Creating an ATC knowledge graph in support of the artificial situational awareness system

Michael Schrefl; Bernd Neumayr; Sebastian Gruber; Marlene Hartmann; Ivan Tukaric; Tomislav Radisic

doi:10.1016/j.trpro.2022.09.037

Creating an ATC knowledge graph in support of the artificial situational awareness system

Michael Schrefl, Bernd Neumayr, Sebastian Gruber, Marlene Hartmann, Ivan Tukaric, Tomislav Radisic

Institute of Business Informatics - Data and Knowledge Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

Automation has been recognized as a possible solution for increasing air traffic controller workload trends. This paper presents a methodology for creating an air traffic control knowledge graph, which is used as part of a hybrid artificial intelligence system for air traffic control operations. The system combines machine learning and symbolic reasoning with the purpose of achieving artificial situational awareness in a narrow domain of en-route air traffic control operations. This approach allows the use of user-defined knowledge alongside existing knowledge repositories. The novel knowledge graph development methodology is universal for any area of air traffic management which relies on the aeronautical information exchange models. In this paper we also present the open-source tools which were developed to make this approach possible and system performance evaluations. Future work should address achieving real-time operation and additional task automation, accompanied by appropriate ontology and graph expansion.

Original language	English
Title of host publication	Proceedings of the International Scientific Conference "The Science and Development of transport" (ZIRP 2022), September 28-30, 2022, Sibenik, Croatia
Editors	Marjana Petrovic, Irina Dovbischuk and André Luiz Cunha
Publisher	Elsevier Publishing
Pages	328-336
Number of pages	9
Volume	64
DOIs	https://doi.org/10.1016/j.trpro.2022.09.037
Publication status	Published - Aug 2022

Publication series

Name	Journal Transportation Research Procedia
ISSN (Print)	2352-1465

Fields of science

102 Computer Sciences
102010 Database systems
102015 Information systems
102016 IT security
102025 Distributed systems
102027 Web engineering
102028 Knowledge engineering
102030 Semantic technologies
102033 Data mining
102035 Data science
509026 Digitalisation research
502050 Business informatics
502058 Digital transformation
503008 E-learning

JKU Focus areas

Digital Transformation

Access to Document

10.1016/j.trpro.2022.09.037

Cite this

Schrefl, M., Neumayr, B., Gruber, S., Hartmann, M., Tukaric, I., & Radisic, T. (2022). Creating an ATC knowledge graph in support of the artificial situational awareness system. In Marjana Petrovic, Irina Dovbischuk and André Luiz Cunha (Ed.), Proceedings of the International Scientific Conference "The Science and Development of transport" (ZIRP 2022), September 28-30, 2022, Sibenik, Croatia (Vol. 64, pp. 328-336). (Journal Transportation Research Procedia). Elsevier Publishing. https://doi.org/10.1016/j.trpro.2022.09.037

Schrefl, Michael ; Neumayr, Bernd ; Gruber, Sebastian et al. / Creating an ATC knowledge graph in support of the artificial situational awareness system. Proceedings of the International Scientific Conference "The Science and Development of transport" (ZIRP 2022), September 28-30, 2022, Sibenik, Croatia. editor / Marjana Petrovic, Irina Dovbischuk and André Luiz Cunha. Vol. 64 Elsevier Publishing, 2022. pp. 328-336 (Journal Transportation Research Procedia).

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abstract = "Automation has been recognized as a possible solution for increasing air traffic controller workload trends. This paper presents a methodology for creating an air traffic control knowledge graph, which is used as part of a hybrid artificial intelligence system for air traffic control operations. The system combines machine learning and symbolic reasoning with the purpose of achieving artificial situational awareness in a narrow domain of en-route air traffic control operations. This approach allows the use of user-defined knowledge alongside existing knowledge repositories. The novel knowledge graph development methodology is universal for any area of air traffic management which relies on the aeronautical information exchange models. In this paper we also present the open-source tools which were developed to make this approach possible and system performance evaluations. Future work should address achieving real-time operation and additional task automation, accompanied by appropriate ontology and graph expansion.",

author = "Michael Schrefl and Bernd Neumayr and Sebastian Gruber and Marlene Hartmann and Ivan Tukaric and Tomislav Radisic",

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language = "English",

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Schrefl, M, Neumayr, B, Gruber, S, Hartmann, M, Tukaric, I & Radisic, T 2022, Creating an ATC knowledge graph in support of the artificial situational awareness system. in Marjana Petrovic, Irina Dovbischuk and André Luiz Cunha (ed.), Proceedings of the International Scientific Conference "The Science and Development of transport" (ZIRP 2022), September 28-30, 2022, Sibenik, Croatia. vol. 64, Journal Transportation Research Procedia, Elsevier Publishing, pp. 328-336. https://doi.org/10.1016/j.trpro.2022.09.037

Creating an ATC knowledge graph in support of the artificial situational awareness system. / Schrefl, Michael; Neumayr, Bernd; Gruber, Sebastian et al.
Proceedings of the International Scientific Conference "The Science and Development of transport" (ZIRP 2022), September 28-30, 2022, Sibenik, Croatia. ed. / Marjana Petrovic, Irina Dovbischuk and André Luiz Cunha. Vol. 64 Elsevier Publishing, 2022. p. 328-336 (Journal Transportation Research Procedia).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

TY - GEN

T1 - Creating an ATC knowledge graph in support of the artificial situational awareness system

AU - Schrefl, Michael

AU - Neumayr, Bernd

AU - Gruber, Sebastian

AU - Hartmann, Marlene

AU - Tukaric, Ivan

AU - Radisic, Tomislav

PY - 2022/8

Y1 - 2022/8

N2 - Automation has been recognized as a possible solution for increasing air traffic controller workload trends. This paper presents a methodology for creating an air traffic control knowledge graph, which is used as part of a hybrid artificial intelligence system for air traffic control operations. The system combines machine learning and symbolic reasoning with the purpose of achieving artificial situational awareness in a narrow domain of en-route air traffic control operations. This approach allows the use of user-defined knowledge alongside existing knowledge repositories. The novel knowledge graph development methodology is universal for any area of air traffic management which relies on the aeronautical information exchange models. In this paper we also present the open-source tools which were developed to make this approach possible and system performance evaluations. Future work should address achieving real-time operation and additional task automation, accompanied by appropriate ontology and graph expansion.

AB - Automation has been recognized as a possible solution for increasing air traffic controller workload trends. This paper presents a methodology for creating an air traffic control knowledge graph, which is used as part of a hybrid artificial intelligence system for air traffic control operations. The system combines machine learning and symbolic reasoning with the purpose of achieving artificial situational awareness in a narrow domain of en-route air traffic control operations. This approach allows the use of user-defined knowledge alongside existing knowledge repositories. The novel knowledge graph development methodology is universal for any area of air traffic management which relies on the aeronautical information exchange models. In this paper we also present the open-source tools which were developed to make this approach possible and system performance evaluations. Future work should address achieving real-time operation and additional task automation, accompanied by appropriate ontology and graph expansion.

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BT - Proceedings of the International Scientific Conference "The Science and Development of transport" (ZIRP 2022), September 28-30, 2022, Sibenik, Croatia

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Schrefl M, Neumayr B, Gruber S, Hartmann M, Tukaric I, Radisic T. Creating an ATC knowledge graph in support of the artificial situational awareness system. In Marjana Petrovic, Irina Dovbischuk and André Luiz Cunha, editor, Proceedings of the International Scientific Conference "The Science and Development of transport" (ZIRP 2022), September 28-30, 2022, Sibenik, Croatia. Vol. 64. Elsevier Publishing. 2022. p. 328-336. (Journal Transportation Research Procedia). doi: 10.1016/j.trpro.2022.09.037

Creating an ATC knowledge graph in support of the artificial situational awareness system

Abstract

Publication series

Fields of science

JKU Focus areas

Access to Document

Other files and links

AISA - AI Situational Awareness Foundation for Advancing Automation

Cite this

Creating an ATC knowledge graph in support of the artificial situational awareness system

Abstract

Publication series

Fields of science

JKU Focus areas

Access to Document

Other files and links

Projects

AISA - AI Situational Awareness Foundation for Advancing Automation

Cite this