PhD Project: Songlin Wang (IfGR, University of Vienna)

Duration: 2024-2027

Abstract

The comprehensive climate mobility nexus has attracted a growing interest from scholars and policymakers; however, research in this domain is fragmented and sometimes ambiguous. This is caused by its decentralized research focuses, diverse data being used for analysis, varying research methods being applied, spatial heterogeneity and disciplinary diversity etc. This fragmentation leads to heterogeneous conclusions, making it hard for people to understand the overall context of climate mobility, hindering trans-disciplinary cooperation.

This project aims to bridge the existing gap by leveraging geo-spatial knowledge graphs for the management of climate mobility information and knowledge. As structured representation of facts, knowledge graphs facilitate the integration, representation, and reasoning over diverse climate mobility data in a clear and systematic manner. This enhances reusability, transparency, interoperability, and ensures the infirmation is both human- and machine-unterstandable. The project specifically employs large language model (LLM)-assisted knowledge graphs, positioning it not only as an application of computer science technologies within the scientific domain, but also as a case study for evaluating the usability if artificial intelligence in a specialized field.