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|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-2317/article-10.pdf
|volume=Vol-2317
|dblpUrl=https://dblp.org/rec/conf/semweb/SchuetzNSGVW18
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==None==
https://ceur-ws.org/Vol-2317/article-10.pdf
The Case for Contextualized Knowledge Graphs
in Air Traffic Management
Christoph G. Schuetz1 , Bernd Neumayr1 , Michael Schrefl1 ,
Eduard Gringinger2 , Audun Vennesland3 , and Scott Wilson4
1
Johannes Kepler University Linz, Linz, Austria
{schuetz,neumayr,schrefl}@dke.uni-linz.ac.at
2
Frequentis AG, Vienna, Austria
eduard.gringinger@frequentis.com
3
Norwegian University of Science and Technology, Trondheim, Norway
audun.vennesland@sintef.no
4
EUROCONTROL, Brussels, Belgium
scott.wilson@eurocontrol.int
Abstract. Knowledge graphs represent real-world entities and their re-
lationships with each other, with a broad range of applications in various
domains. In this paper, we argue that contextualized knowledge graphs
fit naturally with air traffic management, which heavily depends on the
timely flow of required information. In particular, we illustrate how the
temporality concept of the Aeronautical Information Exchange Model
is represented using contextualized knowledge graphs with a temporal
dimension. We further explain what spatial, provenance, and other se-
mantic dimensions of context are relevant for air traffic management.
We draw from experience with past research projects on the benefits of
employing semantic web technologies for air traffic management.
Keywords: Resource Description Framework · Aeronautical Informa-
tion Exchange Model · ATM knowledge graphs · Temporal context.
1 Introduction
For various applications, organizations now employ knowledge graphs. A knowl-
edge graph (KG) comprises knowledge about real-word entities and their rela-
tionships with each other (see [6] for further information). KGs mainly focus on
ABox (instance) rather than TBox (schema) knowledge, although ontologies may
serve to organize KGs and infer knowledge (see [14]). Hence, a KG consists of
statements about the real world, typically covering multiple topics [14]. These
statements are often to be interpreted in a particular temporal, spatial, or other
semantic context. For example, the statement “Vienna airport’s 16/34 runway is
closed” likely refers to a particular temporal context, e.g., a specific date, unless
the runway is permanently closed. For the semantic web, numerous approaches to
context exist. Contextualized knowledge repositories [16], for example, serve as
formal framework for the representation of context, with a knowledge propagation
mechanism from more general to more specific context.
Copyright © 2018 for this paper by its authors. Copying permitted for private
and academic purposes.
2 C. G. Schuetz et al.
Modern air traffic management (ATM) relies heavily on the timely flow of rele-
vant information. Industry and academia display a growing interest in employing
semantic web technologies for ATM (see [8]). Semantic web technologies promise
to improve management and processing of ATM information, e.g., by facilitating
intelligent filtering and annotation of notifications in air traffic operations [18].
For ATM in general, information/knowledge about infrastructure, aircraft, flight
plans, weather, etc. is relevant. In the specific case, however, e.g., an individual
flight, what constitutes the valid, relevant information/knowledge depends on
the context. In this regard, various dimensions of context come into play, mostly
temporal and spatial but also other types of context such as importance and
provenance. For example, the valid, relevant knowledge for a pilot to safely
operate a flight depends, among other things, on the date (temporal context)
and route (spatial context).
In this paper, we propose the use of contextualized knowledge graphs for
ATM in order to organize relevant ATM information/knowledge for different
applications. For representing such contextualized ATM knowledge graphs, we
adapt the concept of semantic container [7, 13] as developed in the course of the
BEST project5 within the SESAR Joint Undertaking of the European Union’s
Horizon 2020 program. A semantic container is a package of data items which has
a semantic description of the container contents. The semantic description can
be regarded as a context which the semantic container contains all the relevant
information for. The remainder of this paper is organized as follows. In Sect. 2, we
briefly present information (exchange) models for ATM. In Sect. 3, we introduce
contextualized ATM knowledge graphs. We conclude with a brief discussion, a
presentation of related work, and an outlook on future work.
2 Information Models for Air Traffic Management
ATM employs multiple information (exchange) models for various different pur-
poses. The Aeronautical Information Exchange Model (AIXM [1]), for example,
serves to exchange information about aeronautical features, e.g., characteristics of
navigation aids and runways, as well as temporary changes thereof. Similarly, the
ICAO Meteorological Information Exchange Model (IWXXM) and the Flight In-
formation Exchange Model (FIXM) serve to exchange information about weather
and flight plans, respectively. The ATM Information Reference Model (AIRM),
on the other hand, incorporates concepts from various information (exchange)
models, acting as common reference model (see [21] for further information).
The AIXM Temporality Concept [19] allows for the definition of “deltas” with
respect to the baseline information, indicating temporary changes to the state of
the world, e.g., a temporarily closed runway. In this regard, the notion of time
slice is central: Each aeronautical feature, in a given time interval – the time
slice – has a state (or none at all). AIXM distinguishes between baseline and
tempdelta as well as snapshot time slices. The baseline consists of the regular
5
http://project-best.eu/
Contextualized Knowledge Graphs in ATM 3
Listing 1. An example DNOTAM in XML notifying of airport closure due to snow
1
- < notamMessage : AIXMBasicMessage >
2
- < notamMessage : hasMember >
3
- < AirportHeliport gml : id =" VIE1 " >
4
- < timeSlice >
5
- < A ir p o r tH e l i po r t T im e S l ic e gml : id =" VIE1_TS1 " >
6
- < gml : validTime >
7
- < gml : TimeInstant gml : id =" VIE1_TS1_TI " >
8
- < gml : timePosition >2018 -06 -08 T08 :00:00
9
- < interpretation > SNAPSHOT
10
- < locationIndicatorICAO > LOWW
11
- < timeSlice >
12
- < A ir p o r tH e l i po r t T im e S l ic e gml : id =" VIE1_TS2 " >
13
- < gml : validTime >
14
- < gml : TimePeriod gml : id =" VIE1_TS2_TP " >
15
- < gml : beginPosition >2018 -06 -08 T08 :00:00
16
- < gml : endPosition >2018 -06 -09 T08 :00:00
17
- < interpretation > TEMPDELTA
18
- < availability >
19
- < A i r p o r t H e l i p o r t A v a i l a b i l i t y gml : id =" VIE1_AV1 " >
20
- < operationalStatus > CLOSED
21
- < annotation >
22 - < Note gml : id =" VIE1_NT1 " >
23 - < propertyName > operationalStatus
24 - < purpose > REMARK
25 - < note lang =" eng " > DUE TO SNOW
feature values (which may change) whereas the tempdelta defines overriding
temporary feature values. Snapshot refers to the current feature values when
considering both baseline and tempdelta time slices. Listing 1 shows the XML
representation of a Digital Notice to Airmen (DNOTAM) according to AIXM, a
message that notifies of a temporary change of the baseline information regarding
an airport’s operational status. The time slice with id “VIE1_TS1” (Lines 5-10)
shows the current state of Vienna airport with respect to its ICAO location
indicator. The time slice with id “VIE1_TS2” (Lines 11-25) defines a tempdelta
for the 24-hour period beginning at 8:00 a.m. on 8th June 2018, indicating a
closure of Vienna airport due to snow.
AIXM describes a comprehensive conceptual model in UML, which along
with IWXXM and FIXM may be used to represent the state of the world (as
relevant for the aeronautical domain) and construct an ATM KG. Although XML
serves as the representation format of DNOTAMs in AIXM, AIXM builds on
the Geography Markup Language (GML), which itself was heavily influenced by
RDF [11, p. 20]. Hence, RDF is a natural fit for the representation of AIXM and
the construction of KGs based upon it. Existing ATM ontologies may also serve
to construct ATM KGs. For example, in the BEST project, we developed an
4 C. G. Schuetz et al.
AIRM ontology [20] which may act as starting point to define further background
knowledge. The NASA ATM Ontology [9] also allows for the representation of
similar ATM information/knowledge.
3 Contextualized ATM Knowledge Graphs
Context serves to structure ATM knowledge graphs in order to accommodate for
the different information needs of different applications. The context determines
which information/knowledge is valid or relevant. Contextualized KGs explicitly
represent the context and thus facilitate selection of valid and relevant informa-
tion/knowledge. In the following, we illustrate the representation of context in
AIXM, particularly temporal context.
A context is characterized by a dimensional vector consisting of concepts from
different ontologies, one for each context dimension. The dimensional vectors
serve to determine coverage relationships between contexts. More specifically,
subsumption or other relationships between concepts in the ontologies determine
the coverage relationships between contexts. Consider, for example, the context
with knowledge relevant in the year 2018 for the entirety of Europe, which covers
the context with knowledge relevant in January 2018 for the route from Vienna
to Frankfurt. We say that when a context c covers a context c0 , the context c0 is
narrower than c. Conversely, we say that c is broader than c0 .
The coverage relationship between contexts is important for knowledge prop-
agation. Specifically, the broader contexts propagate their knowledge to the
covered, narrower contexts. We refer to related work on contextualized knowl-
edge repositories [16, 3] for further information on such knowledge propagation
mechanism for contexts.
Context information often derives directly from the model elements. The
AIXM time slices, for example, serve to associate statements with contexts along
a temporal dimension. Figure 1 shows a contextualized RDF representation of
the DNOTAM information from Listing 1. Metadata and general information
such as the definition of the VIE1 individual as an instance of AirportHeliport
with ICAO location indicator “LOWW” are associated with a temporal context
characterized by All-TemporalEntity rather than any particular time instant or
interval. The All-TemporalEntity concept serves as the single top concept of the
temporal dimension; any time instant or interval is subsumed by that top concept.
Then, another context characterized by the particular time interval from 8th to
9th June 2018 at 8:00 a.m. – which is narrower than the first context – comprises
the statements indicating temporary changes to the baseline data, namely the
closure of VIE1 airport due to snow.
The AIXM Temporal Concept specifies that tempdelta property values over-
ride values from the baseline context. For example, some features such as navi-
gation aids operate on a schedule which might change temporarily. To enable
such an override mechanism in knowledge propagation, an additional context
dimension captures the time slice interpretation. Each context either consists
of baseline, tempdelta, or snapshot information as per the AIXM definition.
Contextualized Knowledge Graphs in ATM 5
All-TemporalEntity
AirportHeliportAvailability
rdfs:domain
AirportHeliport annotation operationalStatus
rdfs:range rdfs:range
rdf:type rdfs:domain
rdfs:domain
VIE1 Note CodeStatusAirportType
locationIndicatorICAO
rdfs:domain
rdfs:domain
purpose
LOWW note
rdfs:range
CodeNotePurposeType
2018-06-08T08:00:00 – narrower
2018-06-09T08:00:00
AirportHeliportAvailability
CodeNotePurposeType
Note
rdf:type operationalStatus
rdf:type
availability VIE1_AV1
propertyName
rdf:type REMARK
VIE1 annotation purpose
VIE1_NT1 CodeStatusAirportType
note
operationalStatus rdf:type
DUE_TO_SNOW CLOSED
Fig. 1. Contextualized ATM knowledge graph which contains the DNOTAM information
from Listing 1. Dotted lines indicate inferred triples. The broader context defines
schema information (TBox) as well as general knowledge, which serve to define and
infer knowledge in the narrower context.
K0
Navaid NavaidOperationalStatus All-TemporalEntity : Baseline:
rdf:type temporalEntity interpretation
rdf:type worksBy VIE1_NAV1_SCHED1
c0 : ATMKnowledge
VIE1_NAV1 RunwayDirection + knowledge = K0
VIE1_RWD1
installedAt rdf:type
K1 2018-04-06 – 2018-05-06 : Tempdelta:
temporalEntity interpretation
NavaidOperationalStatus
VIE1_NAV1 c1 : ATMKnowledge
rdf:type
+ knowledge = K1
worksBy narrower
VIE1_NAV1_SCHED2
K2
narrower
Navaid NavaidOperationalStatus 2018-04-24: Snapshot:
temporalEntity interpretation
rdf:type
rdf:type worksBy VIE1_NAV1_SCHED2
c2 : ATMKnowledge
VIE1_NAV1 RunwayDirection + knowledge = K2
VIE1_RWD1
installedAt rdf:type
Fig. 2. Knowledge propagation with override. The right-hand side shows the different
contexts. The left-hand side shows the RDF triples associated with the different contexts.
The gray triples are inherited knowledge from the broader contexts.
6 C. G. Schuetz et al.
A snapshot combines baseline and tempdelta information; snapshot contexts
inherit from both the baseline and tempdelta contexts. Figure 2 illustrates the
introduction of such an interpretation dimension along with the override mecha-
nism. The context c0 contains baseline knowledge valid in the general case. The
navigation aid VIE1_NAV1, installed at the VIE1_RWD1 runway direction works
by the VIE1_NAV1_SCHED1 schedule. A tempdelta time slice c1 introduces the
VIE1_NAV1_SCHED2 schedule for the VIE1_NAV1 navigation aid in effect in
the time interval from 6th April to 6th May 2018. The snapshot context c2 for
24th June 2018 inherits the knowledge from c0 and c1 , with properties from the
tempdelta context overriding property values from the baseline context.
In terms of semantic containers [7], the broader contexts are composite con-
tainers which comprise the subsumed narrower contexts as component containers.
The narrower contexts – the component containers – “inherit” the knowledge that
the subsuming broader context – the composite container – explicitly associates:
The knowledge propagates from the broader context to the narrower contexts.
Specific merge operations may serve to combine the knowledge from various
different contexts.
Different ontologies may serve to define the context dimensions. In the previous
examples we adopted the concept TemporalEntity from the OWL-Time ontology [5]
in order to describe values in the temporal dimension. Here, the various temporal
relationships may serve to determine coverage relationships between contexts.
For example, we might define coverage in such a temporal dimension to be based
on the intervalDuring property. Note that we employ the OWL-Time ontology as
vocabulary for expressing intervals and instants in the temporal dimension and
the schedule knowledge. Furthermore, to support merge operations with unions of
non-overlapping intervals – the result of which is not an interval – our approach
would benefit from an extension of OWL-Time with a temporal element as a
finite union of intervals (see [17]).
Contexts may also capture geographic relevance of knowledge. Geographic
locations may be represented using GeoSPARQL [15] vocabulary and concepts
derived thereof. For example, flight routes could be defined as geographic concepts
spanning a certain area on the map, based on coordinates defined using the
GeoSPARQL vocabulary. Then, a context may contain all the relevant knowledge
for a specific flight route, possibly in combination with a temporal dimension.
Importance is another dimension of context. Different knowledge may have
different importances (see [18] for DNOTAM importance). For example, a non-
operational navigation aid may be a potential hazard while an airport closure is
flight critical. The importance dimension will typically have to be paired with
other context dimensions in order to capture, e.g., the importance of knowledge
for a particular flight when in a certain geographic location.
Context may also capture the provenance of knowledge, thus facilitating
the accurate assessment of the reliability of the knowledge. In ATM, capturing
provenance is also important for auditability purposes in case of accidents. In
the System Wide Information Management (SWIM) concept, SWIM information
services produce and process ATM information/knowledge. In previous work [7],
Contextualized Knowledge Graphs in ATM 7
we propose a model for capturing provenance in the spirit of the PROV-O ontol-
ogy [12]. Likewise, a provenance context may capture which SWIM information
service the context’s knowledge originates from.
Along with the ABox statements, TBox statements may also be stored
in the contexts. The example in Fig. 1 contains only simple RDF Schema
statements (domain and range). More complex ontologies could also be employed
in contextualized ATM KGs. Existing ATM ontologies are modular (cf. [20, 9]).
A topic dimension could then serve to associate contexts with topics according
to the ATM ontology modules.
4 Discussion, Related Work, and Outlook
In this paper, we have argued for contextualized KGs in ATM. We have illus-
trated how the idea of contextualization fits naturally with the information
requirements of modern ATM. Different applications have different information
needs at different points in time. Contextualization allows for the convenient
representation of ATM information/knowledge along temporal, spatial, or other
semantic dimensions. Applications may then select the appropriate knowledge for
their tasks from the available contexts, using the context dimensions to merge
the knowledge from these contexts.
The aeronautical domain is increasingly becoming aware of the benefits of
semantic web technologies (see [8]). In particular, semantic web technologies
have been employed for data integration [10], and we expect contextualization
to similarly facilitate the data integration task. The overriding propagation
of knowledge as required by AIXM could be expressed using exceptions [2].
Furthermore, contextualized rule repositories [4] may complement the static
information/knowledge with active rules.
Contextualized ATM knowledge graphs may serve different types of appli-
cations: operational and analytical. Both types of applications have different
requirements regarding the context dimensions. In particular, the analysis of
events in air traffic flow and capacity management (ATFCM) may benefit from
more homogeneous context dimensions in the spirit of data warehouses, e.g.,
a fixed granularity of the temporal dimension. Future work will introduce a
framework for management and querying of contextualized KGs.
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