Know, Know Where, Know Where Graph: A densely connected, cross-domain knowledge graph and geo-enrichment service stack for applications in environmental intelligence

Krzysztof Janowicz; Pascal Hitzler; Wenwen Li; Dean Rehberger; Mark Schildhauer; Rui Zhu; Cogan Shimizu; Colby K. Fisher; Ling Cai; Gengchen Mai; Joseph Zalewski; Lu Zhou; Shirly Stephen; Seila Gonzalez; Bryce Mecum; Anna Lopez-Carr; Andrew Schroeder; David Smith; Dawn Wright; Sizhe Wang; Yuanyuan Tian; Zilong Liu; Meilin Shi; Anthony D’onofrio; Zhining Gu; Kitty Currier

doi:10.1002/aaai.12043

Know, Know Where, Know Where Graph: A densely connected, cross-domain knowledge graph and geo-enrichment service stack for applications in environmental intelligence

Krzysztof Janowicz^*
, Pascal Hitzler
, Wenwen Li
, Dean Rehberger
, Mark Schildhauer
, Rui Zhu
, Cogan Shimizu
, Colby K. Fisher
, Ling Cai
, Gengchen Mai
, Joseph Zalewski
, Lu Zhou
, Shirly Stephen
, Seila Gonzalez
, Bryce Mecum
, Anna Lopez-Carr
, Andrew Schroeder
, David Smith
, Dawn Wright
, Sizhe Wang

Yuanyuan Tian, Zilong Liu, Meilin Shi, Anthony D’onofrio, Zhining Gu, Kitty Currier

^*Corresponding author for this work

School of Geographical Sciences

Research output: Contribution to journal › Article (Academic Journal) › peer-review

119 Citations (Scopus)

335 Downloads (Pure)

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 happenedherebefore,”and“howdoesthisregioncompareto…”foranyregionon earth within seconds.

Original language	English
Pages (from-to)	30-39
Number of pages	10
Journal	AI Magazine
Volume	43
Issue number	1
Early online date	30 Mar 2022
DOIs	https://doi.org/10.1002/aaai.12043
Publication status	Published - Mar 2022

Bibliographical note

Funding Information:
The authors acknowledge support by the National Science Foundation under Grant 2033521 A1: KnowWhere-Graph: Enriching and Linking Cross-Domain Knowledge Graphs using Spatially-Explicit AI Technologies. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Publisher Copyright:
© 2022 The Authors.

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Janowicz, K., Hitzler, P., Li, W., Rehberger, D., Schildhauer, M., Zhu, R., Shimizu, C., Fisher, C. K., Cai, L., Mai, G., Zalewski, J., Zhou, L., Stephen, S., Gonzalez, S., Mecum, B., Lopez-Carr, A., Schroeder, A., Smith, D., Wright, D., ... Currier, K. (2022). Know, Know Where, Know Where Graph: A densely connected, cross-domain knowledge graph and geo-enrichment service stack for applications in environmental intelligence. AI Magazine, 43(1), 30-39. https://doi.org/10.1002/aaai.12043

@article{be176aba9dec456c961501a0e8556b7b,

title = "Know, Know Where, Know Where Graph: A densely connected, cross-domain knowledge graph and geo-enrichment service stack for applications in environmental intelligence",

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 happenedherebefore,”and“howdoesthisregioncompareto…”foranyregionon earth within seconds.",

author = "Krzysztof Janowicz and Pascal Hitzler and Wenwen Li and Dean Rehberger and Mark Schildhauer and Rui Zhu and Cogan Shimizu and Fisher, \{Colby K.\} and Ling Cai and Gengchen Mai and Joseph Zalewski and Lu Zhou and Shirly Stephen and Seila Gonzalez and Bryce Mecum and Anna Lopez-Carr and Andrew Schroeder and David Smith and Dawn Wright and Sizhe Wang and Yuanyuan Tian and Zilong Liu and Meilin Shi and Anthony D{\textquoteright}onofrio and Zhining Gu and Kitty Currier",

note = "Funding Information: The authors acknowledge support by the National Science Foundation under Grant 2033521 A1: KnowWhere-Graph: Enriching and Linking Cross-Domain Knowledge Graphs using Spatially-Explicit AI Technologies. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. Publisher Copyright: {\textcopyright} 2022 The Authors.",

year = "2022",

month = mar,

doi = "10.1002/aaai.12043",

language = "English",

volume = "43",

pages = "30--39",

journal = "AI Magazine",

issn = "0738-4602",

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Janowicz, K, Hitzler, P, Li, W, Rehberger, D, Schildhauer, M, Zhu, R, Shimizu, C, Fisher, CK, Cai, L, Mai, G, Zalewski, J, Zhou, L, Stephen, S, Gonzalez, S, Mecum, B, Lopez-Carr, A, Schroeder, A, Smith, D, Wright, D, Wang, S, Tian, Y, Liu, Z, Shi, M, D’onofrio, A, Gu, Z & Currier, K 2022, 'Know, Know Where, Know Where Graph: A densely connected, cross-domain knowledge graph and geo-enrichment service stack for applications in environmental intelligence', AI Magazine, vol. 43, no. 1, pp. 30-39. https://doi.org/10.1002/aaai.12043

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 - Funding Information: The authors acknowledge support by the National Science Foundation under Grant 2033521 A1: KnowWhere-Graph: Enriching and Linking Cross-Domain Knowledge Graphs using Spatially-Explicit AI Technologies. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. Publisher Copyright: © 2022 The Authors.

PY - 2022/3

Y1 - 2022/3

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.

UR - https://www.scopus.com/pages/publications/85134355262

U2 - 10.1002/aaai.12043

DO - 10.1002/aaai.12043

M3 - Article (Academic Journal)

AN - SCOPUS:85134355262

SN - 0738-4602

VL - 43

SP - 30

EP - 39

JO - AI Magazine

JF - AI Magazine

IS - 1

ER -

Know, Know Where, Know Where Graph: A densely connected, cross-domain knowledge graph and geo-enrichment service stack for applications in environmental intelligence

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