Know, Know Where, KnowWhereGraph: A densely connected, cross‐domain knowledge graph and geo‐enrichment service stack for applications in environmental intelligence. Issue 1 (31st March 2022)

• Record Type: Journal Article
• Authors: Janowicz, Krzysztof; Hitzler, Pascal; Li, Wenwen; Rehberger, Dean; Schildhauer, Mark; Zhu, Rui; Shimizu, Cogan; Fisher, Colby K.; Cai, Ling; Mai, Gengchen; Zalewski, Joseph; Zhou, Lu; Stephen, Shirly; Gonzalez, Seila; Mecum, Bryce; Lopez‐Carr, Anna; Schroeder, Andrew; Smith, David; Wright, Dawn; Wang, Sizhe; Tian, Yuanyuan; Liu, Zilong; Shi, Meilin; D'Onofrio, Anthony; Gu, Zhining; Currier, Kitty
• Is Part Of: AI magazine. Volume 43:Issue 1 (2022)
• Page Start: 30
• Page End: 39
• Publication Date: 2022-03-31
• Subjects: Artificial intelligence -- Periodicals; Artificial intelligence -- Computer programs -- Periodicals; System design -- Periodicals; Artificial intelligence -- Technological innovations -- Periodicals; Artificial intelligence -- Industrial applications -- Periodicals; Artificial intelligence -- Educational applications -- Periodicals; Intelligence artificielle -- Périodiques; Intelligence artificielle -- Logiciels -- Périodiques; Conception de systèmes -- Périodiques; Intelligence artificielle -- Innovations -- Périodiques; Intelligence artificielle -- Applications industrielles -- Périodiques; Intelligence artificielle -- Applications en éducation -- Périodiques; Artificial intelligence; Artificial intelligence -- Computer programs; Artificial intelligence -- Educational applications; Artificial intelligence -- Industrial applications; System design; Kunstmatige intelligentie; Electronic journals; Periodicals; Periodicals; 006.305
• DOI: 10.1002/aaai.12043
• Languages: English
• ISSNs: 0738-4602

Abstract

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.