Knowledge graphs are a novel paradigm for the representation, retrieval, and integration of data from highly heterogeneous sources. Within just a few years, knowledge graphs and
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.
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 requires special treatment, and how and when to semantically lift environmental data to a knowledge graph. 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 Information
| Publication Year | 2022 |
|---|---|
| Title | Know, Know Where, KnowWhereGraph: A densely connected, cross-domain knowledge graph and geo-enrichment service stack for applications in environmental intelligence |
| DOI | 10.1002/aaai.12043 |
| Authors | Krzysztof Janowicz, Pascal Hitzler, Wenwen Li, Dean Rehberger, Mark P. Schildhauer, Rui Zhu, Cogan Shimizu, Colby K Fisher, Ling Cai, Gengchen Mai, Joseph Zalewski, Lu Zhou, Shirly Stephen, Seila Gonzalez, Anna Lopez-Carr, Andrew Schroeder, Dave Smith, E. Lynn Usery, Dalia E. Varanka, Dawn Wright, Sizhe Wang, Yuanyuan Tian, Zilong Liu, Meilin Shi, Anthony D'Onofrio, Zhining Gu, Kitty Currier |
| Publication Type | Article |
| Publication Subtype | Journal Article |
| Series Title | AI Magazine |
| Index ID | 70239380 |
| Record Source | USGS Publications Warehouse |
| USGS Organization | Center for Geospatial Information Science (CEGIS) |