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Thèse soutenue le 15.12.2023

Sujet : "Semantic Representation and Computation of Mathematical Formulas for Plant Phenomics Data Exploitation"

Abstract :

Knowledge Graphs (KGs) have become central to managing diverse datasets across fields like agriculture, biomedical, environmental, and social sciences. Semantic Web (SW) technologies excel at representing taxonomic knowledge in these KGs. However, scenarios involving numerical relationships with algebraic operations or unit conversions are less addressed, yet hold potential to enhance KG data.

For instance, consider the Body Mass Index (BMI) computation, which relies on weight and height properties. This can enrich a KG with derived data. Similarly, deriving the Vapour Pressure Deficit (VPD) from air temperature and relative humidity is valuable. While experts understand these formulas, they are often implemented in ad-hoc programming languages, limiting reuse and reproducibility. 

This thesis explores Semantic Web approaches to represent and compute these numerical relationships. We identify current limitations in representation, computational methods, and expressivity. To tackle these challenges, we propose a Semantic Web-based framework with the following goals: (i) Represent mathematical formulas in line with Linked Open Data (LOD) and FAIR principles, to enhance adoption and reproducibility. (ii) Enable on-demand execution of numerical relationships, recognising that materialising results is infeasible for large and diverse KGs. (iii) Express mathematical formulas using KG data in the form of quantity values, leveraging semantic resources and metadata like unit ontologies. (iv) Facilitate aggregations within mathematical formulas, acknowledging that much of this numerical data operates on multiple scales. We evaluate this framework on KGs from the agriculture and plant phenomics domain, the focus of this thesis, as well as on more established Semantic Web KGs like DBpedia.

Directeur : Pierre Martre
Encadrants : Danai Symeonidou, Llorenç Cabrera-Boquet