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Citation: Car, N.J.; Homburg, T.
GeoSPARQL 1.1: Motivations,
Details and Applications of the
Decadal Update to the Most
Important Geospatial LOD Standard.
ISPRS Int. J. Geo-Inf. 2022,11, 117.
https://doi.org/
10.3390/ijgi11020117
Academic Editors: Rob Brennan,
Brian Davis, Armin Haller and
Beyza Yaman
Received: 7 November 2021
Accepted: 7 January 2022
Published: 7 February 2022
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4.0/).
International Journal of
Geo-Information
Article
GeoSPARQL 1.1: Motivations, Details and Applications
of the Decadal Update to the Most Important Geospatial
LOD Standard
Nicholas J. Car 1,2,*,†,‡ and Timo Homburg 3,†,‡
1SURROUND Australia Pty Ltd., Canberra 2600, Australia
2College of Engineering and Computer Science, Australian National University, Canberra 2600, Australia
3i3mainz–Institute for Spatial Information & Surveying Technology, Mainz University of Applied Sciences,
55128 Mainz, Germany; timo.homburg@hs-mainz.de
*Correspondence: nicholas.car@anu.edu.au; Tel.: +61-477-560-177
† This paper is an extended version of our paper published in GeoLD Workshop at ESWC 2021.
‡ These authors contributed equally to this work.
Abstract:
In 2012, the Open Geospatial Consortium published GeoSPARQL defining “an RDF/OWL
ontology for [spatial] information”, “SPARQL extension functions” for performing spatial operations
on RDF data and “RIF rules” defining entailments to be drawn from graph pattern matching. In
the 8+ years since its publication, GeoSPARQL has become the most important spatial Semantic
Web standard, as judged by references to it in other Semantic Web standards and its wide use for
Semantic Web data. An update to GeoSPARQL was proposed in 2019 to deliver a version 1.1 with a
charter to: handle outstanding change requests and source new ones from the user community and
to “better present” the standard, that is to better link all the standard’s parts and better document
and exemplify elements. Expected updates included new geometry representations, alignments to
other ontologies, handling of new spatial referencing systems, and new artifact presentation. This
paper describes motivating change requests and actual resultant updates in the candidate version
1.1 of the standard alongside reference implementations and usage examples. We also describe the
theory behind particular updates, initial implementations of many parts of the standard, and our
expectations for GeoSPARQL 1.1’s use.
Keywords:
GeoSPARQL; GeoSPARQL 1.1; spatial; geospatial; Semantic Web; RDF; OWL; OGC; Open
Geospatial Consortium; standard; ontology
1. Introduction and Motivation
The GeoSPARQL standard, issued in 2012 by the Open Geospatial Consortium (OGC)
(https://www.ogc.org, accessed on 30 October 2021) is one of the most popular Semantic
Web standards for spatial data. References to GeoSPARQL in other well-known standards,
such as DCAT2 [
1
] and CIDOC-CRM [
2
,
3
] suggests it is popular, as do the incoming
links in Linked Open Vocabularies (https://lov.linkeddata.es/dataset/lov/vocabs/gsp,
accessed on 30 October 2021) and the list of implementors that includes most of the
popular triplestore vendors, a list of which has been compiled here: https://github.com/
opengeospatial/ogc-geosparql/issues/59, accessed on 30 October 2021. The original
release–GeoSPARQL 1.0 [4]–contained:
• A specification: document:
–
The main GeoSPARQL document defining, in human-readable terms and with
code snippets, most elements of the standard including ontology elements,
geospatial functions that may be performed on Resource Description Format
(RDF) [
5
] data via SPARQL [
6
,
7
] queries, entailment rules in the Rules Inter-
change Format (RIF) [
8
] for RDF reasoning and requirements and abstract tests
for testing ontology data and function implementations.
ISPRS Int. J. Geo-Inf. 2022,11, 117. https://doi.org/10.3390/ijgi11020117 https://www.mdpi.com/journal/ijgi
ISPRS Int. J. Geo-Inf. 2022,11, 117 2 of 30
• An RDF/OWL [9]schema:
–The GeoSPARQL ontology—Semantic Web data model—in an RDF file.
• An RDF vocabulary:
–
The simple features vocabulary for “defining SimpleFeature geometry types”
taken from [10] in RDF/OWL terms, also in an RDF file.
In this form, GeoSPARQL 1.0 has been used widely; however, requests for updates
to it have been received by the OGC. In this publication, an extension of the workshop
paper [
11
], we discuss the motivations behind updating GeoSPARQL 1.0 in the remainder of
this section, content of the planned GeoSPARQL 1.1 release (see the GeoSPARQL working
repository for the latest candidate release form of GeoSAPRQL 1.1: https://github.com/
opengeospatial/ogc-geosparql, accessed on 30 October 2021) in Section 2and reference
implementations and use cases in Sections 3and 4. Finally, in Section 5, we provide an
outlook to further feature requests which are likely to be tackled in future GeoSPARQL
releases or replacements.
As the GeoSPARQL 1.1 standard, at the time of publication of [
11
] had still been in
active development, we point to changes which have occurred since. These changes involve
the removal of the class
geo:SpatialMeasure
and the inclusion of several supplementary
materials (alignments, SHACL shapes and JSON-LD contexts) on which we elaborate
extensively in this publication.
The OGC and World Wide Web Consortium’s (W3C) Spatial Data On The Web Working
Group (SDWWG) published a list of Best Practice points (see https://w3c.github.io/sdw/
bp/, accessed on 30 October 2021 for an accessible version of the points online and [
12
]
for the corresponding academic publication) for rating web-based spatial data. Through
the use of that rating system and other work, the SDWWG noted that: “A best practice
for returning geometries in a specific requested CRS has not yet emerged”. Given the
scope of GeoSPARQL 1.0, this statement indicates that GeoSPARQL 1.0 had not then
‘solved’ web-based spatial data publishing. The group also informally captured specific
suggested updates for GeoSPARQL (https://www.w3.org/2015/spatial/wiki/Further_
development_of_GeoSPARQL, accessed on 30 October 2021), however no updates to
GeoSPARQL itself were then made.
The authors note that in the 3+ years since that statement’s publication,
GeoSPARQL 1.0
has become far more widely supported by Semantic Web databases (so-called “triplestores”)
and other Semantic Web applications, as evidenced by frequent attempts to benchmark
geospatial-aware triplestores for GeoSPARQL compliance and performance [
13
–
16
]. Some
further notes on GeoSPARQL support is provided in Section 5.1.
In 2019, the OGC reconstituted the GeoSPARQL Standards Working Group (SWG) to
update GeoSPARQL. The motivation for work within the area of GeoSPARQL, that of
spatial Semantic Web data more generally, and some specific fault fixes and proposed
extensions to GeoSPARQL 1.0 are captured in an OGC White Paper [
17
]. Some, but not all,
of the SDWWG’s issues raised were taken up by the SDW, for example, the Best Practices [
12
]
aspiration that “A possible way forward is an update for the GeoSPARQL spatial ontology.
This would provide an agreed spatial ontology, i.e., a bridge or common ground between
geographical and non-geographical spatial data
. . .
”. This issue is specifically addressed in
GeoSPARQL 1.1’s extensions for scalar spatial data.
Other Best Practices issues raised such as “it makes sense to publish different geometric
representations of a spatial object that can be used for different purposes” are partly ad-
dressed; GeoSPARQL 1.1 indeed indicates how to use multiple geometric representations of
geometries in relation to a single feature, but concepts such as defining roles for geometries,
with respect to features, have not been implemented.
The SWG’s Charter–their final scope of work–is also published by the OGC [
18
]
and this guided the SWG’s activities. Specific actions of the SWG and their staging are
explained through the use of a publicly-available, online, task tracking system within
the SWG’s working online code repository (https://github.com/opengeospatial/ogc-
geosparql/projects/1, accessed on 30 October 2021).
ISPRS Int. J. Geo-Inf. 2022,11, 117 3 of 30
At a high-level, proposed updates to GeoSPARQL by both the SDWWG and the SWG
may be categorised as one of the following:
• New geometry serialisations
–GeoJSON, KML and other now-popular formats missing from GeoSPARQL 1.0
–The possibility to convert between literal formats in-query
• New and specialised ontology classes and properties
–More nuanced spatial data representation and alignment with other systems
• More spatial functions
–Implementing functions well-known in non-Semantic Web spatial systems
• Scalar spatial properties
–Area, volume, etc., alongside geometries
• Better handling of Spatial (Coordinate) Reference Systems (SRS)
–Allowing for automated coordinate serialisation conversions
• Internet protocol-based selection of different geometries for features
Some of these proposed updates were predicted in GeoSPARQL 1.0, with the Fu-
ture Work section listing several of the points above. The SWG’s Charter, anticipating
that the more obvious updates such as new geometry serialisations would certainly be
implemented, listed the following extra areas of investigation that emerged from SWG
proponent’s discussions:
•
Revising “upper ontology” GeoSPARQL structure–how its classes relate to fundamen-
tal concepts in ontology
• Alignments to other ontologies, perhaps W3C Time Ontology in OWL [19]
• Catering for very different SRSes, such as Discrete Global Grid Systems
Specifically ruled out of scope in the Charter was any investigation of property graphs.
Recent (last several years) discussions in the OGC and elsewhere about property graphs
motivated their consideration. However, the SWG proponents felt that while property
graphs might be important for future Semantic Web spatial data systems, there was more
than enough work scoped for initial SWG work (several revisions of the standard) to
initially exclude this area of investigation.
After initial meetings, the SWG determined to make multiple versioned releases of
GeoSPARQL updates with different goals:
• 1.1 : Extensions that are fully compatible with GeoSPARQL 1.0
•
1.2: Fully or mostly compatible extensions but which are larger additions to the
standard’s conceptual coverage
• 2.0: Future GeoSPARQL likely incompatible with GeoSPARQL 1.0
The reason for expecting a future, incompatible, GeoSPARQL 2.0 is that early SWG atten-
dees thought spatio-temporal relations and fundamental ontology elements in GeoSPARQL
either could or should be remodeled, which might break the current, familiar, feature/geome-
try class relations. Details of these potential changes have not been fully expounded at the
time of this paper, however initial SWG attendees’ intuition is that a future GeoSPARQL
2.0, or perhaps a renamed GeoSPARQL replacement, might generalise spatial concepts and
move away from only, or primarily, geospatial, or perhaps focus not just on feature/geom-
etry relations but look to generalised mechanisms for describing dimensions of features
of which geometry is just one of many, and temporality might be another. See Section 5for
further details.
Originally unexpected, an area of updates to standard presentation was considered
by the SWG. Motivation from conceptual work within the W3C and OGC for the presen-
tation of multi-part standards and the desire by the OGC’s “Naming Authority”. (the
Naming Authority (https://www.ogc.org/projects/groups/ogcnasc, accessed on 30 Oc-
tober 2021) has the remit to ”..ensure an orderly process for assigning URIs for OGC
ISPRS Int. J. Geo-Inf. 2022,11, 117 4 of 30
resources, such as OGC documents, standards, XML namespaces, ontologies.“ and also
acts as a process evangelist, promoting, as it sees, better standards publication practice) to
publish standards more systematically and in more machine-readable forms, as well as
programs of work such as the OGC’s “Test Bed 17: Model Driven-Standards” (The Testbed
17 work package “Model Driven Standards” (https://portal.ogc.org/files/?artifact_id=
95726#ModelDrivenStandards, accessed on 30 October 2021) focused on generating docu-
ments from models but also partly developed test implementations of formally-defined,
multi-part, standards, such as GeoSPARQL 1.1), this has resulted in the profile declaration
explained in the next section.
2. Updates in GeoSPARQL 1.1
In 2021, the GeoSPARQL SWG addressed many of the GeoSPARQL change requests
in the 1.1 release. All of the changes reported here are now visible in the current working
draft of the GeoSPARQL 1.1 standard, the specification document, and the other standard
parts [
20
]. It is expected that the candidate standard will remain essentially unchanged
through to final publication, barring perhaps minor updates due to wider implementation
feedback. This section lists work completed only (see Section 5.1 for further notes on final
GeoSPARQL 1.1 work) and will point out new features, possibilities, and applications of
the elements included in the GeoSPARQL 1.1 update. At first, we discuss the new structure
of the standard’s specification (Section 2.1), describe relevant extensions to the ontology
model and query language in Sections 2.9 and 2.4.
2.1. Profile Declaration
One of the first SWG actions was to link GeoSPARQL 1.0 elements through a profile
declaration, where a profile is a formally-defined variant of a standard.profile and standard,
as well as relations between them and their parts, are defined by The Profiles Vocabulary [
21
].
The initial motivation for this was twofold: the SWG’s recognition that GeoSPARQL
1.0 consisted of multiple parts, not all of which were easy to discover, so some GeoSPARQL
users were unaware of GeoSPARQL resources, some of which have been accidentally
duplicated or partly re-implemented, and the desire for machine-readable forms of as many
of the standard’s parts as possible.
Descriptions of multi-part standards using the Profiles Vocabulary are anticipated by the
OGC as being their future best practice method for standards delivery (This is ascertained
through personal communication with the OGC’s Naming Authority staff, one of whom
was a co-editor of The Profiles Vocabulary).
As the elements of GeoSPARQL 1.1 have been created, they too have been described
using the Profile Vocabulary, and GeoSPARQL 1.0 has been indicated as being a profile
of, that is, a subset of, GeoSPARQL 1.1 since all GeoSPARQL 1.0 elements are present in
GeoSPARQL 1.1.
The profile declaration for GeoSPARQL 1.0 and all of its parts will be published
alongside those of GeoSPARQL 1.1, currently expected in early 2022. Currently, all draft 1.0
and 1.1 resources are available in the SWG’s online code repository (https://github.com/
opengeospatial/ogc-geosparql, accessed on 30 October 2021).
The 1.1 releases’ profile resources, described using roles given in the Profiles
Vocabulary are
:
1. A profile declaration
•
The definition of the profile links to the things it profiles and a listing of its parts
• Nn human (HTML) and machine (RDF) readable forms
2. A specification resource
• As per GeoSPARQL 1.0, the normative document of the GeoSPARQL standard
• Contains requirements and conformance classes
•
Presented as a document in human-readable form (a PDF) but also containing
normative code (schema) snippets and function definition tables and examples
ISPRS Int. J. Geo-Inf. 2022,11, 117 5 of 30
3. An RDF/OWL model schema resource
• The GeoSPARQL 1.1 ontology, in both RDF and HTML forms
4. Several vocabulary resources
• Mainly derived from the schema
•
Presented in human- and machine-readable forms of the Simple Knowledge
Organization System (SKOS) taxonomy model [22]
•
There are vocabularies for Functions, Rules, Conformance Classes in addition to
GeoSPARQL 1.0’s Simple Features definitions
5. JSON-LD ‘context’ mappings
•
Mappings between local names and fully qualified ontology identifiers for the
GeoSPARQL 1.1 ontology and also the Simple Features definitions vocabulary
6. A validation resource
•
A series of Shapes Constraint Language (SHACL) [
23
] shapes for RDF data
validation.
All elements of the GeoSPARQL 1.1 profile are listed and linked to in the profile
declaration’s current draft, online location:
•GeoSPARQL 1.1 draft profile declaration
When finally published, this resource will be available at its namespace IRI location:
http://www.opengis.net/def/geosparql, accessed on 30 October 2021.
2.2. Ontology Extensions
GeoSPARQL 1.1 extends the GeoSPARQL ontology by adding multiple new proper-
ties and three collection classes. Initially, the SWG proposed a
SpatialMeasure
class to
represent scalar spatial measurements too, but this was ultimately not added in favour of
a series of ‘size’ properties only. See Figure 1for an overview of the current, which is a
redrawing of GeoSPARQL 1.1’s ontology overview diagram.
Figure 1.
GeoSPARQL 1.1 ontology overview diagram including classes new properties. After
GeoSPARQL 1.1’s own overview diagram.
2.2.1. Scalar Spatial Properties
Scalar properties of spatial objects, such as a volume, length, can now be indicated
in GeoSPARQL 1.1 with either a ‘metric’ property– one that indicates a literal value in
meters–or a non-metric property that may indicate a measurement value and a unit of
measure. The metric/non-metric property pairs are all sub properties of a generic ‘size’
property and have the domain of
geo:SpatialObject
meaning they can be used with
either geo:Feature or geo:Geometry instances. Properties pairs defined are:
ISPRS Int. J. Geo-Inf. 2022,11, 117 6 of 30
•geo:hasSize &geo:hasMetricSize–generic property
•geo:hasLength &geo:hasMetricLength
•geo:hasPerimeterLength &geo:hasMetricPerimeterLength
•geo:hasArea, & geo:hasMetricArea
•geo:hasVolume &
•geo:hasMetricVolume
The dual definition of metric and non-metric properties is aimed at allowing both
simple and more detailed use. The metric properties are informally preferred for use.
The SWG considered their inclusion only; however, the non-metric options were ultimately
included to allow for the use of historic units for which no conversion to the metric system
is known. Listing 1shows two metric/non-metric pairs in use for the area and perimeter
length, and Figure 2includes scalar spatial measure examples alongside other ontology
implementation examples.
Inclusion of scalar spatial measurements within GeoSPARQL itself addresses con-
cerns raised by the user community (see the SWG’s Charter) that scalar spatial use with
GeoSPARQL was occurring but that it was un-standardised or unguided and thus not
necessarily interoperable. The particular pattern of non-metric scalar spatial measure
chosen emulates common patterns for such measurements in ontologies such as the W3C’s
SOSA [24,25].
Listing 1. Scalar spatial properties for the Australian federal electoral district of Brisbane.
PREFIX ex: <http://example.com/thing/>
PREFIX geo: <http://www.opengis.net/ont/geosparql#>
PREFIX qudt: <http://qudt.org/schema/qudt/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX unit: <http://qudt.org/vocab/unit/>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
ex:brisbane
ageo:Feature ;
rdfs:label "Brisbane Electorate" ;
geo:hasMetricArea "57486676"^^xsd:double ;
geo:hasArea [
qudt:value "5748.6676"^^xsd:float ;
qudt:unit unit:HA ; # hectare
] ;
geo:hasMetricPerimeterLength "43832"^^xsd:double ;
geo:hasPerimeterLength [
qudt:numericValue "27.235942"^^xsd:float ;
qudt:unit unit:MI ; # mile
];
.
ISPRS Int. J. Geo-Inf. 2022,11, 117 7 of 30
Figure 2. Excerpt of the GeoSPARQL 1.1 ontology including one example feature.
2.2.2. New Geometry Properties
GeoSPARQL 1.1 introduced more sub properties of
geo:hasGeometry
. Where
GeoSPARQL 1.0 defined only a single property of it,
geo:hasDefaultGeometry
, GeoSPARQL
1.1 defines
geo:hasCentroid
to indicate geometries with the role of centroid, and other,
similar properties, such as geo:hasBoundingBox. Figure 3shows multiple geometries
for a single feature and Listing 2shows a representation of them in RDF with these new
properties used.
Figure 3.
Geometries of the Australian federal electoral district of Brisbane. GeoSPARQL 1.0 contained
only the property
geo:hasGeometry
to indicate a
geo:Geometry
instance for a
geo:Feature
instance.
GeoSPARQL 1.1 contains the specialised properties of
geo:hasCentroid
&
geo:hasBoundingBox
which can indicate geometries with particular roles. See Listing 2for an RDF representation of this
figure’s elements.
The SWG recognised that there could be many more subproperties of
geo:hasGeometry
added to GeoSPARQL 1.1 than the few they did ultimately add. However, no inclusion/ex-
clusion logic was determined by the group nor properties deliberately excluded. One path
for future exploration in this area mooted but not implemented for GeoSPARQL 1.1 was
the concept of geometry roles–the nature of a geometry’s role with respect to a feature–and
the SWG discussed creating a geometry roles vocabulary. This is noted again in Section 5.
ISPRS Int. J. Geo-Inf. 2022,11, 117 8 of 30
Listing 2. A possible RDF representation of the elements in Figure 3.
PREFIX ex: <http://example.com/thing/>
PREFIX geo: <http://www.opengis.net/ont/geosparql#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
ex:brisbane
ageo:Feature ;
rdfs:label "Brisbane Federal Electorate" ;
geo:hasGeometry [
ageo:Geometry ;
geo:asWKT """POLYGON ((
153.099932 -27.445258,
... ,
153.099932 -27.445258
))"""^^geo:wktLiteral ;
] ;
geo:hasCentroid [
ageo:Geometry ;
geo:asWKT """POINT (
153.030431 -27.438943
)"""^^geo:wktLiteral ;
] ;
geo:hasBoundingBox [
ageo:Geometry ;
geo:asWKT """POLYGON ((
152.975299 -27.480651,
153.099932 -27.480651,
153.099932 -27.404039,
152.975299 -27.404039,
152.975299 -27.480651
))"""^^geo:wktLiteral ;
] ;
.
2.2.3. Topological Relations
GeoSPARQL 1.1 does not define any new topological relations or properties for them
as compared to GeoSPARQL 1.0 (cf. Table A1), however, the new specification version does
include much more supporting material for users of these relations and GeoSPARQL in
general, in particular examples of real-world geometry data represented in RDF according
to GeoSPARQL 1.1. Figure 4and its associated Listing, Listing 3, show the sorts of examples
given in the GeoSPARQL 1.1 specification document (Annex C) as well as the GeoSPARQL
repository of extended examples (https://github.com/opengeospatial/ogc-geosparql/
tree/master/1.1/examples, accessed on 30 October 2021).
ISPRS Int. J. Geo-Inf. 2022,11, 117 9 of 30
Figure 4.
Geometries of the Australian federal electoral districts of Petrie and Brisbane and the state
electorate of McConnel (
left
) and Brisbane again and the state electorate of Clayfield (
right
). Brisbane
is also the same as in Figure 3. Topological spatial relations that may be declared either between
these geometries directly or between the features that these are geometries for. Possible relations
for these are: Brisbane
geo:sfContains
McConnel, McConnel
geo:sfWithin
Brisbane, Brisbane
geo:sfTouches
Petrie and McConnel
geo:sfDisjoint
Petrie. See Listing 3for an RDF representation
of this figure’s elements.
2.2.4. Support For Collections
GeoSPARQL 1.1 introduces support for collections of Features (geo:FeatureCollection)
and Geometries (geo:GeometryCollection) which are both subclasses of the more general
class geo:SpatialObjectCollection. This allows for the grouping of features and geometries
by specific attributes, and defined object collections in data are also required by standards
such as the OGC’s Features API [26].
While the GIS community commonly organises geospatial features in the form of
collections, including in software such as ArcGIS (https://www.arcgis.com/) or QGIS
(https://www.qgis.org/), before the inclusion of specific collection classes in GeoSPARQL 1.1.,
GeoSPARQL data users had to implement virtual collections from the results of SPARQL
queries for compatibility with many of their tools. This required more work on be-
half of tool maintainers for tools such as the SPARQLing Unicorn QGIS plugin (https:
//github.com/sparqlunicorn/sparqlunicornGoesGIS, accessed on 30 October 2021).
The SWG recognised that there is little semantic difference between virtual and defined
collections from an OWL modelling point of view. However, they also recognised that
other users of Semantic Web data might find defined collections much easier to use, hence
their ultimate inclusion. This follows recent Semantic Web standards practice, for example,
the creation of an extension to the W3C’s SOSA ontology for observation collections [27].
ISPRS Int. J. Geo-Inf. 2022,11, 117 10 of 30
Listing 3.
A possible RDF representation of the elements in Figure 4in the JSON-LD format which
imports the GeoSPARQL 1.1 profile’s JSON-LD context resource which allows for simpler JSON data
representation through the use of locally-defined names. Supplementary context statements are also
included for the example’s namespace and supporting namesaces–RDFS.
1{
2" @ co n te xt " :[
3" h tt ps :/ / r aw . g i t h ub u s er c o nt e n t . co m / o p en g e os p a ti a l /
4og c - g eo s pa r ql / m as t er / 1.1/ c on t ex ts / g eo - c o nt e xt . j so n " ,
5{
6" r df s " :"http:/ / w ww . w 3. or g / 2000/01/ rd f - sc h em a #" ,
7" l ab el " :"rdfs:l a be l " ,
8" ex " :"http:// ex a m pl e . c om / t h in g / "
9}
10 ],
11 "@graph":[
12 {
13 " @ id " :"ex:brisbane",
14 " @ ty pe " :"Feature",
15 " s fC o nt a in s ":{" @ i d " :"e x :m cc o nn e l "},
16 " s fT o uc he s " :{" @i d " :" ex :petrie"},
17 " s fO v er l ap s ":{" @ i d " :"e x :c la y fi e ld " },
18 " l ab el " :" B r is ba n e Fe d er a l El e ct o ra t e "
19 },
20 {
21 " @ id " :"ex:mcconnel",
22 " @ ty pe " :"Feature",
23 " s fD i sj o in t ":{" @ i d " :"e x :petrie"},
24 " s fW i th in " :{" @ id " :" e x :brisbane"},
25 " l ab el " :" M c Co nn e l St a te E le c to r at e "
26 },
27 {
28 " @ id " :"ex:petrie",
29 " @ ty pe " :"Feature",
30 " s fD i sj o in t ":{" @ i d " :"e x :m cc o nn e l "},
31 " s fT o uc he s " :{" @i d " :" ex :brisbane"},
32 " l ab el " :" P et r ie F e de r al E l ec t or a te "
33 },
34 {
35 " @ id " :"ex:cl a yf ie l d ",
36 " @ ty pe " :"Feature",
37 " s fO v er l ap s ":{" @ i d " :"e x :b ri s ba n e "},
38 " l ab el " :" C l ay fi e ld S t at e El e ct o ra t e "
39 }
40 ]
41 }
It needs to be remarked that the support of geometry collections in GeoSPARQL 1.1
does not replace the previously defined class sf:GeometryCollection defined in the
GeoSPARQL 1.0
Simple Features vocabulary. The geo:GeometryCollection class can be
used to group geometries which have been defined natively in RDF. The sf:GeometryCollection
class is used to define a GeometryCollection within a geometry literal, e.g., WKT Listing 4,
which is therefore not modeled in RDF. Listing 4exemplifies a GeometryCollection within
a WKT geometry literal.
ISPRS Int. J. Geo-Inf. 2022,11, 117 11 of 30
Listing 4. GeometryCollection in WKT.
GEOMETRYCOLLECTION (
POINT (40 10),
LINESTRING (10 10, 20 20, 10 40),
POLYGON ((40 40, 20 45, 45 30, 40 40))
)
However, GeoSPARQL 1.1 improves the access to geometry collections defined in
literal types by defining new SPARQL extension functions:
•
geof:geometryN–allows the retrieval of the nth geometry inside a
sf:GeometryCollection
instance
•
geof:numGeometries–allows the retrieval of the number of geometries contained in a
sf:GeometryCollection instance
2.3. Ontology Alignments
GeoSPARQL may be the most widely used ontology to represent spatial data, but there
are many other vocabularies in use which try to encode geospatial locations. Many of these
ontologies, such as W3CGeo, Geonames or Wikidata representations of geolocations may
only be used to encode point geometries. However, some vocabularies such as NeoGeo or
schema.org, also allow for the representation of more complex geometries, for example in
Well-Known-Text literals. Alignments with GeoSPARQL 1.1 and 15 other vocabularies con-
taining spatial elements are presented in an Annex of the upcoming standard. In addition
to written statements and tables of alignments, alignments have also been presented as RDF
files for direct reuse in RDF databases. Listing 5shows two individual alignments–from
schema.org and from the Provenance Ontology–to exemplify their presentation in the
standard: a direct class mapping and a property for which there is no mapping mapping.
Listing 5.
Two example mappings: a class mapping with schema.org and a non-mapping with a
Provenance Ontology property.
1. sc h em a . o rg ( s do )
s do : G e o sp a t ia l G eo m e tr y o wl : eq u i va l e nt C l as s g eo : S p a ti a l Ob j e ct ~.
Since G e os p a tia l G eo m e try is the doma i n o f S i m p leFea t u r e - like
p ro p e r ti e s an d a su p e rc l a ss o f ~ G e oS h a pe
2. P r ov e na n ce ~ O n to lo g y
p ro v : at L oc at i on : no ~ m a pp in g
T he P RO V pr o p er t y in d ic a te s a pr ov : L o ca t io n , so p e rh a ps a
geo : Feature , b u t ~ G eoS P A R QL has no pro p e r ty to i ndi c a t e a
g eo : F e at u re
2.4. New Geometry Literal Types
Three new geometry serializations are introduced in GeoSPARQL 1.1:
1. GeoJSON (Geo-JavaScript Object Notation) [28].
2. KML (Keyhole Markup Language) [29].
3. DGGS (Discrete Global Grid System) [30].
ISPRS Int. J. Geo-Inf. 2022,11, 117 12 of 30
2.4.1. GeoJSON & KML
GeoJSON & KML have been much anticipated and were requested by the SDWWG
and many users of GeoSPARQL, due to those formats’ popularity. The DGGS format is
more forward-looking in that it is not driven by user demand but by predicted demand.
An example GeoJSON geometry, a point, representing the location of the centroid
of the Brisbane electoral district from Figure 3is given in Listing 6and that geometry’s
inclusion in GeoSPARQL 1.1. RDF is given in Listing 8. Listing 7shows the same geometry
in KML for comparison.
Listing 6. GeoJSON point geometry.
1{
2" t yp e " :" Po in t " ,
3"coordinates":[153.030431,-27.438943]
4}
Listing 7. KML point geometry.
1<Point >
2<coordinates >153.030431,-27.438943</coordinates >
3</ P o in t >
In GeoSPARQL 1.1, geometry data formats that allow for feature information, such
as GeoJSON, are limited to representing geometries only, to avoid possibly conflicting
literal and RDF information. This is in keeping with GeoSPARQL 1.0, which imposed the
same restriction on GML data. In the example in Figure 3, GeoJSON is used to indicate
the geometry type–
"type":"Point"
–as well as give the geometry’s coordinates, but not
feature annotations, such as name.
Keyhole Markup Language (KML) is a well-known, XML-based, GML-like geometry
format. Its use in GeoSPARQL 1.1 is straightforward.
Listing 8.
A representation of the centroid point of the Brisbane electoral district from Figure 3in
RDF (Turtle serialization) using the GeoJSON literal data from Listing 6.
PREFIX ex: <http://example.com/thing/>
PREFIX geo: <http://www.opengis.net/ont/geosparql#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
ex:brisbane
ageo:Feature ;
rdfs:label "Brisbane Electorate Centroid" ;
geo:hasCentroid [
ageo:Geometry ;
geo:asGeoJSON
"""{
"type":"Point",
"coordinates":[153.030431, -27.438943]
}"""^^geo:geoJSONLiteral ;
] ;
.
2.4.2. DGGS Literals
Discrete Global Grid System (DGGS) descriptions of spatial objects can be represented
in GeoSPARQL 1.1 using literals too, and their inclusion in GeoSPARQL 1.1 took far more
ISPRS Int. J. Geo-Inf. 2022,11, 117 13 of 30
consideration than either GeoJSON or KML. Listing 9gives an AusPIX (https://w3id.org/
dggs/auspix, accessed on 30 October 2021) DGGS representation of the area of the Brisbane
electoral district from Figure 3.
Listing 9.
A DGGS geometry serialization example in RDF (Turtle) of the area of the feature in
Figure 3
. The generic predicate for indicating a DGGS serialization–
geo:asDGGS
–is used and the
specific DGGS–AusPIX–is indicated via use of a custom datatype ex:auspixLiteral.
PREFIX ex: <http://example.com/thing/>
PREFIX geo: <http://www.opengis.net/ont/geosparql#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
ex:brisbane
ageo:Feature ;
rdfs:label "Brisbane Electorate Centroid" ;
geo:hasGeometry [
ageo:Geometry ;
geo:asDGGS
"""<https://w3id.org/dggs/auspix>
CELLLIST ((
R8338506 R8338507
R8338508 R8338516
R8338530 R8338531
R8338532 R8338534
R8338540
))"""^^ex:auspixLiteral ;
] ;
.
GeoSPARQL 1.1 does not provide for specific DGGS literals, for example AusPIX,
directly but only for an abstract DGGS literal with the
geo:dggsLiteral
datatype and
the relevant
geo:asDGGS
geometry property, hence the use of the example namespace
http://example.com/thing/
in Listing 9. This is because the DGGSes that currently
exist, of which AusPIX is just one, have vastly different representation formats. Users of
GeoSPARQL 1.1’s
geo:asDGGS
geometry property are expected to indicate the particular
DGGS being used by implementations of custom literal datatype properties, as per
Listing 9
.
To assist with understanding what DGGS data is, Figure 5shows the data from
Listing 9as well as finer DGGS approximations of the Brisbane electoral district’s boundary
polygon. DGGS such as AusPIX represent areas, points, lines and other geometric shapes
with collections of ’cells’ of different sizes at different ’levels’. There is no theoretical
limit as to how many levels, and thus fine cells AusPIX may use, thus too, no theoretical
resolution limit.
ISPRS Int. J. Geo-Inf. 2022,11, 117 14 of 30
Figure 5.
AusPIX Discrete Global Grid System representations of the Brisbane electoral district area
geometry. The original polygon is represented at the top with the four lower images showing AusPIX
DGGS cells at higher and higher resolutions, so-called levels 7, 8, 9 and 10.
2.4.3. Appropriateness of DGGS Data as Geometry Literals
The appropriateness of the addition of GeoJSON and KML to GeoSPARQL 1.1 as
new geometry literal formats was uncontroversial, with the single consideration of sub-
stance being the exclusion of information other than geometry information from GeoJSON,
as indicated above.
The inclusion of a placeholder or abstract property and datatype for DGGS literals
was quite controversial due to differing perceptions of what DGGS data represents. The
SWG members do not all agree that DGGS data represent geometries or features, and there
is no straightforward theoretical case to be made for either due to DGGS’ novelty and
mechanisms that are vastly different from traditional spatial data systems.
The SWG decided that the criteria for geometry representation in GeoSPARQL 1.1
were that the literals had to be able to act as geometry objects in order to satisfy at least
some major proportion of the GeoSPARQL functions. Essentially, anything that could be
used for spatial relations calculation and spatial aggregates could be treated, at least by
GeoSPARQL 1.1, as a geometry literal.
During the first half of 2021, within the lifetime of the SWG, a software library was pro-
duced that implemented all the
Simple Features
functions, except for
geof:sfCrosses
(see Section 3.1 for the implementation description). While not all Simple Features func-
tions were implemented for, nor were functions for the other families of spatial relations,
the SWG determined that, given the implementation evidence so far, AusPIX DGGS data
could indeed act as geometry literals, from GeoSPARQL’s point of view. Thus, DGGS
ISPRS Int. J. Geo-Inf. 2022,11, 117 15 of 30
literals could satisfactorily act as geometries, at least from GeoSPARQL’s point of view.
The SWG’s discussion on this matter is captured in their online code repository’s Issue 112.
2.5. New Geometry Conversion Functions
GeoSPARQL 1.1 provides the conversion functions geof:asWKT,geof:asGML,
geof:asKML,geof:asGeoJSON and geof:asDGGS to convert between different literal types
while considering the literal types capabilities. For example, a conversion of a WKT literal
in the EPSG:25832 coordinate system to either KML or GeoJSON will result in the geometry
being converted to the World Geodetic System 1984, as this is the only coordinate reference
system valid in the two respective literal types. Hence, a conversion from GeoJSON or
KML to WKT will stay in the World Geodetic System 1984 coordinate reference system. To
still be able to convert geometries to other coordinate reference systems, GeoSPARQL 1.1
adds the geof:transform function, which converts a geometry literal to another coordinate
reference system if the literal formats allow such a transformation.
Another addition is a projection function, which allows changing dimensionalities of
geometries is likely to be included (https://github.com/opengeospatial/ogc-geosparql/
issues/231, accessed on 30 October 2021).
With these additions, future triplestore implementations become very flexible in
providing a variety of geometry conversions even without being dependent on another
intermediate web service for coordinate/format conversions.
2.6. Spatial Aggregate Functions
While spatial aggregation functions are the norm in many non-semantic geospatial
databases such as PostGIS or Oracle Spatial, at the time of defining the GeoSPARQL 1.0
standard, aggregation functions had not yet been introduced into the SPARQL standard,
but have been with SPARQL 1.1 [
6
]. Spatial aggregation functions similar to traditional
(relational database) aggregation functions such as AVG, MAX, or MIN allow aggregated
results of geometry queries, for example, to create the union of a set of selected serialised
geometries. While calculating these aggregates is certainly possible outside of a semantic
database, and thus GeoSPARQL, the inclusion of the functions provides distinct advantages:
1. No client-side library is needed to create an aggregated geometry result.
2. Fewer/more appropriate results are returned, for example a union result.
3. Federated SPARQL queries can aggregate results from multiple endpoints.
In addition to geof:union,geof:envelope and geof:convexHull defined in GeoSPARQL
1.0 for use within SPARQL
FILTER
operations, 1.1 defines geof:aggUnion as well as
geof:aggBoundingCircle,geof:aggCentroid,geof:aggConcatLines–concatenating a set of overlap-
ping linestrings–and geosf:aggConcaveHull that can return aggregated results. Listing 10
shows one new function in use.
Functions to retrieve min/max values of geometries’ coordinates are added: geof:minX
&geof:maxX,geof:minY &geof:maxY and geof:minZ &geof:maxZ.
Listing 10. Aggregation Function example SPARQL query.
# returns circle bounding all geometries of Feature <x>
SELECT (geof:aggBoundingCircle(?geo) AS ?circ)
WHERE {<x> geo:hasGeometry/geo:asWKT ?geo .}
ISPRS Int. J. Geo-Inf. 2022,11, 117 16 of 30
2.7. Comparison of Query Capabilities
It makes sense to determine how the new version GeoSPARQL 1.1 compares to other,
also non-semantic query languages dealing with geospatial representations. We discuss
the comparable query languages CQL [
31
], Simple Features for SQL specification, and the
PostGIS implementation extensions as the most typical representatives of spatial data query
languages besides GeoSPARQL.
2.7.1. Common Query Language CQL
The Common Query Language CQL was developed for usage with OGC Web Services
and provides filter capabilities for feature collection or coverage data. The CQL query
language is also part of the currently in-development OGC API Features standard [
31
],
the successor specification to OGC web services. OGC API Features proposes using the
Common Query Language (CQL) for filtering but is also open to other query language
implementations such as GeoSPARQL. When comparing the filter capabilities of CQL to
GeoSPARQL, one can observe that the two query languages provide comparable spatial
functionality (cf. Table 1). However, the CQL proposed supports spatiotemporal opera-
tors, which may be an addition to GeoSPARQL to be further explored in its continuous
development process.
Table 1.
Examples of equivalences between CQL and GeoSPARQL for a literal value, query parame-
ters, and comparison predicates in FILTER expressions.
Category CQL Expression GeoSPARQL Expression
Query Parameter limit = 5 LIMIT 5
Literal Value “A string” “A string”ˆˆxsd:string
Comparison predicate name IS NOT NULL EXISTS {?item
my:name ?name}
Spatial Operators CONTAINS(geometry1,geometry2) FILTER(geof:sfContains
(?geometry1,?geometry2))
To exemplify the relationship between CQL and the GeoSPARQL query language,
the SWG will release a Best Practice document (https://opengeospatial.github.io/ogc-
geosparql/bestpractice/bestpractice_cql.html, accessed on 30 October 2021), which in-
cludes detailed descriptions of the equivalences between the two query languages.
2.7.2. Simple Features for SQL
Another interesting comparison can be made between the query capabilities of
GeoSPARQL and the Simple Features for SQL specification [
10
]. To date, we can conclude
that feature parity with the Simple feature specification has been reached with regards to the
expression of geospatial relations using properties and using filter functions. Features still
missing in the GeoSPARQL 1.1 standard are functions that specifically address attributes
of particular geometry types. In that way, it is impossible to access specific points of
LineStrings (such as start and end points) and their closedness in-query and the access of
polygonal rings and single coordinates of points using query functions. These functions
are planned to be implemented in an upcoming, possibly minor update to GeoSPARQL 1.1.
We include a comparison table between SQL and the GeoSPARQL specification to highlight
the differences in detail.
ISPRS Int. J. Geo-Inf. 2022,11, 117 17 of 30
2.7.3. PostGIS Query Capabilities
In relational spatial databases, one may observe more types of spatial functions as
compared to the Simple Features for SQL specification. In addition, more types of geospa-
tial representations can be processed. Finally, one can look at comparable implementations
in relational database systems, such as PostGIS. In particular, this is true for representing
coverage data, i.e., rasters in PostGIS. The database features comparing raster data to vector
data representations, raster algebra operations on raster, and further raster-specific modifi-
cations. These query capabilities are currently far beyond the capabilities of GeoSPARQL
1.1 and might be tackled in further development.
2.8. Shacl Shapes for Graph Validation
Since the adoption of SHACL [
23
] as the recommended way to represent constraints
on RDF graphs by W3C, the semantic web community has created shapes for various
knowledge domains. These shapes fulfill different purposes, from checking the graph struc-
ture for consistency to checking the contents of the graph. Included with GeoSPARQL 1.1,
there are SHACL shapes that validate the graph structure as defined in GeoSPARQL 1.1 in
the following aspects:
1.
Encouragement of a unified Geometry instance structure: GeoSPARQL 1.1 encourages
geo:Geometry instances to only link to one serialisation. The intention behind this rule
is that not all Geometry serialisations that GeoSPARQL 1.1 supports are 1:1 convertible.
Users are still free to use more than one serialisation attached to a Geometry but should
be warned about the fact that serialisations may not be 100% equivalent. A simple
example of this non-equivalence can be seen when a geometry is associated with
a WKT literal in a non CRS84 coordinate reference system and a GeoJSON literal.
Because of a limitation of the GeoJSON literal to only accept one coordinate reference
system, the literal values of these to literals cannot be equivalent.
2.
Rudimentary checks of literal contents: Geometry literal contents are checked for plausi-
bility. These checks do not contain the parsing of geometry literals and its validation
but aim to check whether the contents of the geometry literal seem to be correct
according to its literal type.
3.
Correct usage of GeoSPARQL classes: Several SHACL shapes test the proper usage of
GeoSPARQL classes. In particular, SpatialObjectCollections are expected to have at
least one member relation, and geo:Feature instances are expected to be associated to
at least one geo:Geometry instance, whereas each Geometry instance is expected to
relate to at least one Geometry serialisation.
4.
Geometry property consistency: Further SHACL shapes test for the consistency of values
and cardinality of properties of a geo:Feature or geo:Geometry. For example, one
SHACL shape tests the consistency of dimensionality properties of a geo:Geometry.
For the scope of the standard, GeoSPARQL 1.1 stops at the definition of the aforemen-
tioned SHACL shapes. However, the SWG has identified the need for various research
communities to define checks of geometry relations and further consistency checks of Geom-
etry contents and their relations to each other. Independently of the efforts of the SWG [
32
]
introduced GeoSHACL for exactly this purpose and the SWG intends to found a commu-
nity group for the collection of further useful validation shapes. Shapes will be created
in a new Github Repository (https://github.com/opengeospatial/ogc-geosparql-shapes,
accessed on 30 October 2021).
We illustrate the usefulness of the defined GeoSPARQL 1.1 SHACL shapes using a
minimum example in Listing 11.
ISPRS Int. J. Geo-Inf. 2022,11, 117 18 of 30
Listing 11.
SHACL shape validation case: A geometry with a wrong literal type, two serializations,
a missing connection to its corresponding feature and a non-connected FeatureCollection.
ex:electorateCollection rdf:type geo:FeatureCollection .
ex:brisbane
ageo:Feature ;
rdfs:label "Brisbane Electorate Centroid" ;
.
ex:brisbane_geom
ageo:Geometry ;
geo:asGeoJSON
"""{
"type":"Point",
"coordinates":[153.030431, -27.438943]
}"""^^xsd:string ;
geo:asWKT """POINT(153.030431 -27.438943)"""^^geo:wktLiteral ;
geo:asKML """POINT(153.030431 -27.438943)"""^^geo:kmlLiteral ;
.
The example shows possible common mistakes in GeoSPARQL graphs with respect
to the aspects mentioned previously. Each mistake cannot be detected using OWL reason-
ing approaches. While an empty
geo:FeatureCollection
and a non-connected feature
instance are not necessarily wrong instances in a graph, we deem these as useless; hence
they produce violations in a SHACL validation. Compared to this, using the wrong literal
type (xsd:string) or a wrong literal content (geo:kmlLiteral for WKT content) is a clear
error in applying the standard specification. The last issue, representing two serialisations
connected to a geometry, is something noteworthy. Either the author of the given data is
aware that geometries in different serialisations are not necessarily equivalent as precisions
and CRS conversions might differ, or the author should consider creating different instances
of geometry that can represent different aspects of geospatial data quality.
2.9. JSON-LD Contexts
GeoSPARQL 1.1 provides JSON-LD [
33
] contexts for the Simple Features and
GeoSPARQL vocabularies allowing for the publication of simpler representations of
GeoSPARQL data. Listing 3provides example data as context-less JSON, which can
still be interpreted as RDF through its linking to the GeoSPARQL 1.1 JSON-LD context
resource. Context-less JSON is simpler than the more verbose JSON-LD for systems
to produce and for developers to understand. Presentation of a JSON-LD context for
GeoSPARQL is also aimed at assisting implementors of APIs that wish to present both
Linked Data API and OGC API Features [
26
] interfaces: the context-less JSON will be
far easier to incorporate into both required specifications’ outputs. Tests of such sys-
tems are in development (see (https://github.com/surroundaustralia/ogcldapi, accessed
on 30 October 2021) for a combination of a Linked Data and OGC API framework and
(http://floods.surroundaustralia.com, accessed on 30 October 2021) for an instance of its
use. This system plans to move its RDF payloads to context-less JSON).
2.10. Requirements and Conformance Class Vocabulary
As the OGC mandates, all GeoSPARQL requirements and conformance classes are
described using a URI. However, in the GeoSPARQL 1.1 specification, these unique identi-
fiers are also modeled in RDF as part of the standard’s profile specification. This allows the
combination of said vocabularies with different other RDF resources.
To date, we can see the usefulness of this design in two cases:
ISPRS Int. J. Geo-Inf. 2022,11, 117 19 of 30
2.10.1. Compliance Benchmarking
Good practices of standards of any kind are that they are first defined and then
implemented in reference implementations. To test whether the reference implementation
and all following implementations fulfil the criteria that the given standard sets, compliance
benchmarking can be used. [
13
] created the first compliance benchmark for GeoSPARQL
1.0 using the HOBBIT benchmarking platform [
34
]. Once an execution of the GeoSPARQL
compliance benchmark is finished, it may produce a benchmark result in RDF (https:
//github.com/hobbit-project/platform/issues/531, accessed on 30 October 2021). Since
the definition of the GeoSPARQL 1.1 requirements and conformance class vocabulary, these
results can now be related to the actual definitions of GeoSPARQL requirements in RDF as
shown in Figure 6.
Figure 6.
Requirements vocabulary applied to benchmark results of the GeoSPARQL compliance
benchmark applied in the HOBBIT platform. Benchmark results can be mapped to RDF concepts
representing requirements and conformance classes of the to-be-tested specification.
The provision of benchmark results connected to requirements of a standard in RDF
makes these results accessible as a machine-readable resource.
2.10.2. Partial Data Conformance Claims
In addition to systems claiming to implement GeoSPARQL functions, data may claim
conformance to GeoSPARQL’s ontology. Such claims may be tested both with RDF rea-
soners and also with the use of the SHACL validators that GeoSPARQL 1.1 provides (see
Section 2.8). Since GeoSPARQL contains many parts, useful data may be created that is
conformant to only part of GeoSPARQL, and the vocabulary of conformance classes allows
for the indication of conformance of data to parts of GeoSPARQL, not just the whole.
Additionally, particular GeoSPARQL user communities might create more constrained
SHACL validators to ensure that GeoSPARQL data conforms to a particular implemen-
tation pattern from a set of possible patterns. One example is the pattern whereby each
geo:Feature
is only associated with at most one
geo:Geometry
. In this case, a commu-
nity could define additional conformance classes, like those in GeoSPARQL, and indi-
cate data conformance to them too. Listing 12 shows a custom validator enforcing a
restriction on GeoSPARQL 1.1 Features: that they must have one and only one GeoJSON
geometry serialization.
ISPRS Int. J. Geo-Inf. 2022,11, 117 20 of 30
Listing 12.
An custom GeoSPARQL Feature validator ensuring all Features have only one GeoJSON
geometry representation.
<hasGeometry-hasSerialization-specialized>
ash:NodeShape ;
sh:targetObjectsOf geo:hasGeometry ;
sh:property [
ash:PropertyShape ;
sh:path geo:asGeoJSON ;
sh:minCount 1 ;
sh:maxCount 1 ;
sh:message "Each node with an incoming geo:hasGeometry,
or aspecialization of it, must have one and
only one outgoing relation of geo:asGeoJSON"@en ;
] ;
.
3. Reference Implementations
This section describes reference implementations that implement the GeoSPARQL 1.1
specification either entirely or to a certain extent.
3.1. RDFLib DGGS
The rHEALPix DGGS Simple Feature functions Python package [
35
] is a library of
functions built in mid-2021 to demonstrate that DGGS geometries within the rHealPix
DGGS family [
36
], of which AusPIX is a member, could be used in the calculation of Simple
Features topological relations. Using these functions and RDF and SPARQL capability
from the RDFlib (RDFlib is a widely-used, open source, Python programming language,
RDF manipulation toolkit: https://github.com/RDFLib/rdflib, accessed on 30 October
2021) Python package, the RDFlib GeoSPARQL Functions for DGGS [
37
] was then built that
exposes DGGS geometry-based Simple Features calculation functions to RDFlib’s SPARQL
interpreter via SPARQL extension functions. Listing 13 shows Python application code
demonstrating the use of RDFlib GeoSPARQL Functions for DGGS with some AusPIX data.
At the time of writing, all Simple Features topological relations functions were imple-
mented except for
sfCrosses
, but this is expected to be implemented soon: this appears
to be held up by programming issues only, not theoretical issues. While only the rHealPix
DGGS family has currently been catered for, there appears to be no theoretical reason why
other DGGSes, such as H3 (https://eng.uber.com/h3/, accessed on 30 October 2021) could
not also be catered for.
3.2. GeoSPARQL-Jena
The GeoSPARQL implementation of the Apache Jena software library GeoSPARQL-
Jena [
38
] provides, according to recent benchmarks [
13
], the only complete implementation
of the GeoSPARQL 1.0 specification. In addition, GeoSPARQL-Jena has been extended
in a prototypical use case to support raster data in [
39
]. This implementation featured
prototypical raster support in GeoSPARQL-Jena and aimed at implementing a variety of
functions defined in the Simple Features implementation standard. Work is underway by
members of the SWG to implement DGGS topological relations functions in Jena in a mirror
implementation to that in RDFlib described above. A combination of the DGGS algorithms
from the RDFlib implementation and the raster handling from [
39
] will likely provide
the first implementation of an RDF library and accompanying triple store to provide full
support of the GeoSPARQL 1.1 specification within Jena.
3.3. SPARQLing Unicorn QGIS Plugin
Another implementation already making use of the GeoSPARQL 1.1 specification
is the SPARQLing Unicorn QGIS Plugin [
40
]. This plugin is, to the authors’ knowledge,
ISPRS Int. J. Geo-Inf. 2022,11, 117 21 of 30
the only client library to make geospatial vector data accessible as vector layers in the
popular, open source, QGIS desktop GIS software (https://qgis.org/en/site/, accessed on
30 October 2021). The plugin aims to provide three main functions: 1. Querying Linked
Data; 2. preparing geodata for publication as Linked Data resources; and 3. the enrichment
of geospatial data from Linked Data resources.
Listing 13.
Python programming code showing the use of the RDFlib GeoSPARQL Functions for DGGS.
After [37].
# i m po r t e l em e nt s fr o m R D Fl ib a nd t hi s p ac k ag e ( g s dg g s )
from rdflib import Literal , Gr a ph , Namespa c e , U R IRe f
from gsdggs import~DGGS
EX =N a me s pa c e (" h t tp : // e x am p l e . co m / " )
GEO =N am e sp ac e ( " ht t p : // w w w . op e ng i s . n et / o nt / g e os p a rq l # " )
# De f ine the DGGS Ge o m e t ri es , add them to an in - memo r y R D F
graph
g=G ra ph ( )
g . ad d ( (
URIRef(’ h tt p s : // g eo m - a ’ ) ,
G EO . a sD G GS ,
Literal(’ C E LLLI S T ( ( R0 R10 R13 R16 R30 R31 R32 R40 ) ) ’ , EX .
a us P ix L it er a l )) )
g . ad d ( (
URIRef(’ h tt p s : // g eo m - b ’ ) ,
G EO . a sD G GS ,
Literal(’ C E LLLI S T ( ( R06 R07 R30 R31 )) ’ , E X. a u sP i xL i te r al ) )
)
g . ad d ( (
URIRef(’ h tt p s : // g eo m - c ’ ) ,
G EO . a sD G GS ,
Literal(’ C E LLLI S T ( ( R11 R12 R14 R15 )) ’ , E X. a u sP i xL i te r al ) )
)
# Q u er y t h e in - m e mo r y g r ap h
q="""
P RE F IX g eo : < h tt p : // w w w . op e n gi s . n et / o nt / g e os p a rq l # >
P RE FI X d gg s : < ht t ps : // p l ac e ho l de r . co m / dg g sf u nc s / >
SELE C T ? a ? b
WHERE {
? a g eo : a s DG G S ? a _ ge o m .
? b g eo : a s DG G S ? b _ ge o m ~ .
F IL T ER d gg s : s fW i th i n ( ? a_ ge om , ? b _ ge o m )
}"""
# Int e r a te t h r ough a n d p r i n t r esul t s
for rin g . q ue r y (q ) :
p ri nt (f"{r [ ’a ’ ]}is within {r[’b ’]}")
To extend the query capabilities of the plugin for GeoSPARQL 1.1, support for
KML and GeoJSON literals has been added, as has support for the processing and re-
view of geo:FeatureCollection and geo:GeometryCollection instances (cf. Figure 7) in a
given triplestore.
ISPRS Int. J. Geo-Inf. 2022,11, 117 22 of 30
As an aspect of data processing and integration, the plugin implements the newly
defined literal formats in its Linked Data conversion dialogue (cf. Figure 8). This dialogue
allows converting geometry literals in Linked Data to other CRS supported by QGIS or to
other geometry literal formats within the specifications of GeoSPARQL 1.1, as mentioned
in Section 2.4.
With this implementation, the general public can discover, access, prepare and convert
GeoSPARQL1.1-prepared data.
Figure 7.
The SPARQL Unicorn QGIS Plugin extended with support for FeatureCollections and
GeometryCollections as defined in GeoSPARQL 1.1.
Figure 8.
In-development conversion dialogue for geospatial Linked Data files aiming at full
GeoSPARQL 1.1 literal conversion support.
ISPRS Int. J. Geo-Inf. 2022,11, 117 23 of 30
4. Examples of Usage of GeoSPARQL 1.1
In this section, we illustrate the use of the new parts of GeoSPARQL 1.1.
4.1. Profile Declaration
The Australian government is currently conducting a project defining a “National
Data Exchange Standard” (NDES) for biodiversity data, validators which are used in
an online Application Programming Interface (API) system (The API is online in test
form at http://ndesgateway.surroundaustralia.com/, accessed on 30 October 2021. This
location will likely remain live until July 2022, at which point it will move to a long-term
departmental web location). The system obtains the multiple validators required for use
from the many standards that the NDES profiles, including GeoSPARQL, via Linked Data
link-following methods which require that standards are made available online in machine-
readable form (RDF), with RDF predicates linking to any resources with the role validator
that they define and also that standards are linked together forming a dependency-based
profile hierarchy. With such information online, the NDES API is able to recurse through
the profile hierarchy, retrieve all defined validation resources and then compound them
for use automatically, saving system update and maintenance time if/when standards
update validators.
Even in the current draft form, the NDES system polls the GeoSPARQL 1.1 publication
for validators. Polling has proved useful to retrieve the latest versions of the validators as
the SWG has continued to develop them.
4.2. Use of New Geometry Formats
The Australian government’s “National Map” (https://nationalmap.gov.au/, accessed
on 30 October 2021) is a web-based globe that can display spatial data in a number of
formats but none that GeoSPARQL supported until this 1.1 version. Now, with GeoJSON
geometry literal support, triplestores can be used to store and filter spatial data, which
the globe can now easily consume and display. A triplestore/Globe implementation is
now operational (https://globe.surroundaustralia.com/, accessed on 30 October 2021)
that reads data from a triplestore, within which the geometries of features are stored as
GeoJSON. With geometries in that format, only very simple translations of a few RDF
properties to JSON for
geo:Feature
instances are necessary for the Globe can display
feature metadata, such as labels, as well as the geometries. Figure 9shows an instance
of the Globe listing test polygons for the Australian state of Queensland as well as three
features it contains.
With the Globe’s triplestore also containing Simple Feature relations, it is also now
possible to show feature-to-feature links within the globe instance, as shown in Figure 9.
These links are actionable, and the globe can refocus on features navigated to.
4.3. OGC API Features Backend
Traditional spatial data infrastructures are transitioning from being providers of spatial
data via specified web services only to becoming spatial knowledge infrastructures [
41
,
42
].
These spatial knowledge infrastructures will provide spatial data in two ways, as illus-
trated in Figure 10. The first way is the provision of spatial data using geospatial web
services. The second way is the provision of spatial data as Linked Data, the latter being
enabled by ontologies such as GeoSPARQL. However, spatial web services are currently
being reworked in the OGC API Features specification, which, as elaborated previously in
Section 2.2.4, allows for Linked Data backends.
A live example of data delivered according to Linked Data principles, the OGC API
Features specification, and also a SPARQL Protocol (https://www.w3.org/TR/sparql1
1-protocol/, accessed on 30 October 2021) service is Geoscience Australia’s Floods API
(http://floods.surroundaustralia.com/, accessed on 30 October 2021), a screenshot of which
is shown in Figure 11. Through an application of the CQL to SPARQL mappings that the
SWG is creating (see Section 2.7), the Floods API will also be able to respond to CQL queries.
ISPRS Int. J. Geo-Inf. 2022,11, 117 24 of 30
This API supplies information according to the required OGC API Features URL struc-
tures with very simple queries to a GeoSPARQL 1.1. backend. The simplicity is possible due
to GeoSPARQL 1.1’s modelling which allows for
geo:FeatureCollection
,
geo:Feature
and
geo:Geometry
instances, the first of which as introduced in GeoSPARQL 1.1 to specifi-
cally cater for APIs like the OGC API Features.
Figure 9.
A development version of Australia’s "National Map" software showing data sourced
from a GeoSPARQL 1.1 RDF dataset. The dialog box on the map interface shows features related to
the ‘Queensland’ feature via Simple Features relations such as
geo:sfWithin
that the National Map
software has previously not been able to show.
Figure 10.
Excerpt of some components of a spatial knowledge infrastructure. A triplestore with
GeoSPARQL 1.1. support is provided as a data backend for an OGC API Features powered services
infrastructure used by traditional GIS clients.
ISPRS Int. J. Geo-Inf. 2022,11, 117 25 of 30
Figure 11.
Screenshot of a Linked Data API run by Geoscience Australia that delivers flooded area
data online in HTML and RDF forms from a GeoSPARQL 1.1 backend. The API is also an OGC API
Features-compatible API since the RDF is converted to (Geo)JSON as needed.
APIs such as this Floods API act similarly to previous generation spatial data APIs,
such as the well-known Web Feature Service (WFS) (https://www.ogc.org/standards/wfs,
accessed on 30 October 2021) from which OGC API Features derives but are also able to
present much simpler human User Interfaces (web page), SPARQL endpoints and more
data formats.
4.4. DGGS Application Example
DGGSes represent both vector and raster spatial data as collections of cell IDs. Since
GeoSPARQL 1.1, the storage system for DGGS data may be a triplestore.
Data for the API in Figure 11 are initially recorded as rasters, and data for the Australian
Statistical Geographies Standard (ASGS) (https://www.abs.gov.au/websitedbs/D3310114
.nsf/home/Australian+Statistical+Geography+Standard+(ASGS), accessed on 30 October
2021) dataset is recorded in vector form. Both datasets are able to be stored using the
Apache Jena TDB triplestore (https://jena.apache.org/documentation/tdb/index.html,
accessed on 30 October 2021) as GeosPARQL 1.1 data with geometries in the AusPIX
DGGS format.
The Floods & ASGS datasets are actually stored in the Loc-I for Disaster Recovery
project’s (https://ldr.surroundaustralia.com/, accessed on 30 October 2021) Data Platform
with several other rasters and vector datasets, all of which can be cross-queried and
presented as either features with geometries in vector form, features with geometries in
ISPRS Int. J. Geo-Inf. 2022,11, 117 26 of 30
raster form or as cells within regular grids with cell values of data or the IDs of their
corresponding features.
The Loc-I platform does require up-front geometry data conversion to DGGS and
feature information to GeoSPARQL RDF but then use of the spatial and non-spatial data is
extremely simple–SPARQL queries–and all elements of the data can be used in one system.
5. Future Work
5.1. GeoSPARQL 1.1 Finalization
GeoSPARQL 1.1 is in the final stages of drafting as of November 2021. The new
elements of the version, with one possible exception detailed below, have been finalised
and the major remaining tasks are to:
• Send the new version to system implementors for wider review.
• Respond to implementors’ feedback.
• Register the new IRIs within version 1.1 with the OGC Naming Authority.
• Initiate the mandatory OGC standard update notification period.
Given the simple nature of most of version 1.1’s additions and the fact that SWG
members have been able to create at least one implementation of almost all new ontology
elements and functions, it is expected that the implementors’ wider review will not result
in major changes.
5.2. Work beyond GeoSPARQL 1.1
The original intention of the GeoSPARQL SWG, when formed in 2019, was to publish
a 1.1 version of GeoSPARQL, and then possibly a 1.2 and a 2.0, as described in Section 1.
While the scope of GeoSPARQL 1.1 has been in keeping with the original estimates for that
version and there are still known un-tackled change requests that the SWG has tagged for a
possible 1.2 version (see https://github.com/opengeospatial/ogc-geosparql/milestone/2,
accessed on 30 October 2021), the SWG has entertained many thoughts about a more
comprehensive tackling of spatial Semantic Web concerns that might require a different
direction altogether, for example, non-earth geometries, comprehensive handling of rasters
(see Issue 18) or a whole new ontological handling of Coordinate Reference Systems.
If there is a desire for much non-geo work, and if it extends beyond the SPARQL query
language, it could be that neither the ‘Geo-’ or the ‘-SPARQL’ parts of GeoSPARQL will
sensibly apply to it, thus something other than a GeoSPARQL 1.2 or even a 2.0 may be
required. The SWG will consult on this matter after GeoSPARQL 1.1’s release.
The following subsections provide some more detail on specific GeoSPARQL 1.1+
potential directions.
5.3. Inclusion of Further Spatial Data Types
For now, GeoSPARQL 1.1 is a standard to describe 2D and 3D vector data and a single
grid reference system. However, members of the SWG have already hinted at the need to
represent mobile (e.g., 3D Meshes) and further immobile spatial objects and the inclusion
of raster data query capabilities.
5.4. Geometry Roles
As noted in Section 2.2.1, new ontology elements were proposed for GeoSPARQL 1.1
to represent the roles of geometries with respect to features. SWG members saw new prop-
erties specialising
geo:hasGeometry
, such as
geo:hasCentroid
and
geo:hasBoundingBox
,
as indicating geometries with particular roles and that GeoSPARQL could perhaps allow
for an extensible way to indicate role by creating a vocabulary of them, which could be
added to, rather than describing them in a number of properties that must remain fixed
after a GeoSPARQL version’s publication.
Timing and SWG participant effort did not allow for roles to be added in GeoSPARQL 1.1,
and their addition may be sensible for a GeoSPARQL 1.2.
ISPRS Int. J. Geo-Inf. 2022,11, 117 27 of 30
5.5. Interoperability with Buildings Data
There is a growing appetite for Semantic Web data within the building information
modelling (BIM) communities, evidenced by the existence of working groups like the W3C
Linked Building Data Community Group (https://www.w3.org/community/lbd/, ac-
cessed on 30 October 2021), and the existence of projects such as BRICK [
43
], IFC/Ontology
mappings [
44
] and the “Building Topology Ontology” (https://w3id.org/bot, accessed
on 30 October 2021). These all naturally have spatial components and some already use
GeoSPARQL 1.0 (BRICK). It is also clear, evidenced by the fact that members of the Linked
Building Data Community Group worked with members of the SWG to outline building-
related use cases and some shortcomings of GeoSPARQL 1.0 for their purposes in a ‘White
Paper’ in preparation for the formation of the SWG [
17
], that GeoSPARQL has long been
considered important to that community. Unfortunately, not all of this community’s needs
were met in GeoSPARQL 1.1, so more could be done to support it, for example, the inclu-
sion of other, specialised, topological relations for voids and non-void features; geometry
roles (as per the Section above); non-coordinate geometry characterisations, for example,
cylinders; and sub-surface geometry handling.
5.6. Formalisation of Spatial Reference Systems
While it is currently possible to use spatial reference system definitions in literal
descriptions, spatial reference system definitions have not been completely formalised
using an ontology model as of today. This can be a problem in many ways. For example,
a triplestore might store a geometry encoded in a coordinate reference system not registered
in a well-known repository such as the EPSG Geodetic Parameter Dataset. In these cases,
the hosting triplestore might provide custom support for this coordinate reference system,
but this support ends once the geospatial data is queried in a federated query scenario. A fu-
ture version of GeoSPARQL, or a successor to the standard, should be able to describe the
contents of spatial reference systems so that the user can make informed decisions about the
appropriateness of the spatial reference system assigned to a given geometry that federated
queries may resolve even previously not known coordinate reference system definitions.
5.7. Linked Data Fragments Support
GeoSPARQL data collections can be very large, either regarding the number of features
or geometries stored, the size of their geometry literals, or both. APIs wishing to deliver
large numbers of GeoSPARQL Feature or geometry instances would benefit from the
ability to deliver them in a streaming manner, and for this, the so-called “Linked Data
Fragments” (LDF) [
45
] approach has been considered. It appears that the LDF approach
if implemented to stream data from an API, would allow for client-side GeoSPARQL
topological querying. An example scenario could be that an API user wishes to know if
an API contains a feature that overlaps a locally held feature. If the API were to stream
features from a
geo:FeatureCollection
or as a result of a filtering query, the user could
incrementally test for an overlap and cease the streaming session when a match is found.
6. Conclusions
A staged schedule of updates to this essential Semantic Web spatial standard has been
initiated with simple and strictly backward-compatible changes now in GeoSPARQL 1.1.
Reference implementations for all of GeoSPARQL 1.1’s new features have been made. Ex-
amples of all elements used can be indicated online. Features discussed for GeoSPARQL 1.2
include the formalisation of coordinate reference systems in RDF, the depiction of accuracies
and level of detail, and the addition of further–possibly also binary–literal types. Work on
GeoSPARQL 1.2 will start at the earliest in mid-2022. GeoSPARQL 2.0, as yet unspecified,
is likely to introduce more substantial changes to the standard. Changes proposed for
GeoSPARQL 2.0 include broadening the scope of GeoSPARQL to include further kinds of
spatial data. To that end, full-featured support for 3D geometries and support for coverages
are discussed on the level of data representations. These proposals are related to some
ISPRS Int. J. Geo-Inf. 2022,11, 117 28 of 30
growing interest in the semantic web community in representing further geospatial data
related to building information [
46
] and coverage data [
39
]. More requirements might also
be introduced once feedback has been received from the GeoSPARQL 1.1 and 1.2 releases.
Author Contributions:
The two authors of this paper, Nicholas J. Car and Timo Homburg contributed
equally to this paper. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
Example data indicated in this paper is all already freely available
online, linked to at its places of mention. All of the GeoSPARQL 1.1 official example data is stored
in the version’s repository, see (https://github.com/opengeospatial/ogc-geosparql, accessed on 30
October 2021).
Acknowledgments:
We thank the individual members of the GeoSPARQL Standards Working
Group for the work to update GeoSPARQL for this version 1.1. It was a larger task than initially
anticipated, and the SWG rose to that larger occasion. In particular, Joseph Abhayaratna (SWG chair),
Matthew Perry (one of the original GeoSPARQL editors), Simon J.D Cox, and Paul Cripps. We also
thank non-SWG member contributors to the standard, particularly Frans Knibbe, Mathias Bonduel,
and Robert Gibb. Finally, we wish to thank the Open Geospatial Consortium, both the organisation
and their staff, for assisting with this important standards work.
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations
The following abbreviations are used in this manuscript:
API Application Programming Interface
BIM Building Information Modeling
CQL Common Query Language
CRS Coordinate Reference System
DGGS Discrete Global Grid System
EPSG European Petroleum Survey Group
GeoSPARQL Geographic SPARQL Protocol Furthermore, RDF Query Language
GIS Geographic Information System
GML Geography Markup Language
HTML Hypertext Markup Language
KML Keyhole Markup Language
JSON JavaScript Object Notation
LDF Linked Data Fragments
NDES National Data Exchange Standard
OGC Open Geospatial Consortium
OWL Web Ontology Language
QGIS Quantum GIS
RDF Resource Description Framework
RDFS Resource Description Framework Schema
RIF Rule Interchange Format
SDWWG Spatial Data On The Web Working Group
SHACL Shapes Constraint Language
SKOS Simple Knowledge Organization System
SOSA Semantic Sensor Network Ontology
SPARQL SPARQL Protocol Furthermore, RDF Query Language
SRS Spatial Reference System
SWG Standard Working Group
W3C World Wide Web Consortium
WKT Well-Known Text
XML Extensible Markup Language
ISPRS Int. J. Geo-Inf. 2022,11, 117 29 of 30
Appendix A. GeoSPARQL 1.0 and 1.1 Topological Relations
GeoSPARQL 1.0 contains three types of topological relations. Simple Features Re-
lations, Egenhofer Relations, and relations derived from the specification of the Region
Connection Calculus (RCC8) [47].
Table A1.
GeoSPARQL Topological Relations as defined in the GeoSPARQL 1.0 standard. The three columns
reflect the order of standardization and not the order of relation between the given properties.
Simple Features Egenhofer Relations Region Connection Calculus (RCC8)
geo:sfEquals geo:ehEquals geo:rcc8eq
geo:sfDisjoint geo:ehDisjoint geo:rcc8dc
geo:sfIntersects geo:ehMeet geo:rcc8ec
geo:sfTouches geo:ehOverlap geo:rcc8po
geo:sfCrosses geo:ehCovered geo:rcc8tppi
geo:sfWithin geo:ehCoveredBy geo:rcc8tpp
geo:sfContains geo:ehInside geo:rcc8ntpp
geo:sfOverlaps geo:ehContains geo:rcc8ntppi
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