Know, Know Where, Knowwheregraph: A Densely Connected, Cross-Domain Knowledge Graph and Geo-Enrichment Service Stack for Applications in Environmental Intelligence

TLDR

Knowledge graphs enable representation, retrieval, and integration of heterogeneous data and are central to modern search engines and AI, yet they remain underutilized in environmental intelligence. This study aims to explain why spatial data need special treatment and how environmental data can be semantically lifted into a knowledge graph. The authors introduce KnowWhereGraph, a densely connected graph integrating human–environment datasets, and describe geospatial enrichment services built atop it. Together, the graph and services answer questions such as “what is here,” “what happened here before,” and “how does this region compare to …” for any Earth region within seconds.

Abstract

Knowledge graphs (KGs) are a novel paradigm for the representation, retrieval, and integration of data from highly heterogeneous sources. Within just a few years, KGs and their supporting technologies have become a core component of modern search engines, intelligent personal assistants, business intelligence, and so on. Interestingly, despite large-scale data availability, they have yet to be as successful in the realm of environmental data and environmental intelligence. In this paper, we will explain why spatial data require special treatment, and how and when to semantically lift environmental data to a KG. We will present our KnowWhereGraph that contains a wide range of integrated datasets at the human–environment interface, introduce our application areas, and discuss geospatial enrichment services on top of our graph. Jointly, the graph and services will provide answers to questions such as “what is here,” “what happened here before,” and “how does this region compare to …” for any region on earth within seconds.