An Ontology for Decision-Making Support in Air Traffic Management
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data models are developed by an organization to facilitate the data to be generated, used, and stored [1]. A data model includes the framework that represents
real-world entities, format of encoding, and restrictions on the range of data.
However, aviation data providers may adopt a variety of data models. Furthermore, another key requirement for designing future Decision-Support Systems
(DSS) for ATM is to perform tasks agilely and reliably with little human effort
[2].
Ontology models can describe concepts and their relations, and reason based
on description logic. Thus, ontology facilitates decision-making in ATM by
improving data combination and reasoning according to ATM rules. This paper
proposes an ontology consisting of 4D trajectory and thunderstorm information,
as well as a reasoning architecture and an automatic decision-making process in
future ATM systems.
This paper is organized as follows. Section 2 gives an literature review of
current technologies. Backgrounds of ontology-based decision-making in aviation are introduced in Sect. 3. Sections 4 and 5 discusses the ontology models of
four-dimensional (4D) trajectories and thunderstorms. A detailed case study for
the proposed approach is shown in Sect. 6. Section 7 concludes this paper and
presents future research topics.
2 Related Work
2.1 Ontologies
An ontology [3] is a kind of data framework that is widely used in the many
research areas, i.e., artificial intelligence (AI), knowledge engineering, and semantic web [4]. Gruber [5] thinks that an ontology represents “an explicit specification of a conceptualization,” where conceptualization is an abstract, simplified
view of the world. Borst and Akkermans argue that an ontology is a formal,
explicit specification of a shared conceptualization [6]. Formal indicates that the
ontology should be understood by computers. Explicit requires that the concepts
and the relations between them are defined explicitly. Shared means that a group
of people can use an ontology to express knowledge.
An ontology is based on “triples” that are often expressed by a subjectpredicate-object structure. Triples are usually expressed by Resource Description
Framework (RDF) [7] and RDF Schema (RDFS) [8], and both of them form the
foundation for the Web Ontology Language (OWL) [9]. Currently, OWL is one
of the most popular languages for semantic modeling today.
In this paper, an ontology is the explicit description of the set of concepts,
properties, and relationships in a certain domain. For our purpose, the domain is
decision-making in ATM. The concepts are the key components about the ATM
domain, e.g., 4D trajectories, airports, and thunderstorms [10].
Properties describe the characteristics of the concepts. Location is a property
of a thunderstorm that will be common across all thunderstorms. The location
of the thunderstorm may differ, but we can expect each thunderstorm to have
one. Each 4D trajectory of a flight, regardless of its instantiation, will have a
459
data models are developed by an organization to facilitate the data to be generated, used, and stored [1]. A data model includes the framework that represents
real-world entities, format of encoding, and restrictions on the range of data.
However, aviation data providers may adopt a variety of data models. Furthermore, another key requirement for designing future Decision-Support Systems
(DSS) for ATM is to perform tasks agilely and reliably with little human effort
[2].
Ontology models can describe concepts and their relations, and reason based
on description logic. Thus, ontology facilitates decision-making in ATM by
improving data combination and reasoning according to ATM rules. This paper
proposes an ontology consisting of 4D trajectory and thunderstorm information,
as well as a reasoning architecture and an automatic decision-making process in
future ATM systems.
This paper is organized as follows. Section 2 gives an literature review of
current technologies. Backgrounds of ontology-based decision-making in aviation are introduced in Sect. 3. Sections 4 and 5 discusses the ontology models of
four-dimensional (4D) trajectories and thunderstorms. A detailed case study for
the proposed approach is shown in Sect. 6. Section 7 concludes this paper and
presents future research topics.
2 Related Work
2.1 Ontologies
An ontology [3] is a kind of data framework that is widely used in the many
research areas, i.e., artificial intelligence (AI), knowledge engineering, and semantic web [4]. Gruber [5] thinks that an ontology represents “an explicit specification of a conceptualization,” where conceptualization is an abstract, simplified
view of the world. Borst and Akkermans argue that an ontology is a formal,
explicit specification of a shared conceptualization [6]. Formal indicates that the
ontology should be understood by computers. Explicit requires that the concepts
and the relations between them are defined explicitly. Shared means that a group
of people can use an ontology to express knowledge.
An ontology is based on “triples” that are often expressed by a subjectpredicate-object structure. Triples are usually expressed by Resource Description
Framework (RDF) [7] and RDF Schema (RDFS) [8], and both of them form the
foundation for the Web Ontology Language (OWL) [9]. Currently, OWL is one
of the most popular languages for semantic modeling today.
In this paper, an ontology is the explicit description of the set of concepts,
properties, and relationships in a certain domain. For our purpose, the domain is
decision-making in ATM. The concepts are the key components about the ATM
domain, e.g., 4D trajectories, airports, and thunderstorms [10].
Properties describe the characteristics of the concepts. Location is a property
of a thunderstorm that will be common across all thunderstorms. The location
of the thunderstorm may differ, but we can expect each thunderstorm to have
one. Each 4D trajectory of a flight, regardless of its instantiation, will have a