TY - JOUR
T1 - Know, Know Where, Know Where Graph
T2 - A densely connected, cross-domain knowledge graph and geo-enrichment service stack for applications in environmental intelligence
AU - Janowicz, Krzysztof
AU - Hitzler, Pascal
AU - Li, Wenwen
AU - Rehberger, Dean
AU - Schildhauer, Mark
AU - Zhu, Rui
AU - Shimizu, Cogan
AU - Fisher, Colby K.
AU - Cai, Ling
AU - Mai, Gengchen
AU - Zalewski, Joseph
AU - Zhou, Lu
AU - Stephen, Shirly
AU - Gonzalez, Seila
AU - Mecum, Bryce
AU - Lopez-Carr, Anna
AU - Schroeder, Andrew
AU - Smith, David
AU - Wright, Dawn
AU - Wang, Sizhe
AU - Tian, Yuanyuan
AU - Liu, Zilong
AU - Shi, Meilin
AU - D’onofrio, Anthony
AU - Gu, Zhining
AU - Currier, Kitty
N1 - Publisher Copyright:
© 2022 The Authors.
PY - 2022/3/1
Y1 - 2022/3/1
N2 - 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 happenedherebefore,”and“howdoesthisregioncompareto…”foranyregionon earth within seconds.
AB - 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 happenedherebefore,”and“howdoesthisregioncompareto…”foranyregionon earth within seconds.
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U2 - 10.1002/aaai.12043
DO - 10.1002/aaai.12043
M3 - Article
AN - SCOPUS:85134355262
SN - 0738-4602
VL - 43
SP - 30
EP - 39
JO - AI Magazine
JF - AI Magazine
IS - 1
ER -