The PANORAMA knowledge graph based prediction of outdoor air quality impact on health

Anonymous

Keywords: Air quality, health outcomes, knowledge graph, link prediction

TL;DR: Designing and instanciating a knowledge graph of air quality and health outcomes data to enable performing prediction about their association

Abstract:

Current air quality surveillance ecosystems cannot monitor air pollution levels across time and space continuously, and lack the ability to integrate historical data from heterogeneous sources into artificial intelligence algorithms capable of providing more insights on the impact of severe air pollution. Integrating in-situ sensor and satellite data has attracted recent attention, but the characteristics of graph-based learning with heterogeneous air quality data have not been explored in depth.

In this paper, we present the PANORAMA knowledge graph based prediction approach of the links between pollutants exposure and health outcomes. PANORAMA aims at integrating heterogeneous air quality and health data in order to better predict and explain their relationships. An Extract, Transform, and Load process is used to design and incorporate discrete data into the knowledge graph, and a knowledge graph embedding model is trained and tested to assess inferential capabilities of the graph. A use case with a dataset related to the Gironde department in France is showcased. The promising preliminary results of a 31.376 MR, 0.312 MRR, and 0.254, 0.335, and 0.524 Hits@ 1, 3, and 10 respectively on a TransE model suggest the potential of leveraging semantic structuring and knowledge graph technology for environmental public health.