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.

Citation

Janowicz, K., P. Hitzler, W. Li, D. Rehberger, M. Schildhauer, R. Zhu, C. Shimizu, C. K. Fisher, L. Cai, G. Mai, J. Zalewski, L. Zhou, S. Stephen, S. Gonzalez, B. Mecum, A. Lopez-Carr, A. Schroeder, D. Smith, D.Wright, S. Wang, Y. Tian, Z. Liu, M. Shi, A. D’Onofrio, Z. Gu, and K. Currier. 2022. “Know, KnowWhere, KnowWhereGraph: A densely connected, cross-domain knowledge graph and geo-enrichment service stack for applications in environmental intelligence.” AI Magazine 43: 30–39. https://doi.org/10.1002/aaai.12043