Context-Aware Embedded Language Transformers for Evaluating Climate Change-Based Sustainable Development Goals

This research work addresses the pressing issue of climate change and the urgent need for a comprehensive and reliable dataset that can assist stakeholders, policymakers and researchers in making informed and data-driven decisions. We propose the process for leveraging publicly available raw data such as news articles and social media posts and help to create meaningful ontologies. The study demonstrates the use of advanced Natural Language Processing and Deep Learning techniques to transform raw data into potential insights. Additionally, we present a methodology for utilizing these generated ontologies to anticipate the impacts of decisions on climate change. The primary objective of this research is to present a Context-Aware Embedded Language Transformers model that can be easily integrated into various pipelines by generating meaningful ontologies to support more informed decision-making. These ontologies will serve as knowledge graphs and contribute to a large dataset for future research in the field and address the current lack of comprehensive resources.