DESCRIPTION :
This PhD thesis takes place within the MetaboLinkAI ANR-SNF project, which aspires to revolutionize the analysis and interpretation of metabolomics data through a multidisciplinary approach that combines a comprehensive knowledge graph hub (MetaKH) with cutting-edge artificial intelligence (AI) and machine learning (ML) techniques. The project's main goals are to enhance the querying and ease of use of metabolomics data, improve research efficiency, and stimulate creativity in the field. These objectives are set to surpass current standards by creating an encyclopedic and expandable knowledge base, integrating advanced AI to handle the uncertainties of experimental data, and enabling a broader range of hypothesis testing and evaluation.
Within this project, we will focus on developing innovative methodologies and tools, such as graph mining methods, to enhance data interaction, analysis capabilities, and representation of uncertainty.
One distinctive peculiarity of metabolomics data (and thus MetaKH) is incompleteness, variable confidence and inherent uncertainty. Here, we adopt AI to enhance the completeness and reliability of the KG and to correctly account for uncertainty.
Mission confiée
Computational approaches for graph mining and completion
Because of the uncertain nature of metabolomics data and associated knowledge, MetaKH will be largely incomplete and partly incorrect. Therefore, it will be crucial to develop a comprehensive computational framework to enhance the quality, completeness and validity to eventually increase the quality of any processing using MetaKH. We propose to adapt heuristic methods and algorithms to discover/induce topological motifs, axioms (OWL), rules (SWRL or SPARQL) or shapes (SHACL) from knowledge graphs (TBox construction/refinement). These will account for the possible uncertainty of knowledge represented in the ABox (as defined in WP3.2). Expert-in-the-loop techniques will also be considered. We will design algorithms and data structures to allow KG queries at different levels of data granularity. The methods will exploit heuristics derived from expert knowledge in combination with semi-succinct and, where needed, approximated data structures. In parallel, we will
work on methods for knowledge graph completion, correction and enrichment, to enhance quality and content (ABox refinement). The developed methods will combine deductive reasoning (including analogic), SHACL validation, and link prediction and retraction based on KG embeddings. They will take into account the uncertainty of knowledge as defined in WP3.2. Evaluation will be done by measuring the improvement of KG completeness and validity, and the effectiveness of reasoning by corrupting the KG by adding/removing/perturbing some edges, applying completion/inference/querying, and assessing the impact in comparison with the original KG.
Dealing with (lack of) confidence in KGs
The objective is to develop and integrate a sophisticated framework into semantic web standards for formal representation and reasoning of uncertainty (both ontic and epistemic) in MetaKH, improving data confidence and decision-making processes. Initially, we will review literature to identify adequate models to represent ontic uncertainty (certainly probability theory) and epistemic uncertainty (e.g. possibility theory, Dempster Shafer theory) adequate to represent mass spectrometry observations and metabolomic knowledge. Based on such models, we will propose extensions to Semantic Web standards to express uncertainty, provenance, and temporality metadata, facilitating richer data interpretation and trustworthiness. We will develop algorithms to integrate uncertainty in querying, deduction and embedding in KGs. We will establish criteria for using KGs based on uncertainty and provenance metadata, as well as other types of metadata, enabling users and agents to make
informed decisions regarding trust and data application. Algorithms developed in WP3.1 will be extended to integrate uncertainty. Finally, we plan to implement mechanisms for evaluating KG completeness, validity, and reasoning under uncertainty, incorporating expert feedback and adapting methodologies based on provenance and other metaknowledge types.
Principales activités
This thesis will start with a state of the art of the different domains involved, in particular graph-based knowledge representation, KG mining, uncertainty representation and management in KG.
The PhD student is expected to first address computational approaches for MetaKH mining and completion, and then extend these approaches considering the inherent uncertainty of some knowledge in MetaKH, and of the mining approaches and their results.
Expected deliverables are:
[D1] Heuristic methods, data structures and algorithms for KG querying and mining
[D2] Methods and algorithms for KG completion
[D3] Proposal of an extension of SW standards for uncertainty annotation
[D4] Implementation of uncertainty annotation in MetaKH
References
1. Ahmed El Amine Djebri. Uncertainty Management for Linked Data Reliability on the Semantic Web. PhD thesis, Université Côte d'Azur, 2022.
2. Ahmed El Amine Djebri, Andrea G. B. Tettamanzi, and Fabien Gandon. Publishing uncertainty on the semantic web: Blurring the LOD bubbles. In Graph-Based Representation and Reasoning - 24th International Conference on Conceptual Structures, ICCS 2019, Marburg, Germany, July 1-4, 2019, Proceedings, volume 11530 of Lecture Notes in Computer Science, pages 42-56. Springer, 2019.
3. Antonia Ettorre, Anna Bobasheva, Catherine Faron, and Franck Michel. A systematic approach to identify the information captured by knowledge graph embeddings. In WI-IAT '21 : IEEE/WIC/ACM International Conference on Web Intelligence, Melbourne VIC Australia, December 14 - 17, 2021, pages 617-622. ACM, 2021.
4. Rémi Felin. Evolutionary knowledge discovery from RDF data graphs. PhD thesis, Université Côte d'Azur, 2024.
5. Rémi Felin, Catherine Faron, and Andrea G. B. Tettamanzi. A framework to include and exploit probabilistic information in SHACL validation reports. In The Semantic Web - 20th International Conference, ESWC 2023, Hersonissos, Crete, Greece, May 28 - June 1, 2023, Proceedings, volume 13870 of Lecture Notes in Computer Science, pages 91-104. Springer, 2023.
6. Rémi Felin, Pierre Monnin, Catherine Faron, and Andrea G. B. Tettamanzi. An Algorithm Based on Grammatical Evolution for Discovering SHACL Constraints. In EuroGP 2024 - 27th European Conference on Genetic Programming, Genetic Programming - 27th European Conference, EuroGP 2024, Aberystwyth, United Kingdom, April 2024.
7. Thu Huong Nguyen. Mining the semantic Web for OWL axioms. PhD thesis, University of Côte d'Azur, 2021.
8. Andrea G. B. Tettamanzi, Catherine Faron-Zucker, and Fabien Gandon. Possibilistic testing of OWL axioms against RDF data. Int. J. Approx. Reason., 91 :114-130, 2017., There you can provide a "broad outline" of the collaborator you are looking for what you consider to be necessary and sufficient, and which may combine :
* tastes and appetencies,
* area of excellence,
* personality or character traits,
* cross-disciplinary knowledge and expertise...
Code d'emploi : Thésard (h/f)
Niveau de formation : Bac+5
Temps partiel / Temps plein : Plein temps
Type de contrat : Contrat à durée indéterminée (CDI)
Compétences : Intelligence Artificielle, Analyse des Données, Logiciel de Groupe, Structure de Données, Données Expérimentales, Genetic Algorithm, Test d'Hypothèse Statistique, Données Liées, Machine Learning, Web Sémantique, SPARQL, Web Intelligence, Knowledge Representation, Technologies Informatiques, Anglais, Adaptabilité, Capacité d'Analyse, Prise de Décision, Sens de la Communication, Créativité, Capacité de Déduction, Capacité de Persuasion, Optimisme, Enthousiasme, Fiabilité, Innovation, Algorithmes, Travaux de Construction, Découverte de Connaissances, Organisation d'Événements, Orthographe et Grammaire, Bases de Connaissances, Spectrométrie de Masse, Théorie des Probabilités, Métabolomique, Approche Pluridisciplinaire, Recherche Scientifique, Littérature, Publication / Edition
Courriel :
Catherine.Faron@inria.fr
Téléphone :
0139635511
Type d'annonceur : Employeur direct