TY - GEN
T1 - Sustainable Grain Transportation in Ukraine Amidst War Utilizing KNARM and KnowWhereGraph
AU - Zhang, Yinglun
AU - Broyaka, Antonina
AU - Kastens, Jude
AU - Featherstone, Allen M.
AU - Shimizu, Cogan
AU - Hitzler, Pascal
AU - Mcginty, Hande Küçük
N1 - Publisher Copyright:
© 2023 Owner/Author.
PY - 2023/4/30
Y1 - 2023/4/30
N2 - In this work, we propose a sustainable path-finding application for grain transportation during the ongoing Russian military invasion in Ukraine. This application is to build a suite of algorithms to find possible optimal paths for transporting grain that remains in Ukraine. The application uses the KNowledge Acquisition and Representation Methodology(KNARM) and the KnowWhereGraph to achieve this goal. Currently, we are working towards creating an ontology that will allow for a more effective heuristic approach by incorporating the lessons learned from the KnowWhereGraph. The aim is to enhance the path-finding process and provide more accurate and efficient results. In the future, we will continue exploring and implementing new techniques that can further improve the sustainability of the path-finding applications with a knowledge graph backend for grain transportation through hazardous and adversarial environments. The code is available upon reviewer's request. It can not be made public due to the sensitive nature of the data.
AB - In this work, we propose a sustainable path-finding application for grain transportation during the ongoing Russian military invasion in Ukraine. This application is to build a suite of algorithms to find possible optimal paths for transporting grain that remains in Ukraine. The application uses the KNowledge Acquisition and Representation Methodology(KNARM) and the KnowWhereGraph to achieve this goal. Currently, we are working towards creating an ontology that will allow for a more effective heuristic approach by incorporating the lessons learned from the KnowWhereGraph. The aim is to enhance the path-finding process and provide more accurate and efficient results. In the future, we will continue exploring and implementing new techniques that can further improve the sustainability of the path-finding applications with a knowledge graph backend for grain transportation through hazardous and adversarial environments. The code is available upon reviewer's request. It can not be made public due to the sensitive nature of the data.
KW - global food systems
KW - knowledge graphs
KW - ontology engineering
KW - path-finding
UR - https://corescholar.libraries.wright.edu/cse/706
UR - https://www.scopus.com/pages/publications/85159571622
UR - https://www.scopus.com/pages/publications/85159571622#tab=citedBy
U2 - 10.1145/3543873.3587618
DO - 10.1145/3543873.3587618
M3 - Conference contribution
AN - SCOPUS:85159571622
T3 - ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023
SP - 742
EP - 745
BT - ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023
PB - Association for Computing Machinery, Inc
T2 - 32nd Companion of the ACM World Wide Web Conference, WWW 2023
Y2 - 30 April 2023 through 4 May 2023
ER -