Fully funded PhD position in AI & Databases
Place: Télécom SudParis – Polytechnic Institute of Paris (Palaiseau-France)
Application Deadline: November 30, 2023
Duration: Three years
Funding: French National Research Foundation
Keywords: Databases, Artificial Intelligence, Knowledge Representation and Reasoning, Provenance
The ANR project EXPIDA1 (EXplainable and parsimonious Preference models to get the most out of Inconsistent DAtabases) aims to develop principled and rigorous explainable techniques for dealing with imperfect data. More precisely, EXPIDA aims to design tractable methods for dealing with conflicts in databases by efficiently exploring novel inconsistency-tolerant semantics and quantifying contradictions  to answer queries and to draw (high level) explanatory information. While the set of repairs (maximal consistent datasets) is often large for real databases, we aim to explore preference mechanisms (e.g., ) in order to retrieve meaningful answers and explanations to identify the reasons of query answers, and to assist end-users to “realize” query outputs. The EXPIDA project aims in addition to be useful for applications intensively relying on multiple heterogeneous data sources. Many such applications are nowadays developed in various domains such as transportation control, health management, social network analysis, data journalism, etc. This research project will advance the state-of-the-art in two major ways: innovations in inconsistency management, preferences and explanation for databases, and developing practical Artificial Intelligence tools for managing inconsistent databases with validations on real data. This PhD thesis will focus on the aspect of explainability in two ways, as presented in what follows.
First, we will consider explanations for query results over inconsistent databases with different conflict-tolerant semantics (e.g., consistent, brave, intersection repair, intersection closed repair, non-objection, nonconsensus based semantics, etc.). To this end, we will adapt the notion of lineage (or provenance)  in the context of uncertain/inconsistent data and devise mathematical formalisations that will provide the necessary properties for characterising and measuring the ‘quality’ of the explanations. Through the study of causality  and argumentation [4, 9, 8, 3] in our setting, we further aim at improving the acceptability and usefulness of the provided explanations by the end-user. Second, we will investigate the complementary problem of explaining missing query results, widely known as Why-Not explanations, which has not been yet addressed in the context of inconsistent databases. In the setting of consistent databases, Why-Not explanations ‘explain’ why certain results are not generated by a query (or a workflow) by means of instance-based (i.e., source tuples), query-based (i.e., query operators) or refinement-based explanations (i.e., corrected query). Close to our problem,  has proposed Why-Not provenance polynomials, which may account for probabilistic tuples. It would be interesting to check how such formalisations can be revisited to fit the inconsistent database’s different conflict-tolerant semantics.
The PhD thesis will be supervised by Badran Raddaoui (Telecom SudParis, SAMOVAR), in collaboration with Aikaterini Tzompanaki (CY Cergy University, ETIS lab) and Yue Ma (Paris Saclay University, LISN).
The application must include:
- Curriculum vitae.
- Transcripts and diplomas for bachelor’s and master’s degrees.
- Cover letter with personal motivation and relevance w.r.t. the requirements of the position.
- Recommendation letters or contact information of at least two referees.
Applications should be submitted via email to Badran RADDAOUI (firstname.lastname@example.org) and Aikaterini TZOMPANAKI (email@example.com), with the subject “Application for EXPIDA PhD 2”.
 Nicole Bidoit, Melanie Herschel, and Katerina Tzompanaki. Immutably answering why-not questions for equivalent conjunctive queries. Ingénierie des Systèmes d Inf., 20(5):27–52, 2015.
 James Cheney, Laura Chiticariu, and Wang Chiew Tan. Provenance in databases: Why, how, and where. Found. Trends Databases, 1(4):379–474, 2009.
 Kristijonas Cyras, Antonio Rago, Emanuele Albini, Pietro Baroni, and Francesca Toni. Argumentative XAI: A survey. In IJCAI, pages 4392–4399, 2021.
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