PhD Thesis Defence: 12 novembre 2019, 13h30
- Start Date: October 2016
- Defence : November 2019
- University: University of Montpellier
- PhD School: I2S – Information, Structures, Systèmes
- Field(s): Computer Science, Data Sciences
- Doctoral Thesis Advisors: Mathieu Roche, Cirad Tetis et Maguelonne Teisseire, Irstea Tetis
- Funding: Cirad – Irstea
- #DigitAg: Axes 4 & 5 – Challenges : cross-cutting subject, 8
Keywords: Data sciences, Big Data, biodiversity data
Abstract:
In scientific literature, few approaches exists for matching heterogeneous data in a generic way. As part of this thesis, propositions will be established in multidisciplinary ways of matching under 3 axes : thematic matching, spatial matching and temporal matching. The identification of pertinent descriptors will be established under these 3 axes using symbolic, statistic and semantic methods and the use of NLP methods for exploring textual data.
Contact: jacques.fize [AT] cirad.fr
Social Networks: site – GitHub – ResearchGate – LinkedIn
Communications / Publications
Fize J., Roche M., Teisseire M. (2018) Gemedoc: A Text Similarity Annotation Platform. In: Silberztein M., Atigui F., Kornyshova E., Métais E., Meziane F. (eds) Natural Language Processing and Information Systems. NLDB 2018. Lecture Notes in Computer Science, vol 10859. Springer, Cham – https://doi.org/10.1007/978-3-319-91947-8_35
Jacques Fize, Mathieu Roche, Maguelonne Teisseire (2018). Matching heterogeneous textual data using spatial features 13th International Workshop on Spatial and Spatiotemporal Data Mining (SSTDM-18) (to appear)
