Challenges for Qualitative Spatial Reasoning in Linked Geospatial Data (BASR-2011)

Challenges for Qualitative Spatial Reasoning in Linked Geospatial Data
Manolis Koubarakis and Kostis Kyzirakos and Manos Karpathiotakis and
Charalampos Nikolaou and Michael Sioutis and Stavros Vassos
Dept. of Informatics and Telecommunications, National and Kapodistrian University of Athens
Dimitrios Michail
Dept. of Informatics and Telematics, Harokopio University of Athens
Themistoklis Herekakis and Charalampos Kontoes and Ioannis Papoutsis
Inst. for Space Applications and Remote Sensing, National Observatory of Athens
{koubarak,kkyzir,mk,charnik,sioutis,stavrosv}@di.uoa.gr
{therekak,kontoes,ipapoutsis}@space.noa.gr
{michail}@hua.gr
Abstract
Linked geospatial data has recently received atten-
tion, as researchers and practitioners have started
tapping the wealth of geospatial information avail-
able on the Web. We discuss some core research
problems that arise when querying linked geospa-
tial data, and explain why these are relevant for the
qualitative spatial reasoning community. The prob-
lems are presented in the context of our recent work
on the models stRDF and stSPARQL and their ex-
tensions with indefinite geospatial information.
1 Introduction
Linked data is a new research area which studies how one can
make RDF data available on the Web, and interconnect it with
other data with the aim of increasing its value for everybody
[Bizer et al., 2009]. The resulting “Web of data” has recently
started been populated with geospatial data. A representa-
tive example of such efforts is LinkedGeoData1 where Open-
StreetMap data are made available as RDF and queried using
the declarative query language SPARQL [Auer et al., 2009].
With the recent emphasis on open government data, some of
it encoded already in RDF2, portals such as LinkedGeoData
demonstrate that the development of useful Web applications
might be just a few SPARQL queries away.
We have recently developed stSPARQL, an extension of
the query language SPARQL for querying linked geospa-
tial data [Koubarakis and Kyzirakos, 2010]3. stSPARQL has
been fully implemented and it is currently being used to query
1http://linkedgeodata.org/
2http://data.gov.uk/linked-data/
3The paper [Koubarakis and Kyzirakos, 2010] presents the lan-
guage stSPARQL that also enables the querying of valid times of
triples. Here, we omit time and discuss only the geospatial subset of
stSPARQL.
linked data describing sensors in the context of project Sem-
sorGrid4Env4 [Kyzirakos et al., 2010] and linked earth obser-
vation (EO) data in the context of project TELEIOS5.
In the context of TELEIOS we are developing a Virtual Ob-
servatory infrastructure for EO data. One of the applications
of TELEIOS is fire monitoring and management led by the
National Observatory of Athens (NOA). This application fo-
cuses on the development of techniques for real time hotspot
and active fire front detection, and burnt area mapping. Tech-
nological solutions to both of these cases require the integra-
tion of multiple, heterogeneous data sources, some of them
available on the Web, with data of varying quality and vary-
ing temporal and spatial scales.
In this paper we show how well-known approaches to qual-
itative spatial representation and reasoning [Renz and Nebel,
2007] can be used to represent and query linked geospatial
data using RDF and stSPARQL. Thus, we propose linked
geospatial data as an interesting application area of qualita-
tive spatial reasoning techniques, and discuss open problems
that might be of interest to the qualitative spatial reasoning
community. In particular, we address the problem of repre-
senting and querying indefinite geospatial information, and
discuss the approach we adopt in TELEIOS.
The organization of the paper is as follows. Section 2 in-
troduces the kinds of linked geospatial data that we need to
represent in the NOA application of TELEIOS, shows how to
represent it in stRDF, and presents some typical stSPARQL
queries. Then, Section 3 shows how the introduction of qual-
itative spatial information in the stRDF data model enables
us to deal with the NOA application more accurately. The
same section introduces the new model stRDFi which al-
lows qualitative spatial information to be expressed in RDF
and gives examples of interesting queries in the new model.
In Section 4 we proceed to discuss some open problems in
the stRDFi framework that require new contributions by the
4http://www.semsorgrid4env.eu/
5http://www.earthobservatory.eu/

qualitative spatial reasoning community. Finally, in Section 5
we discuss related work and in Section 6 we draw conclu-
sions.
The paper is mostly informal and uses examples from the
NOA application of TELEIOS. Even in the places where the
paper becomes formal, we do not give any detailed tech-
nical results for which the interested reader is directed to
[Koubarakis et al., 2011].
2 Linked geospatial data in the NOA
application
The NOA application of TELEIOS concentrates on the devel-
opment of solutions for real time hotspot and active fire front
detection, and burnt area mapping. Technological solutions
to both of these cases require integration of multiple, hetero-
geneous data sources with data of varying quality and vary-
ing temporal and spatial scales. Some of the data sources are
streams (e.g., streams of EO images) while others are static
geo-information layers (e.g., land use/land cover maps) pro-
viding additional evidence on the underlying characteristics
of the affected area.
2.1 Datasets
The following datasets are available in the NOA application:
• Hotspot maps. NOA operates an MSG/SEVIRI6 acqui-
sition station and receives raw satellite images every 15
minutes. These images are processed with image pro-
cessing algorithms to detect the existence of hotspots.
The information related to hotspots is stored in ESRI
shapefiles and KML files. These files hold informa-
tion about the date and time of image acquisition, carto-
graphic X, Y coordinates of detected fire locations, the
level of reliability in the observations, the fire radiative
power assessed, and the observed fire area. NOA re-
ceives similar hotspot shapefiles covering the geograph-
ical area of Greece from the European project SAFER
(Services and Applications for Emergency Response).
• Burnt area maps. From project SAFER, NOA also
receives ready-to-use accumulated burnt area mapping
products in polygon format, projected to the EGSA87
reference system7. These products are derived daily us-
ing the MODIS satellite and cover the entire Greek ter-
ritory. The data formats are ESRI shapefiles and KML
files with information relating to date and time of image
acquisition, and the mapped fire area.
• Corine Land Cover data. The Corine Land Cover
project is an activity of the European Environment
Agency which is collecting data regarding land cover
(e.g., farmland, forest) of European countries. The
Corine Land Cover nomenclature uses a hierarchical
scheme with three levels to describe land cover:
6MSG refers to Meteosat Second Generation satellites, and SE-
VIRI is the instrument which is responsible for taking infrared im-
ages of the earth.
7EGSA87 is a 2-dimensional projected coordinate reference sys-
tem that describes the area of Greece.
Figure 1: An example of hotspots and burnt area mapping
products in the region of Attiki, Greece
– The first level consists of five items and indicates
the major categories of land cover on the planet,
e.g., forests and semi-natural areas.
– The second level consists of fifteen items and
is intended for use on scales of 1:500,000 and
1:1,000,000 identifying more specific types of land
cover, e.g., open spaces with little or no vegetation.
– The third level consists of forty-four items and is
intended for use on a scale of 1:100,000, narrow-
ing down the land use to a very specific geographic
characterization, e.g., burnt areas.
The land cover of Greece is available as an ESRI shape-
file that is based on the Corine Land Cover nomencla-
ture.
• Coastline geometry of Greece. An ESRI shapefile that
describes the geometry of the coastline of Greece is
available.
Figure 1 presents an example of hotspots and burnt area
mapping products, as viewed when layered together over a
map of Greece.
2.2 Using semantic web technology
An important challenge in the context of TELEIOS is to de-
velop advanced semantics-based querying of the available
datasets along with linked data available on the web. This
is a necessary step in order to unlock the full potential of the
available datasets, as their correlation with the abundance of
data available in the web can offer significant added value.
As an introduction to Semantic Web technology, we present a
simple example that shows how burnt area data is expressed
in the language stRDF, and then proceed to illustrate some
interesting queries using the language stSPARQL.
Similar to RDF, in stRDF we can express information using
triples of URIs, literals, and blank nodes in the form “subject
predicate object”. Figure 2 shows four stRDF triples that
encode information related to the burnt area that is identified

ex:BurntArea_1 rdf:type noa:BurntArea.
ex:BurntArea_1 noa:hasID "1"ˆˆxsd:decimal.
ex:BurntArea_1 geo:geometry "POLYGON((
38.16 23.7, 38.18 23.7,
38.18 23.8, 38.16 23.8,
38.16 23.7));<http://spatialreference
.org/ref/epsg/4121/>"ˆˆstrdf:geometry.
ex:BurntArea_1 noa:hasArea
"23.7636"ˆˆxsd:double.
Figure 2: An example of a burnt area represented in stRDF
by the URI ex:BurntArea_1. The prefixes noa and ex
correspond to appropriate namespaces for the URIs that refer
to the NOA application and our running example, while xsd
and strdf correspond to the XML Schema namespace and
our stRDF namespace, respectively.
In stRDF the standard RDF model is extended with the
ability to represent geospatial data. In our latest version of
stRDF we opt for a practical solution that uses OGC stan-
dards to represent geospatial information. We introduce the
new data type strdf:geometry for modeling geometric
objects. The values of this datatype are typed literals that en-
code geometric objects using the OGC standard Well-known
Text (WKT) or Geographic Markup Language (GML). Liter-
als of this datatype are called spatial literals.
The third triple in Figure 2 shows the use of spatial lit-
erals to express the geometry of the burnt area in question.
This spatial literal specifies a polygon that has exactly one
exterior boundary and no holes. The exterior boundary is
serialized as a sequence of its vertices’ coordinates. These
coordinates are interpreted according to the GGRS87 geode-
tic coordinate reference system identified by the URI http:
//spatialreference.org/ref/epsg/4121/.
In the case of burnt area maps, these stRDF triples are
created by a procedure that processes the relevant shapefiles
and produces one stRDF triple for each property that refers
to a particular area. Although we are currently doing this
manually, in the future we plan to use automated tools as in
[Bla´zquez et al., 2010].
Figure 3 presents a query in stSPARQL that looks for all
the URIs of burnt areas that are located in Greece and cal-
culates their area. stSPARQL is an extension of SPARQL
in which variables may refer to spatial literals (e.g., variable
?BAGEO in ?BA geo:geometry ?BAGEO8). stSPARQL
provides functions that can be used in filter expressions to
express qualitative or quantitative spatial relations. For ex-
ample the function strdf:Contains is used in Figure 3
to encode the topological relation non-tangential proper part
inverse (NTPP−1) of RCC-8 [Cui et al., 1993].
In this query, linked data from DBpedia9 are used to iden-
tify those burnt areas that are located in Greece. DBpedia is
an RDF dataset consisting of the contents of Wikipedia that
allows you to link other data sets on the Web to Wikipedia
8We are assuming that DBpedia offers precise representations
of country geometries as values of the predicate geo:geometry.
This is not the case at the moment since these values are points cor-
responding to the bounds of a region located in the center of Greece.
9http://www.dbpedia.org/
select ?BA strdf:Area(?BA)
where {?BA rdf:type noa:BurntArea .
?BA geo:geometry ?BAGEO .
?C rdf:type noa:GeographicBound .
?C dbpedia:Country dbpedia:Greece .
?C geo:geometry ?CGEO .
filter(strdf:Contains(?CGEO,?BAGEO))}
Figure 3: An example of a query expressed in stSPARQL
select ?BA ?BAGEO
where {?R rdf:type noa:Region .
?R geo:geometry ?RGEO .
?R noa:hasCorineLandCoverUse ?F .
?F rdfs:subClassOf clc:Forests .
?CITY rdf:type dbpedia:City .
?CITY geo:geometry ?CGEO .
?BA rdf:type noa:BurntArea .
?BA geo:geometry ?BAGEO .
filter(strdf:Intersect(?RGEO,?BAGEO)&&
strdf:Distance(?BAGEO,?CGEO)<2)}
Figure 4: A more complex example of a query expressed in
stSPARQL
data. The result of this query is a list of URIs that may in-
clude ex:BurntArea_1 of Figure 2.
Figure 4 presents a more complex query in stSPARQL that
looks for all burnt areas that were classified as forests accord-
ing to the Corine Land Cover dataset. These areas must also
be located within 2km from a city. This query also uses linked
data from DBpedia to retrieve geospatial information about
cities.
3 Indefinite geospatial information in the
NOA use case
This section motivates our approach towards extending the
model stRDF with the ability to represent and query indefi-
nite qualitative spatial information. The new model is named
stRDFi where “i” stands for “indefinite”.
The infrared imager SEVIRI on board of the MSG satel-
lites has medium resolution, i.e., each image pixel represent-
ing a hotspot in the NOA shapefiles corresponds to a 3km
by 3km rectangle in geographic space. Thus, a precise rep-
resentation of the real world situation that corresponds to a
hotspot would be to state that there is a geographic region
with unknown exact coordinates where a fire is taking place,
and that region is included in a known 3km by 3km rect-
angle. This is captured by the following triples and con-
straints in stRDFi that introduce the hotspot, the fire cor-
responding to it and the region corresponding to the fire.
This region ( region1) is a new kind of literal, called an
unknown literal, which is asserted to be inside the polygon
defined by "POLYGON((24.81 35.32, 24.84 35.33,
24.84 35.30, 24.81 35.30, 24.81 35.32))".
noa:hotspot1 rdf:type noa:Hotspot .
noa:fire1 rdf:type noa:Fire .
noa:hotspot1 noa:correspondsTo noa:fire1 .
noa:fire1 noa:occuredIn _region1 .

_region1 strdf:NTPP "POLYGON((24.81 35.32,
24.84 35.33, 24.84 35.30, 24.81 35.30,
24.81 35.32));<http://spatialreference.
org/ref/epsg/4121/>"ˆˆstrdf:geometry.
Unknown literals are like existentially quantified variables
in first-order logic. By convention, identifiers for unknown
literals in stRDFi always start with an underscore. In the
above example, strdf:NTPP is the non-tangential proper
part relation of RCC-8.
The NOA fire monitoring activities include validating
hotspots, i.e., making sure that they do not correspond to false
alarms due to the medium resolution of the images, or fires
that are not of interest since they do not take place in forested
areas. Part of the validation activities of NOA include col-
lecting information about forest fires reported in the Greek
Press. Therefore, when fire noa:fire1 is validated, NOA
may want to annotate the relevant hotspot, validated fire and
burnt area with information from news sources available on
the Web that have reported the corresponding fire. Assuming
that Greek newspapers will soon follow the example of New
York Times and use tags to annotate news articles, articles re-
porting fire events may be tagged with the name of the admin-
istrative area in which the fire occurred and the word “fire”.
Then, it is easy to retrieve the geographical coordinates of
the place mentioned in the tag and, using standard geometric
methods, decide whether the location of the hotspot is near
that place.
Alternatively, using techniques from Geographic Informa-
tion Retrieval and Natural Language Processing [Schockaert
et al., 2008; Hoffart et al., 2010] one could harvest qualita-
tive spatial information from the Web. As an example, in-
formation related to noa:fire1 obtained from a regional
Greek newspaper available on the Web might say that “there
was a fire north of the village of Zoniana in the Prefecture
of Rethymno, Crete”. In this case NOA might choose to pro-
duce an annotation which mixes the qualitative spatial infor-
mation discovered from the newspaper with information that
corresponds to the relevant administrative regions of Greece.
Of course, such techniques are not always accurate and ex-
tracted information has to be accompanied by a confidence
level [Hoffart et al., 2010].
The next triples introduce the burnt area corresponding to
noa:fire1 and some details related to the administrative
geography of Greece as defined by the recent “Kallikratis
Plan”10. Since there is already work in encoding the adminis-
trative geography of countries, e.g., the UK [Goodwin et al.,
2008], in terms of qualitative spatial constraints such as the
ones we used above, we expect that such annotations can be a
useful source of information for the NOA application. This is
stressed by the fact that currently much of this information is
or will become available as public open data in portals of the
relevant European governments (e.g., see the geodata portal
of the Government of Greece11).
noa:fire1 rdf:type noa:ValidatedFire .
noa:fire1 ex:hasBurntArea _region2 .
10http://en.wikipedia.org/wiki/
Administrative_divisions_of_Greece/
11http://geodata.gov.gr/
kal:Zoniana rdf:type kal:Community .
kal:Mylopotamos rdf:type kal:Municipality .
kal:Rethymno rdf:type kal:Prefecture .
kal:Zoniana kal:occupies _region3 .
kal:Mylopotamos kal:occupies _region4 .
kal:Rethymno kal:occupies _region5 .
kal:Zoniana kal:partOf kal:Mylopotamos .
kal:Mylopotamos kal:partOf kal:Rethymno .
_region3 strdf:NTPP _region4 .
_region4 strdf:NTPP _region5 .
_region1 strdf:northOf kal:Zoniana .
_region2 strdf:northOf kal:Zoniana .
In the following, we discuss how to evaluate stSPARQL
queries over the stRDFi data given in the beginning of this
section. Let us consider the following query: “Find all fires
that have occurred in a region which is a non-tangential
proper part of the polygon defined by "POLYGON((24.823
35.308, 24.827 35.308, 24.827 35.305, 24.823
35.305, 24.823 35.308))"12. In stSPARQL, this query
can be expressed as shown in Figure 5. The answer to that
query is the one shown in Table 1. Notice, that this answer
is conditional. Because the information in the database is
indefinite (the exact geometry of region1 is not known),
we cannot say for sure whether fire1 satisfies the require-
ments of the query. These requirements are satisfied under
the condition given in the answer.
select ?F
where { ?F rdf:type noa:Fire .
?F noa:occuredIn ?R .
filter (strdf:NTPP(?R, "POLYGON((24.823
35.308, 24.827 35.308, 24.827 35.305,
24.823 35.305, 24.823 35.308))"))}
Figure 5: An example of a query for the stRDFi model ex-
pressed in stSPARQL
Table 1: A conditional answer in stRDFi
?F Condition
noa:fire1 region1 strdf:NTPP
"POLYGON((24.823 35.308, 24.827
35.308, 24.827 35.305, 24.823
35.305, 24.823 35.308))"
Let us consider the query of Figure 5 again. If we
rephrase it to “Find fires that have certainly occurred
in a region which is a non-tangential proper part of
the polygon defined by "POLYGON((24.823 35.308,
24.827 35.308, 24.827 35.305, 24.823 35.305,
24.823 35.308))", fire1 does not satisfy the query.
To be able to express such queries over stRDFi data,
in [Koubarakis et al., 2011] we have extended the
12Notice, that this second polygon is contained in the one men-
tioned previously.

semantics of query answering for stSPARQL given
in [Koubarakis and Kyzirakos, 2010] using well-known
techniques from the literature of incomplete informa-
tion in relational databases [Imielinski and Lipski, 1984;
Grahne, 1991] and constraint databases [Koubarakis, 1997].
4 Open Problems
In Sections 2 and 3 we used the NOA application of
TELEIOS as an example to demonstrate how linked geospa-
tial data sets that typically contain geometric objects spec-
ified by exact co-ordinates can be enriched with qualitative
spatial information to enable better knowledge representation
and more expressive query answering.
We expect that various kinds of qualitative spatial informa-
tion will soon become part of linked geospatial data sets with
advances in the automatic extraction of qualitative spatial re-
lations from textual Web sources [Schockaert et al., 2008],
images [Mylonas et al., 2009; Hudelot et al., 2008], etc. and
the creation of ontologies with a geospatial component such
as YAGO2 [Hoffart et al., 2010].
Let us now discuss a few open problems in the stRDFi
framework that require new contributions by the qualitative
spatial reasoning community:
• Checking the consistency of constraint networks that in-
volve qualitative spatial relations among regions identi-
fied by a URI and constant ones (e.g., a rectangle or a
polygon in the plane Q2 or in a Cartesian co-ordinate
system). This combination of qualitative and quantita-
tive constraints has been studied in detail for temporal
constraints [Koubarakis, 2006], but similar results do not
exist for spatial constraints.
• Checking the consistency of constraint networks that
involve qualitative and quantitative spatial relations
among planar regions that are constrained to have cer-
tain shapes (e.g., triangles, rectangles, polygons). The
case of rectangles has been studied in detail in the past
(e.g., see [Balbiani et al., 1999]) and there is some re-
cent work on topological relations among convex planar
regions [Li and Liu, 2010].
• Performing variable elimination in constraint networks
with qualitative and quantitative spatial constraints or,
equivalently, performing quantifier elimination in the as-
sociated first-order theory. As shown for the tempo-
ral case in [Koubarakis, 1997], variable elimination is
needed for answering certainty queries with answer vari-
ables (i.e., “What is the region that is on fire and is cer-
tainly inside a specific area?”). This cannot be done in
the general case even for topological relations [Bennet,
1997] but no detailed results beyond this are known.
• Scalable implementations of constraint network algo-
rithms for qualitative and quantitative spatial constraints.
RDF stores supporting linked geospatial data are ex-
pected to scale to billions of triples like their non-spatial
counterparts [Neumann and Weikum, 2008] and recent
work in this area is encouraging [Brodt et al., 2010].
Can this level of scalability be achieved when qualita-
tive spatial relations come into play? A good approach
here might to start with algorithms with low polynomial
complexity (even if they do not cover the general case)
and try to implement them as efficiently as possible. In
the temporal case, this approach has been followed suc-
cessfully by temporal reasoners such as TimeGraph-II
and extensions [Gerevini et al., 1994]. In addition, there
might be cases where network structure can be exploited
(e.g., hierarchical organization of geographical regions).
• There are no publicly available data sets, benchmarks
and related implementations. This workshop and the as-
sociated QSTR library is an excellent way to bring to-
gether the community and make progress in this area.
It is also important to liaise with similar efforts in the
Semantic Web community.
5 Related Work
Enriching linked data sources with geospatial information is
a recent activity. Two representative examples are [Auer et
al., 2009; de Leo´n et al., 2010]. In [Auer et al., 2009] Open-
StreetMap data are made available as RDF and queried using
the declarative query language SPARQL. Using similar tech-
nologies, [de Leo´n et al., 2010] makes available as linked
data various heterogeneous Spanish public datasets. In both
of these data sources qualitative spatial relations do not ap-
pear in the triples. YAGO2 [Hoffart et al., 2010] offers only
a part-of relation.
In addition to stSPARQL there have also been other works
developing spatial and temporal extensions for RDF and
SPARQL [Perry, 2008; Kolas, 2008]. There is also a forth-
coming OGC standard [OGC, 2010] for the development of
a query language for geospatial data encoded in RDF, called
GeoSPARQL.
In contrast to the above works, the area of description log-
ics has studied the representation and reasoning with quali-
tative spatial relations utilizing data models that are similar
to RDF. Racer was the first reasoner to support qualitative
spatial relations [Wessel and Moller, 2009]. More recently,
[Stocker and Sirin, 2009] has developed an extension of the
DL reasoner Pellet [Parsia and Sirin, 2004] that allows rea-
soning with RCC-8 relations. Finally, [Batsakis and Petrakis,
2010] proposes SOWL, an extension of OWL, to represent
spatial qualitative and quantitative information employing the
RCC-8 topological relations, cardinal direction relations, and
distance relations. To reason about spatial relations a set of
SWRL rules are implemented in the Pellet reasoner.
6 Conclusions
In this paper we proposed linked geospatial data on Semantic
Web as an interesting application area of qualitative spatial
reasoning techniques. In the context of our recent work on
the models stRDF and stSPARQL and their extensions with
indefinite geospatial information, we discussed some open
problems that may be of interest to the qualitative spatial rea-
soning community. As part of our future work we intend to
study the computational complexity of query processing for
the languages we have developed.

Acknowledgements
This work has been funded by the FP7 project TELEIOS
(257662). We are grateful to our colleagues from NOA for
providing us with data and discussing their use case with us.
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