBDA setting, but existing definitions and results do not readily transfer.
The first step will be to define a suitable notion of policy and examine its basic properties. Afterwards, the PhD student will develop novel reasoning services and associated reasoning algorithms for managing such policies: How to determine if a policy is well defined, and if it is guaranteed to yield a unique result? How can we aid users in constructing such policies, e.g. by suggesting refinements?
Profil du candidat :
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).
Formation et compétences requises :
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.
Adresse d’emploi :
The position will be based in Bordeaux in the LaBRI research lab, with regular funded stays in Paris to visit the co-supervisor (ENS Ulm).
Document attaché : 202006101515_phd1-intended.pdf