# Understanding ecological systems using knowledge graphs: an application to highly pathogenic avian influenza

## TL;DR

The paper explores using knowledge graphs to better understand complex ecological systems, specifically focusing on highly pathogenic avian influenza.

## Contribution

The novel contribution is applying knowledge graphs to ecological data to reveal relationships and generate hypotheses about HPAI.

## Key findings

- Knowledge graphs can integrate diverse ecological data and reveal known relationships.
- The method supports generating testable hypotheses about HPAI ecology.
- The approach demonstrates utility for analyzing complex ecological systems.

## Abstract

Ecological systems are complex. Representing heterogeneous knowledge about ecological systems is a pervasive challenge because data are generated from many subdisciplines, exist in disparate sources, and only capture a subset of interactions underpinning system dynamics. Knowledge graphs (KGs) have been successfully applied to organize heterogeneous data and to predict new linkages in complex systems. Though not previously applied broadly in ecology, KGs have much to offer in an era when system dynamics are responding to rapid changes across multiple scales.

We developed a KG to demonstrate the method’s utility for ecological problems focused on highly pathogenic avian influenza (HPAI), a highly transmissible virus with a broad host range, wide geographic distribution, and rapid evolution with pandemic potential. We describe the development of a graph to include data related to HPAI including pathogen–host associations, species distributions, and population demographics, using a semantic ontology that defines relationships within and between datasets. We use the graph to perform a set of proof-of-concept analyses validating the method and identifying patterns of HPAI ecology. We underscore the generalizable value of KGs to ecology including ability to reveal previously known relationships and testable hypotheses in support of a deeper mechanistic understanding of ecological systems.

The data and code are available under the MIT License on GitHub at https://github.com/cghss-data-lab/uga-pipp.