Providing Humanitarian Relief Support through Knowledge Graphs

Rui Zhu; Ling Cai; Gengchen Mai; Cogan Shimizu; Colby K. Fisher; Krzysztof Janowicz; Anna Lopez-Carr; Andrew Schroeder; Mark Schildhauer; Yuanyuan Tian; Shirly Stephen; Zilong Liu

doi:10.1145/3460210.3493581

Providing Humanitarian Relief Support through Knowledge Graphs

Rui Zhu
, Ling Cai
, Gengchen Mai
, Cogan Shimizu
, Colby K. Fisher
, Krzysztof Janowicz
, Anna Lopez-Carr
, Andrew Schroeder
, Mark Schildhauer
, Yuanyuan Tian
, Shirly Stephen
, Zilong Liu

School of Geographical Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

6 Citations (Scopus)

Abstract

Disasters are often unpredictable and complex events, requiring humanitarian organizations to understand and respond to many different issues simultaneously and immediately. Often the biggest challenge to improving the effectiveness of the response is quickly finding the right expert, with the right expertise concerning a specific disaster type/disaster and geographic region. To assist in achieving such a goal, this paper demonstrates a knowledge graph-based search engine developed on top of an expert knowledge graph. It accommodates three modes of information retrieval, including a follow-your-nose search, an expert similarity search, and a SPARQL query interface. We will demonstrate utilizing the system to rapidly navigate from a hazard event to a specific expert who may be helpful, for example. More importantly, as the data is fully integrated including links between hazards and their abstract topics, we can find experts who have relevant expertise while navigating the graph.

Original language	English
Title of host publication	K-CAP 2021 - Proceedings of the 11th Knowledge Capture Conference
Publisher	Association for Computing Machinery
Pages	285-288
Number of pages	4
ISBN (Electronic)	9781450384575
DOIs	https://doi.org/10.1145/3460210.3493581
Publication status	Published - 2 Dec 2021
Event	11th ACM International Conference on Knowledge Capture, K-CAP 2021 - Virtual, Online, United States Duration: 2 Dec 2021 → 3 Dec 2021

Publication series

Name	K-CAP 2021 - Proceedings of the 11th Knowledge Capture Conference
ISSN (Electronic)	1549-5922

Conference

Conference	11th ACM International Conference on Knowledge Capture, K-CAP 2021
Country/Territory	United States
City	Virtual, Online
Period	2/12/21 → 3/12/21

Bibliographical note

Funding Information:
This work was partially supported by the NSF award 2033521, “KnowWhereGraph: Enriching and Linking Cross-Domain Knowledge Graphs using Spatially-Explicit AI Technologies".

Publisher Copyright:
© 2021 ACM.

Keywords

disaster response
expert search
expert system
knowledge graph
similarity

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10.1145/3460210.3493581Licence: Unspecified

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Cite this

Zhu, R., Cai, L., Mai, G., Shimizu, C., Fisher, C. K., Janowicz, K., Lopez-Carr, A., Schroeder, A., Schildhauer, M., Tian, Y., Stephen, S., & Liu, Z. (2021). Providing Humanitarian Relief Support through Knowledge Graphs. In K-CAP 2021 - Proceedings of the 11th Knowledge Capture Conference (pp. 285-288). (K-CAP 2021 - Proceedings of the 11th Knowledge Capture Conference). Association for Computing Machinery. https://doi.org/10.1145/3460210.3493581

@inproceedings{f5dafdc9cfa04c448bba7608303c2fae,

title = "Providing Humanitarian Relief Support through Knowledge Graphs",

abstract = "Disasters are often unpredictable and complex events, requiring humanitarian organizations to understand and respond to many different issues simultaneously and immediately. Often the biggest challenge to improving the effectiveness of the response is quickly finding the right expert, with the right expertise concerning a specific disaster type/disaster and geographic region. To assist in achieving such a goal, this paper demonstrates a knowledge graph-based search engine developed on top of an expert knowledge graph. It accommodates three modes of information retrieval, including a follow-your-nose search, an expert similarity search, and a SPARQL query interface. We will demonstrate utilizing the system to rapidly navigate from a hazard event to a specific expert who may be helpful, for example. More importantly, as the data is fully integrated including links between hazards and their abstract topics, we can find experts who have relevant expertise while navigating the graph. ",

keywords = "disaster response, expert search, expert system, knowledge graph, similarity",

author = "Rui Zhu and Ling Cai and Gengchen Mai and Cogan Shimizu and Fisher, \{Colby K.\} and Krzysztof Janowicz and Anna Lopez-Carr and Andrew Schroeder and Mark Schildhauer and Yuanyuan Tian and Shirly Stephen and Zilong Liu",

note = "Funding Information: This work was partially supported by the NSF award 2033521, “KnowWhereGraph: Enriching and Linking Cross-Domain Knowledge Graphs using Spatially-Explicit AI Technologies{"}. Publisher Copyright: {\textcopyright} 2021 ACM.; 11th ACM International Conference on Knowledge Capture, K-CAP 2021 ; Conference date: 02-12-2021 Through 03-12-2021",

year = "2021",

month = dec,

day = "2",

doi = "10.1145/3460210.3493581",

language = "English",

series = "K-CAP 2021 - Proceedings of the 11th Knowledge Capture Conference",

publisher = "Association for Computing Machinery",

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Zhu, R, Cai, L, Mai, G, Shimizu, C, Fisher, CK, Janowicz, K, Lopez-Carr, A, Schroeder, A, Schildhauer, M, Tian, Y, Stephen, S & Liu, Z 2021, Providing Humanitarian Relief Support through Knowledge Graphs. in K-CAP 2021 - Proceedings of the 11th Knowledge Capture Conference. K-CAP 2021 - Proceedings of the 11th Knowledge Capture Conference, Association for Computing Machinery, pp. 285-288, 11th ACM International Conference on Knowledge Capture, K-CAP 2021, Virtual, Online, United States, 2/12/21. https://doi.org/10.1145/3460210.3493581

Providing Humanitarian Relief Support through Knowledge Graphs. / Zhu, Rui; Cai, Ling; Mai, Gengchen et al.
K-CAP 2021 - Proceedings of the 11th Knowledge Capture Conference. Association for Computing Machinery, 2021. p. 285-288 (K-CAP 2021 - Proceedings of the 11th Knowledge Capture Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - Providing Humanitarian Relief Support through Knowledge Graphs

AU - Zhu, Rui

AU - Cai, Ling

AU - Mai, Gengchen

AU - Shimizu, Cogan

AU - Fisher, Colby K.

AU - Janowicz, Krzysztof

AU - Lopez-Carr, Anna

AU - Schroeder, Andrew

AU - Schildhauer, Mark

AU - Tian, Yuanyuan

AU - Stephen, Shirly

AU - Liu, Zilong

N1 - Funding Information: This work was partially supported by the NSF award 2033521, “KnowWhereGraph: Enriching and Linking Cross-Domain Knowledge Graphs using Spatially-Explicit AI Technologies". Publisher Copyright: © 2021 ACM.

PY - 2021/12/2

Y1 - 2021/12/2

N2 - Disasters are often unpredictable and complex events, requiring humanitarian organizations to understand and respond to many different issues simultaneously and immediately. Often the biggest challenge to improving the effectiveness of the response is quickly finding the right expert, with the right expertise concerning a specific disaster type/disaster and geographic region. To assist in achieving such a goal, this paper demonstrates a knowledge graph-based search engine developed on top of an expert knowledge graph. It accommodates three modes of information retrieval, including a follow-your-nose search, an expert similarity search, and a SPARQL query interface. We will demonstrate utilizing the system to rapidly navigate from a hazard event to a specific expert who may be helpful, for example. More importantly, as the data is fully integrated including links between hazards and their abstract topics, we can find experts who have relevant expertise while navigating the graph.

AB - Disasters are often unpredictable and complex events, requiring humanitarian organizations to understand and respond to many different issues simultaneously and immediately. Often the biggest challenge to improving the effectiveness of the response is quickly finding the right expert, with the right expertise concerning a specific disaster type/disaster and geographic region. To assist in achieving such a goal, this paper demonstrates a knowledge graph-based search engine developed on top of an expert knowledge graph. It accommodates three modes of information retrieval, including a follow-your-nose search, an expert similarity search, and a SPARQL query interface. We will demonstrate utilizing the system to rapidly navigate from a hazard event to a specific expert who may be helpful, for example. More importantly, as the data is fully integrated including links between hazards and their abstract topics, we can find experts who have relevant expertise while navigating the graph.

KW - disaster response

KW - expert search

KW - expert system

KW - knowledge graph

KW - similarity

UR - https://www.scopus.com/pages/publications/85120884400

U2 - 10.1145/3460210.3493581

DO - 10.1145/3460210.3493581

M3 - Conference Contribution (Conference Proceeding)

AN - SCOPUS:85120884400

T3 - K-CAP 2021 - Proceedings of the 11th Knowledge Capture Conference

SP - 285

EP - 288

BT - K-CAP 2021 - Proceedings of the 11th Knowledge Capture Conference

PB - Association for Computing Machinery

T2 - 11th ACM International Conference on Knowledge Capture, K-CAP 2021

Y2 - 2 December 2021 through 3 December 2021

ER -

Providing Humanitarian Relief Support through Knowledge Graphs

Abstract

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Bibliographical note

Keywords

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