Offre en lien avec l’Action/le Réseau : RoD/– — –
Laboratoire/Entreprise : LaBRI – Laboratoire Bordelais de Recherche en Info
Durée : 24
Contact : meghyn.bienvenu@labri.fr
Date limite de publication : 2020-09-30
Contexte :
Accessing the relevant information in real-world data g 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that real-world data is plagued with quality issues, such as incompleteness (missing information) and errors (false or outdated information).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 OBDA, it is 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?
The two postdoc positions are part of the INTENDED Chair on Artificial Intelligence, whose aim is to develop intelligent, knowledge-based methods for handling imperfect data. The chair project will involve both foundational work as well as a more applied component with a hospital use case. The chair begins in September 2020 and has a duration of four years. It is funded by the French National Research Agency (ANR) and the University of Bordeaux.
Sujet :
Topic 1: Holistic Approach to Data Quality in Ontology-Based Data Access
As part of the INTENDED project, we plan to develop a holistic approach to data quality in ontology-based data access (OBDA) that incorporates existing data cleaning techniques (e.g. entity linking and statistical outliers), in order to tackle a wider class of data quality issues and to improve overall results by exploiting synergies among different methods. For instance, merging distinct values using entity linking tools may both resolve violations of functionality constraints from the ontology, or bring to light additional conflicts with the ontology that would be missed otherwise, and conversely, the entity linking process could be improved by taking into account the logical constraints imposed by the ontology.
The postdoctoral researcher will participate in devising a suitable formal framework for integrating different data quality methods into the OBDA approach, and subsequently developing algorithms for computing repairs and query answering in this framework.
Topic 2: Practical Inconsistency Handling in Expressive Settings
Currently, state-of-the-art inconsistency-tolerant algorithms rely, directly or indirectly, on the first-order rewritability property, which restricts their applicability to simple ontology languages (e.g. DL-Lite, OWL 2 QL). Tackling other ontology languages (such as OWL 2 EL, a popular choice for biomedical ontologies) presents significant technical challenges, as the facts involved in contradictions cannot be efficiently identified.
The postdoctoral researcher will be involved in developing pragmatic approaches to handling inconsistencies in more expressive OBDA settings. This could include conceiving new inconsistency-tolerant query answering algorithms for more expressive ontology languages, devising optimizations, identifying interesting tractable subcases, and extending the algorithms to tackle other important features (e.g. mappings, temporal dimension) that have been little explored thus far.
Profil du candidat :
Applicants should hold (or be close to completing) a PhD degree in computer science and should have a strong research record in knowledge representation and reasoning or databases (ideally, demonstrated by at least one publication in a top-tier venue).
The precise research topic may be adjusted to suit the background and interests of the postdoctoral researcher, so brilliant candidates with an interest for the overall project topic should not hesitate to apply.
Formation et compétences requises :
Familiarity with logic and/or ontology languages is desirable.
Experience and interest in one or more of the following areas would be relevant and welcome: description logics, Semantic Web, automated reasoning, inconsistency handling, data integration, data quality, data cleaning, theoretical computer science (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.
Adresse d’emploi :
Laboratoire Bordelais de Recherche en Informatique
351, cours de la Libération F-33405 Talence cedex
Document attaché : 202006231505_postdocs-intended-labri.pdf