Geo Data Models and Query Languages

Data Models and Query Languages
for Linked Geospatial Data
Reasoning Web 2012
Dept. of Informatics and Telecommunications
National and Kapodistrian University of Athens
Summer School
Manolis Koubarakis, Kostis Kyzirakos and
Charalampos Nikolaou

Reasoning Web 2012 – Summer School
Data Models and Query Languages for Linked Geospatial Data
2
Tutorial Organization
14:30 – 14:45 Introduction
14:45 – 15:15 Background in geospatial data modeling
15:15 – 16:00 Geospatial data in RDF - stSPARQL
16:00 – 16:30 Coffee break
16:30 – 16:45 Geospatial data in RDF - GeoSPARQL
16:45 – 17:00 Implemented RDF Stores with geospatial support
17:00 – 17:50 Geospatial information with description logics, OWL
and rules
17:50 – 18:00 Conclusions, questions, discussion

Introduction
Presenter: Manolis Koubarakis
Reasoning Web 2012
Summer School

4
Outline
• Why should you be interested in
geospatial information?
• Why should you attend this tutorial?

5
Why Geospatial Information?
• Geospatial, and in general geographical, information is very
important in reality: everything that happens, happens
somewhere (location).
• Decision making can be substantially improved if we know
where things take place.

6
Geography
• From http://en.wikipedia.org/wiki/Geography
• Geography is the science that studies the lands,
the features, the inhabitants and the phenomena of
the Earth.
• From the Greek word γεωγραφία (geographia)
which means “describing the Earth”.

7
Geographical Information Systems
and Science
• A geographical information system (GIS) is a system designed to capture,
store, manipulate, analyze, manage, and present all types of geographical
data.
• GIS science is the field of study for developing and using GIS.

8
Combining GIS Data for Decision Making

9
Why this tutorial?
• Lots of geospatial data is available on the Web
today.
• Lots of public data coming out of open government
initiatives is geospatial.
• Lots of the above data is quickly being transformed
into linked data!
• People have started building applications utilizing
linked data.

10
Geospatial data on the Web

11
Open Government Data

12
Linked geospatial data –
Ordnance Survey

13
Linked geospatial data –
Research Funding Explorer

14
Linked geospatial data – Spain

15
Linked geospatial data – Open Street Map

16
Conclusions
• Introduction
• Why should you be interested in
geospatial information?
• Why should you attend this tutorial?
• Next topic: Background in geospatial
data modeling

Background in geospatial data
modeling
Reasoning Web 2012
Summer School

2
Outline
• Basic GIS concepts and terminology
• Geographic space modeling paradigms
• Geospatial data standards

3
Basic GIS Concepts and Terminology
• Theme: the information corresponding to a particular domain
that we want to model. A theme is a set of geographic
features.
• Example: the countries of Europe

4
Basic GIS Concepts (cont’d)
• Geographic feature or geographic object: a domain entity
that can have various attributes that describe spatial and non-
spatial characteristics.
• Example: the country Greece with attributes
• Population
• Flag
• Capital
• Geographical area
• Coastline
• Bordering countries

5
• Geographic features can be atomic or complex.
• Example: According to the Kallikratis administrative reform of
2010, Greece consists of:
• 13 regions (e.g., Crete)
• Each region consists of perfectures (e.g., Heraklion)
• Each perfecture consists of municipalities (e.g., Dimos
Chersonisou)

6
• The spatial characteristics of a feature can involve:
• Geometric information (location in the underlying
geographic space, shape etc.)
• Topological information (containment, adjacency etc.).
Municipalities of the perfecture of
Heraklion:
1. Dimos Irakliou
2. Dimos Archanon-Asterousion
3. Dimos Viannou
4. Dimos Gortynas
5. Dimos Maleviziou
6. Dimos Minoa Pediadas
7. Dimos Festou
8. Dimos Chersonisou

7
Geometric Information
• Geometric information can be captured by using geometric primitives
(points, lines, polygons, etc.) to approximate the spatial attributes of
the real world feature that we want to model.
• Geometries are associated with a coordinate reference system which
describes the coordinate space in which the geometry is defined.

8
Topological Information
• Topological information is inherently qualitative and it is
expressed in terms of topological relations (e.g., containment,
adjacency, overlap etc.).
• Topological information can be derived from geometric
information or it might be captured by asserting explicitly the
topological relations between features.

9
Topological Relations
• The study of topological relations has produced
a lot of interesting results by researchers in:
• GIS
• Spatial databases
• Artificial Intelligence (qualitative reasoning
and knowledge representation)

10
The 4-intersection model
• The 4-intersection model has been defined by Egenhofer and
Franzosa in 1991 based on previous work by Egenhofer and
colleagues.
• It is based on point-set topology.
• Spatial regions are defined to be non-empty, proper subsets
of a topological space. In addition, they must be closed and
have connected interiors.
• Topological relations are the ones that are invariant under
topological homeomorphisms.

11
4IM and 9IM
• The 4-intersection model can captures topological relations
between two spatial regions a and b by considering whether the
intersection of their boundaries and interiors is empty or
non-empty.
• The 9-intersection model is an extension of the 4-intersection
model (Egenhofer and Herring, 1991).
• 9IM captures topological relations between two spatial regions a
and b by considering whether the intersection of their boundaries,
interiors and exteriors is empty or non-empty.

12
DE-9IM
• The dimensionally extended 9-intersection model
has been defined by Clementini and Felice in 1994.
• It is also based on the point-set topology of R2 and
deals with “simple”, closed geometries (areas,
lines, points).
• Like its predecessors (4IM, 9IM), it can also be
extended to more complex geometries (areas with
holes, geometries with multiple components).

13
DE-9IM
• It captures topological relationships between two
geometries a and b in R2 by considering the
dimensions of the intersections of the
boundaries, interiors and exteriors of the two
geometries:
• The dimension can be 2, 1, 0 and -1 (dimension of
the empty set).

14
DE-9IM
• Five jointly exclusive and pairwise disjoint (JEPD)
relationships between two different geometries can be
distinguished (disjoint, touches, crosses, within, overlaps).
• The model can also be defined using an appropriate calculus of
geometries that uses these 5 binary relations and boundary
operators.
• See the paper: E. Clementini and P. Felice. A Comparison of
Methods for Representing Topological Relationships. Information
Sciences 80 (1994), pp. 1-34.

15
Example: A disjoint C
I(C) B(C) E(C)
I(A) F F *
B(A) F F *
E(A) * * *
A
C
Notation:
• T = { 0, 1, 2 }
• F = -1
• * = don’t care = { -1, 0, 1, 2 }

16
Example: A within C
I(C) B(C) E(C)
I(A) T * F
B(A) * * F
E(A) * * *
C
A
Notation equivalent to 3x3
matrix:
• String of 9 characters
representing the above matrix in
row major order.
• In this case: T*F**F***

17
DE-9IM Relation Definitions

18
The Region Connection Calculus (RCC)
• The primitives of the calculus are spatial regions. These are
non-empty, regular subsets of a topological space.
• The calculus is based on a single binary predicate C that
formalizes the “connectedness” relation.
• C(a,b) is true when the closure of a is connected to the
closure of b i.e., they have at least one point in common.
• It is axiomatized using first order logic.
• See the original paper by Randell, Cui and Cohn (KR 1991).

19
RCC-8
• This is a set of eight JEPD binary relations
that can be defined in terms of predicate C.

20
RCC-5
• The RCC-5 subset has also been studied. The
granularity here is coarser. The boundary of a region is
not taken into consideration:
• No distinction among DC and EC, called just DR.
• No distinction among TPP and NTPP, called just
PP.
• RCC-8 and RCC-5 relations can also be defined
using point-set topology, and there are very close
connections to the models of Egenhofer and others.

21
More Qualitative Spatial Relations
• Orientation/Cardinal directions (left of, right of,
north of, south of, northeast of etc.)
• Distance (close to, far from etc.). This information
can also be quantitative.

22
Coordinate Systems
• Coordinate: one of n scalar values that determines the position
of a point in an n-dimensional space.
• Coordinate system: a set of mathematical rules for specifying
how coordinates are to be assigned to points.
• Example: the Cartesian coordinate system

23
Coordinate Reference Systems
• Coordinate reference system: a coordinate system
that is related to an object (e.g., the Earth, a planar
projection of the Earth, a three dimensional
mathematical space such as R3) through a datum
which specifies its origin, scale, and orientation.
• The term spatial reference system is also used.

24
Geographic Coordinate Reference Systems
• These are 3-dimensional coordinate systems that utilize latitude
(φ), longitude (λ) , and optionally geodetic height (i.e.,
elevation), to capture geographic locations on Earth.

25
The World Geodetic System
• The World Geodetic System (WGS) is the most well-known
geographic coordinate reference system and its latest revision is
WGS84.
• Applications: cartography, geodesy, navigation (GPS), etc.

26
Projected Coordinate Reference Systems
• Projected coordinate reference system: they transform the 3-
dimensional approximation of the Earth into a 2-dimensional
surface (distortions!)
• Example: the Universal Transverse Mercator (UTM) system

27
Coordinate Reference Systems (cont’d)
• There are well-known ways to translate between co-
ordinate reference systems.
• Various authorities maintain lists of coordinate
reference systems. See for example:
• OGC http://www.opengis.net/def/crs/
• European Petroleum Survey Group
http://www.epsg-registry.org/

28
Geographic Space Modeling Paradigms
• Abstract geographic space modeling
paradigms: discrete objects vs. continuous
fields
• Concrete representations: tessellation vs.
vectors vs. constraints

29
Abstract Modeling Paradigms:
Feature-based
• Feature-based (or entity-based or object-based). This kind of
modeling is based on the concepts we presented already.

30
Abstract Modeling Paradigms:
Field-based
• Each point (x,y) in geographic space is associated with one or
several attribute values defined as continuous functions in x
and y.
• Examples: elevation, precipitation, humidity, temperature for
each point (x,y) in the Euclidean plane.

31
From Abstract Modeling to Concrete
Representations
• Question: How do we represent the infinite objects of the
abstract representations (points, lines, fields etc.) by finite
means (in a computer)?
• Answers:
• Approximate the continuous space (e.g., ℝ2) by a discrete
one (ℤ2).
• Use special encodings

32
Approximations: Tessellation
• In this case a cellular decomposition of the plane (usually, a
grid) serves as a basis for representing the geometry.
• Example: raster representation (fixed or regular tesselation)

33
Example
• Cadastral map (irregular tessellation) overlayed on a satellite
image.

34
Special Encodings:
Vector Representation
• In this case objects in space are represented using points as
primitives as follows:
• A point is represented by a tuple of coordinates.
• A line segment is represented by a pair with its beginning
and ending point.
• More complex objects such as arbitrary lines, curves,
surfaces etc. are built recursively by the basic primitives
using constructs such as lists, sets etc.
• This is the approach used in all GIS and other popular
systems today. It has also been standardized by various
international bodies.

35
Example
[(1,2) (2,2) (5,3) (3,1) (2,1) (1 2)]

36
Special Encodings:
Constraint Representation
• In this case objects in space are represented by quantifier free
formulas in a constraint language (e.g., linear constraints).
)
3
4
3
53()124()223( ≤−∧≤∧≥∨≥∧≥∧≤+∨≤∧≤∧≥+
x
yxyyxxyyxxy

37
Constraint Databases
• The constraint representation of spatial data was the focus of
much work in databases, logic programming and AI after the
paper by Kanellakis, Kuper and Revesz (PODS, 1991).
• The approach was very fruitful theoretically but was not adopted
in practice.
• See the book by Revesz for a tutorial introduction.

38
Geospatial Data Standards
• The Open Geospatial Consortium (OGC) and the
International Organization for Standardization (ISO) have
developed many geospatial data standards that are in wide use
today. In this tutorial we will cover:
• Well-Known Text
• Geography Markup Language
• OpenGIS Simple Feature Access

39
Well-Known Text (WKT)
• WKT is an OGC and ISO standard for representing geometries,
coordinate reference systems, and transformations between
coordinate reference systems.
• WKT is specified in OpenGIS Simple Feature Access - Part 1:
Common Architecture standard which is the same as the ISO 19125-1
standard. Download from
http://portal.opengeospatial.org/files/?artifact_id=25355 .
• This standard concentrates on simple features: features with all
spatial attributes described piecewise by a straight line or a
planar interpolation between sets of points.

40
WKT Class Hierarchy

41
Example
WKT representation:
GeometryCollection(
Point(5 35),
LineString(3 10,5 25,15 35,20 37,30 40),
Polygon((5 5,28 7,44 14,47 35,40 40,20 30,5 5),
(28 29,14.5 11,26.5 12,37.5 20,28 29))
)

42
Geography Markup Language (GML)
• GML is an XML-based encoding standard for the
representation of geospatial data.
• GML provides XML schemas for defining a variety of concepts:
geographic features, geometry, coordinate reference
systems, topology, time and units of measurement.
• GML profiles are subsets of GML that target particular
applications.
• Examples: Point Profile, GML Simple Features Profile etc.

43
GML Simple Features:
Class Hierarchy

44
Example
GML representation:
<gml:Polygon gml:id="p3" srsName="urn:ogc:def:crs:EPSG:6.6:4326”>
<gml:exterior>
<gml:LinearRing>
<gml:coordinates>
5,5 28,7 44,14 47,35 40,40 20,30 5,5
</gml:coordinates>
</gml:LinearRing>
</gml:exterior>
</gml:Polygon>

45
OpenGIS Simple Features Access
(cont’d)
• OGC has also specified a standard for the storage, retrieval,
query and update of sets of simple features using
relational DBMS and SQL.
• This standard is “OpenGIS Simple Feature Access - Part 2: SQL
Option” and it is the same as the ISO 19125-2 standard. Download from
http://portal.opengeospatial.org/files/?artifact_id=25354.
• Related standard: ISO 13249 SQL/MM - Part 3.

46
OpenGIS Simple Features Access
(cont’d)
• The standard covers two implementations options: (i) using only
the SQL predefined data types and (ii) using SQL with
geometry types.
• SQL with geometry types:
• We use the WKT geometry class hierarchy presented earlier
to define new geometric data types for SQL
• We define new SQL functions on those types.

47
SQL with Geometry Types -
Functions
• Functions that request or check properties of a geometry:
• ST_Dimension(A:Geometry):Integer
• ST_GeometryType(A:Geometry):Character Varying
• ST_AsText(A:Geometry): Character Large Object
• ST_AsBinary(A:Geometry): Binary Large Object
• ST_SRID(A:Geometry): Integer
• ST_IsEmpty(A:Geometry): Boolean
• ST_IsSimple(A:Geometry): Boolean

48
SQL with Geometry Types –
Functions (cont’d)
• Functions that test topological relations between two geometries
using the DE-9IM:
• ST_Equals(A:Geometry, B:Geometry):Boolean
• ST_Disjoint(A:Geometry, B:Geometry):Boolean
• ST_Intersects(A:Geometry, B:Geometry):Boolean
• ST_Touches(A:Geometry, B:Geometry):Boolean
• ST_Crosses(A:Geometry, B:Geometry):Boolean
• ST_Within(A:Geometry, B:Geometry):Boolean
• ST_Contains(A:Geometry, B:Geometry):Boolean
• ST_Overlaps(A:Geometry, B:Geometry):Boolean
• ST_Relate(A:Geometry, B:Geometry, Matrix: Char(9)):Boolean

49
DE-9IM Relation Definitions
• A equals B can also be
defined by the pattern
TFFFTFFFT.
• A intersects B is the
negation of A disjoint B
• A contains B is equivalent
to B within A

50
SQL with Geometry Types –
Functions (cont’d)
• Functions for constructing new geometries out of existing
ones:
• ST Boundary(A:Geometry):Geometry
• ST_Envelope(A:Geometry):Geometry
• ST_Intersection(A:Geometry, B:Geometry):Geometry
• ST_Union(A:Geometry, B:Geometry):Geometry
• ST_Difference(A:Geometry, B:Geometry):Geometry
• ST_SymDifference(A:Geometry, B:Geometry):Geometry
• ST_Buffer(A:Geometry, distance:Double):Geometry

51
Geospatial Relational DBMS
• The OpenGIS Simple Features Access Standard is today been
used in all relational DBMS with a geospatial extension.
• The abstract data type mechanism of the DBMS allows
the representation of all kinds of geospatial data types
supported by the standard.
• The query language (SQL) offers the functions of the
standard for querying data of these types.

52
Conclusions
• Background in geospatial data modeling:
• Why geographical information?
• Geographical Information Science and Systems
• Geospatial data on the Web and linked geospatial data
• Abstract geographic space modeling paradigms: discrete
objects vs. continuous fields
• Concrete representations: tessellation vs. vectors vs.
constraints
• Geospatial data standards
• Next topic: Geospatial data in the Semantic Web

Geospatial data in RDF –
stSPARQL
Presenter: Kostis Kyzirakos
Reasoning Web 2012
Summer School

2
Outline
 Main idea
 Early works
 The data model stRDF
 Examples of publicly available linked
geospatial data
 The query language stSPARQL

3
Main idea
How do we represent and query geospatial
information in the Semantic Web?
Extend RDF to take into account the
geospatial dimension.
Extend SPARQL to query the new kinds of
data.

4
Early works
SPAUK
 Geometric attributes of a resource are represented by:
• introducing a blank node for the geometry
• specifying the geometry using GML vocabulary
• associating the blank node with the resource
using GeoRSS vocabulary
 Queries are expressed in SPARQL utilizing appropriate
geometric vocabularies and ontologies (e.g., the
topological relationships of RCC-8).
 Introduces a new PREMISE clause in SPARQL to specify
spatial geometries to be used in a query
 Use some form of the DESCRIBE query form of SPARQL
for asking queries about geometries

5
Early works
SPARQL-ST
 Assumes a particular upper ontology
expressed in RDFS for modeling theme,
space and valid time.
 Spatial geometries in SPARQL-ST are
specified by sets of RDF triples that give
various details of the geometry.
 SPARQL-ST provides a set of built-in spatial
conditions that can be used in SPATIAL
FILTER clauses to constrain the geometries
that are returned as answers to queries.

6
stRDF and stSPARQL
 Similar approach to SPARQL-ST
(theme, space and valid time
can be represented)
 Linear constraints are used to represent
geometries
 Constraints are represented using literals of
an appropriate datatype
 Formal approach
 New version to be presented today uses OGC
standards to represent and query
geometries

7
Example

8
Example with simplified geometries

9
Example in stRDF
geonames:Olympia
geonames:name "Ancient Olympia";
owl:sameAs dbpedia:Olympia_Greece;
rdf:type dbpedia:Community .
Spatial
data type
geonames:Olympia strdf:hasGeometry
"POLYGON((21.5 18.5, 23.5 18.5,
23.5 21, 21.5 21, 21.5 18.5));
<http://www.opengis.net/def/crs/EPSG/0/4326>"^^
strdf:WKT .
Spatial
literal

10
strdf:geometry rdf:type rdfs:Datatype;
rdfs:subClassOf rdfs:Literal.
strdf:WKT rdf:type rdfs:Datatype;
rdfs:subClassOf rdfs:Literal;
rdfs:subClassOf strdf:geometry.
strdf:GML rdf:type rdfs:Datatype;
rdfs:subClassOf rdfs:Literal;
rdfs:subClassOf strdf:geometry.
The stRDF Data Model

11
We define the datatypes strdf:WKT and strdf:GML
that can be used to represent spatial objects
using the WKT and GML serializations.
 Lexical space: the finite length sequences of
characters that can be produced from the WKT and
GML specifications.
 Literals of type strdf:WKT consist of an optional URI
identifying the coordinate reference system used.
e.g., "POINT(21 18);
<http://www.opengis.net/def/crs/EPSG/0/4326>"
^^strdf:WKT

12
 Value space: the set of geometry values defined
in the WKT and GML standard that is a subset of
the powerset of ℝ2
and ℝ3
.
 Lexical-to-value mapping: takes into account
that the vector-based model is used for
representing geometries.
 The datatype strdf:geometry is the union of
the datatypes strdf:WKT and strdf:GML.

13
Examples of publicly available
linked geospatial data
 Geonames
 Greek Administrative Geography
 Corine Land Use / Land Cover
 Burnt Area Products

14
Geonames

15
Geonames

Geonames
gn:2761333
rdf:type geonames:Feature;
geonames:officialName "Vienna"@en;
geonames:name "Politischer Bezirk Wien (Stadt)";
geonames:countryCode "AT";
wgs84_pos:lat "48.2066";
wgs84_pos:long "16.37341".
geonames:parentCountry gn:2782113;
gn:2782113
geonames:name "Austria";
geonames:altName "Republic of Austria"@en,
"Republik Osterreich"@de,
"Αυστρία"@el.

17
Greek Administrative Geography
Kallikrates ontology

18
Kallikrates ontology

gag:Olympia
rdf:type gag:Community;
gag:population "184"^^xsd:int;
strdf:hasGeometry "POLYGON
(((25.37 35.34,…)))"^^strdf:WKT.
gag:OlympiaBorough
rdf:type gag:Borough;
rdfs:label "Borough of
Ancient Olympia".
gag:OlympiaMunicipality
rdf:type gag:Municipality;
rdfs:label "Municipality of
Ancient Olympia".
gag:Olympia gag:isPartOf gag:OlympiaBorough .
gag:OlympiaBorough gag:isPartOf gag:OlympiaMunicipality.
19

20
Corine Land Use / Land Cover

21

22
noa:Area_24015134
rdf:type noa:Area ;
noa:hasCode "312"^^xsd:decimal;
noa:hasID "EU-203497"^^xsd:string;
noa:hasArea_ha "255.5807904"^^xsd:double;
strdf:hasGeometry "POLYGON((15.53 62.54,
…))"^^strdf:WKT;
noa:hasLandUse noa:ConiferousForest

23
Burnt Area Products

24
Burnt Area Products

25
Burnt Area Products
noa:ba_15
rdf:type noa:BurntArea;
noa:isProducedByProcessingChain
"static thresholds"^^xsd:string;
noa:hasAcquisitionTime
"2010-08-24T13:00:00"^^xsd:dateTime;
strdf:hasGeometry "MULTIPOLYGON(((
393801.42 4198827.92, ..., 393008 424131)));
<http://www.opengis.net/def/crs/
EPSG/0/2100>"^^strdf:WKT.

26
stSPARQL: Geospatial SPARQL 1.1
We define a SPARQL extension function for each function
defined in the OpenGIS Simple Features Access standard
Basic functions
 Get a property of a geometry
xsd:int strdf:Dimension(strdf:geometry A)
xsd:string strdf:GeometryType(strdf:geometry A)
xsd:int strdf:SRID(strdf:geometry A)
 Get the desired representation of a geometry
xsd:string strdf:AsText(strdf:geometry A)
strdf:wkb strdf:AsBinary(strdf:geometry A)
xsd:string strdf:AsGML(strdf:geometry A)
 Test whether a certain condition holds
xsd:boolean strdf:IsEmpty(strdf:geometry A)
xsd:boolean strdf:IsSimple(strdf:geometry A)

27
Functions for testing topological spatial
relationships
 OGC Simple Features Access
xsd:boolean strdf:Equals(strdf:geometry A, strdf:geometry B)
xsd:boolean strdf:Disjoint(strdf:geometry A, strdf:geometry B)
xsd:boolean strdf:Intersects(strdf:geometry A, strdf:geometry B)
xsd:boolean strdf:Touches(strdf:geometry A, strdf:geometry B)
xsd:boolean strdf:Crosses(strdf:geometry A, strdf:geometry B)
xsd:boolean strdf:Within(strdf:geometry A, strdf:geometry B)
xsd:boolean strdf:Contains(strdf:geometry A, strdf:geometry B)
xsd:boolean strdf:Overlaps(strdf:geometry A, strdf:geometry B)
xsd:boolean strdf:Relate(strdf:geometry A, strdf:geometry B,
xsd:string intersectionPatternMatrix)
 Egenhofer
 RCC-8

28
Spatial analysis functions
 Construct new geometric objects from existing geometric
objects
strdf:geometry strdf:Boundary(strdf:geometry A)
strdf:geometry strdf:Envelope(strdf:geometry A)
strdf:geometry strdf:Intersection(strdf:geometry A, strdf:geometry B)
strdf:geometry strdf:Union(strdf:geometry A, strdf:geometry B)
strdf:geometry strdf:Difference(strdf:geometry A, strdf:geometry B)
strdf:geometry strdf:SymDifference(strdf:geometry A, strdf:geometry B)
strdf:geometry strdf:Buffer(strdf:geometry A, xsd:double distance)
 Spatial metric functions
xsd:float strdf:distance(strdf:geometry A, strdf:geometry B)
xsd:float strdf:area(strdf:geometry A)
 Spatial aggregate functions
strdf:geometry strdf:Union(set of strdf:geometry A)
strdf:geometry strdf:Intersection(set of strdf:geometry A)
strdf:geometry strdf:Extent(set of strdf:geometry A)

29
Select clause
 Construction of new geometries (e.g., strdf:buffer(?geo, 0.1))
 Spatial aggregate functions (e.g., strdf:union(?geo))
 Metric functions (e.g., strdf:area(?geo))
Filter clause
 Functions for testing topological spatial relationships between spatial terms
(e.g., strdf:contains(?G1, strdf:union(?G2, ?G3)))
 Numeric expressions involving spatial metric functions
(e.g., strdf:area(?G1) ≤ 2*strdf:area(?G2)+1)
 Boolean combinations
Having clause
 Boolean expressions involving spatial aggregate functions and spatial
metric functions or functions testing for topological relationships between
spatial terms (e.g., strdf:area(strdf:union(?geo))>1)

30
stSPARQL: An example (1/3)
SELECT ?name
WHERE {
?community rdf:type dbpedia:Community;
geonames:name ?name;
strdf:hasGeometry ?comGeom.
?ba rdf:type noa:BurntArea;
strdf:hasGeometry ?baGeom.
FILTER(strdf:overlap(?comGeom,?baGeom))
}
Spatial
Function
Return the names of communities that have been
affected by fires

31
stSPARQL: An example (2/3)
SELECT ?ba ?baGeom
WHERE {
?r rdf:type noa:Region;
strdf:geometry ?rGeom;
noa:hasCorineLandCoverUse ?f.
?f rdfs:subClassOf clc:Forests.
?c rdf:type dbpedia:Community;
strdf:geometry ?cGeom.
?ba rdf:type noa:BurntArea;
strdf:geometry ?baGeom.
FILTER( strdf:intersects(?rGeom,?baGeom) &&
strdf:distance(?baGeom,?cGeom) < 0.02)}Spatial
Functions
Find all burnt forests near communities

Spatial
Function
32
SELECT ?burntArea
(strdf:intersection(?baGeom,
strdf:union(?fGeom))
AS ?burntForest)
WHERE {
?burntArea rdf:type noa:BurntArea;
strdf:hasGeometry ?baGeom.
?forest rdf:type noa:Region;
noa:hasLandCover noa:coniferousForest;
strdf:hasGeometry ?fGeom.
FILTER(strdf:intersects(?baGeom,?fGeom))
}
GROUP BY ?burntArea ?baGeom
Isolate the parts of the burnt areas that lie in
coniferous forests.
stSPARQL: An example 3/3)
Spatial
Aggregate

33
Conclusions
 Geospatial data in the Semantic Web - stSPARQL
 Early works
 The data model stRDF
 Examples of publicly available linked geospatial
data
 The query language stSPARQL
 Next topic: Geospatial data in RDF - GeoSPARQL

Bibliography
34
[Kolas and Self, 2007]
Kolas, D., Self, T.: Spatially Augmented Knowledgebase. In:
Proceedings of the 6th International Semantic Web Conference and
2nd Asian Semantic Web Conference (ISWC/ASWC2007). Lecture
Notes in Computer Science, vol. 4825, pp. 785-794. Springer
Verlag (2007)
[Perry, 2008]
Perry, M.: A Framework to Support Spatial, Temporal and Thematic
Analytics over Semantic Web Data. Ph.D. thesis, Wright State
University (2008)
[Koubarakis and Kyzirakos, 2010]
Koubarakis, M., Kyzirakos, K.: Modeling and Querying Metadata in the
Semantic Sensor Web: The Model stRDF and the Query Language
stSPARQL. In: ESWC. pp. 425-439 (2010)

Geospatial data in RDF –
GeoSPARQL
Reasoning Web 2012
Summer School

2
GeoSPARQL
GeoSPARQL is a recently completed
OGC standard
Functionalities similar to stSPARQL:
 Geometries are represented using literals
similarly to stSPARQL.
 The same families of functions are offered for
querying geometries.
Functionalities beyond stSPARQL:
 Topological relations can now be asserted as
well so that reasoning and querying on them is
possible.

3
Example in GeoSPARQL (1/2)
geonames:Olympia
rdf:type dbpedia:Community ;
geo:hasGeometry ex:polygon1.
ex:polygon1
rdf:type geo:Polygon;
geo:asWKT "POLYGON((21.5 18.5,23.5 18.5,
23.5 21,21.5 21,21.5 18.5))
"^^sf:wktLiteral.
Spatial
data type
Spatial
literal

4
Example in GeoSPARQL (2/2)
rdfs:label "ΔΗΜΟΣ ΑΡΧΑΙΑΣ
ΟΛΥΜΠΙΑΣ"@el;
Ancient Olympia".
Asserted
topological
relation
gag:olympiaMunicipality geo:sfContains geonames:olympia .

GeoSPARQL Components
Core
Topology Vocabulary
Extension
- relation family
Geometry Extension
- serialization
- version
Geometry Topology
Extension
- serialization
- version
- relation family
Query Rewrite
Extension
- serialization
- version
- relation family
RDFS Entailment
Extension
- serialization
- version
- relation family
Parameters
• Serialization
• WKT
• GML
• Relation Family
• Simple
Features
• RCC-8
• Egenhofer

6
GeoSPARQL Core
Defines top level classes that provides
users with vocabulary for modeling geospatial
information.
 The class geo:SpatialObject is the
top class and has as instances
everything that can have a spatial
representation.
 The class geo:Feature is a subclass
of geo:SpatialObject. Feature is a
domain entity that can have various
attributes that describe spatial and
non-spatial characteristics.

7
Example
GeoSPARQL representation of the community of
Ancient Olympia.
dbpedia:Community rdfs:subClassOf geo:Feature .
geonames:Olympia geonames:name "Ancient Olympia";

8
GeoSPARQL Geometry Extension
Provides vocabulary for asserting and querying
information about geometries.
 The class geo:Geometry is a top class which is a
superclass of all geometry classes.

9
Example
GeoSPARQL representation of the community of Ancient
Olympia.
dbpedia:Community rdfs:subClassOf geo:Feature .
geonames:Olympia geonames:name "Ancient Olympia";
geonames:Olympia geo:hasGeometry ex:polygon1.
ex:polygon1 rdf:type geo:Polygon;
geo:isEmpty "false"^^xsd:boolean;
geo:asWKT "POLYGON((21.5 18.5, 23.5
18.5, 23.5 21, 21.5 21,
21.5 18.5))"^^sf:wktLiteral.
Spatial
data type

10
GeoSPARQL Geometry Extension
Spatial analysis functions
 Construct new geometric objects from existing geometric
objects
geof:boundary (geom1: ogc:geomLiteral): ogc:geomLiteral
geof:envelope (geom1: ogc:geomLiteral): ogc:geomLiteral
geof:intersection( geom1: ogc:geomLiteral,
geom2: ogc:geomLiteral): ogc:geomLiteral
geof:union ( geom1: ogc:geomLiteral,
geof:difference ( geom1: ogc:geomLiteral,
geof:symDifference (geom1: ogc:geomLiteral,
geom2:ogc:geomLiteral): ogc:geomLiteral
geof:buffer(geom: ogc:geomLiteral, radius: xsd:double,
units: xsd:anyURI): ogc:geomLiteral
geof:convexHull(geom1: ogc:geomLiteral): ogc:geomLiteral
 Spatial metric functions
geof:distance(geom1: ogc:geomLiteral, geom2:
ogc:geomLiteral, units: xsd:anyURI): xsd:double

11
GeoSPARQL Topology Vocabulary Extension
 The extension is parameterized by the family of topological
relations supported.
 Topological relations for simple features
 The Egenhofer relations e.g., geo:ehMeet
 The RCC-8 relations e.g., geo:rcc8ec

gag:Olympia
rdf:type gag:Community;
geonames:name "Ancient Olympia".
gag:OlympiaBorough
rdf:type gag:Borough;
rdfs:label "Borough of
Ancient Olympia".
Ancient Olympia".
gag:OlympiaBorough geo:sfContains geonames:Olympia .
gag:OlympiaMunicipality geo:sfContains
geonames:OlympiaBorough.
12
Example
Asserted
topological
relation

13
GeoSPARQL: An example
SELECT ?m
WHERE {
?m rdf:type gag:Borough.
?m geo:sfContains geonames:Olympia.
}
Find the borough that contains the
community of Ancient Olympia
Topological
Predicate

14
SELECT ?m
WHERE {
?m rdf:type gag:Municipality.
?m geo:sfContains geonames:Olympia.
}
Find the municipality that contains the
community of Ancient Olympia
What is the
answer to this
query?

15
Example (cont’d)
The answer to the previous query is
?m = gag:OlympiaMunicipality
GeoSPARQL does not tell you how to
compute this answer which needs
reasoning about the transitivity of
relation geo:sfContains.
Options:
• Use rules
• Use constraint-based techniques

16
GeoSPARQL Geometry Topology Extension
 Defines Boolean functions that correspond to each of
the topological relations of the topology vocabulary
extension:
 OGC Simple Features Access
geof:sfEquals(geom1: ogc:geomLiteral, geom2: ogc:geomLiteral): xsd:boolean
geof:sfDisjoint(geom1: ogc:geomLiteral, geom2: ogc:geomLiteral): xsd:boolean
geof:sfIntersects(geom1: ogc:geomLiteral,geom2: ogc:geomLiteral): xsd:boolean
geof:sfTouches(geom1: ogc:geomLiteral, geom2: ogc:geomLiteral): xsd:boolean
geof:sfCrosses(geom1: ogc:geomLiteral, geom2: ogc:geomLiteral): xsd:boolean
geof:sfWithin(geom1: ogc:geomLiteral, geom2: ogc:geomLiteral): xsd:boolean
geof:sfContains(geom1: ogc:geomLiteral, geom2: ogc:geomLiteral): xsd:boolean
geof:sfOverlaps(geom1: ogc:geomLiteral, geom2: ogc:geomLiteral): xsd:boolean
 Egenhofer
 RCC-8

 Provides a mechanism for realizing the RDFS entailments that
follow from the geometry class hierarchies defined by the WKT
and GML standards.
 Systems should use an implementation of RDFS entailment to
allow the derivation of new triples from those already in a graph.
17
GeoSPARQL RDFS Entailment Extension

18
Example
Given the triples
ex:f1 geo:hasGeometry ex:g1 .
geo:hasGeometry rdfs:domain geo:Feature.
we can infer the following triples:
ex:f1 rdf:type geo:Feature .
ex:f1 rdf:type geo:SpatialObject .

19
GeoSPARQL Query Rewrite Extension
 Provides a collection of RIF rules that use topological extension
functions to establish the existence of topological predicates.
 Example: given the RIF rule named geor:sfWithin, the
serializations of the geometries of dbpedia:Athens and
dbpedia:Greece named AthensWKT and GreeceWKT and the
fact that
geof:sfWithin(AthensWKT, GreeceWKT)
returns true from the computation of the two geometries, we can
derive the triple
dbpedia:Athens geo:sfWithin dbpedia:Greece
 One possible implementation is to re-write a given SPARQL
query.

20
RIF Rule
Forall ?f1 ?f2 ?g1 ?g2 ?g1Serial ?g2Serial
(?f1[geo:sfWithin->?f2] :-
Or(
And (?f1[geo:defaultGeometry->?g1]
?f2[geo:defaultGeometry->?g2]
?g1[ogc:asGeomLiteral->?g1Serial]
External(geo:sfWithin (?g1Serial,?g2Serial)))
?f2[ogc:asGeomLiteral->?g2Serial]
And (?f1[ogc:asGeomLiteral->?g1Serial]
))
Feature
-
Feature
Feature
-
Geometry
Geometry
-
Feature
Geometry
-
Geometry

21
SELECT ?feature
WHERE {
?feature geo:sfWithin
geonames:OlympiaMunicipality.
}
Discover the features that are inside the municipality of
Ancient Olympia

22
SELECT ?feature
WHERE { {?feature geo:sfWithin geonames:Olympia }
UNION
{ ?feature geo:defaultGeometry ?featureGeom .
?featureGeom geo:asWKT ?featureSerial .
geonames:Olympia geo:defaultGeometry ?olGeom .
?olGeom geo:asWKT ?olSerial .
FILTER (geof:sfWithin (?featureSerial, ?olSerial)) }
UNION { ?feature geo:defaultGeometry ?featureGeom .
?featureGeom geo:asWKT ?featureSerial .
geonames:Olympia geo:asWKT ?olSerial .
UNION { ?feature geo:asWKT ?featureSerial .
geonames:Olympia geo:defaultGeometry ?olGeom .
?olGeom geo:asWKT ?olSerial .
UNION {
?feature geo:asWKT ?featureSerial .
geonames:Olympia geo:asWKT ?olSerial .

23
Conclusions
 Geospatial data in the Semantic Web
 The query language GeoSPARQL
 Core
 Topology vocabulary extension
 Geometry extension
 Geometry topology extension
 Query rewrite extension
 RDFS entailment extension
 Next topic: Implemented RDF Stores with Geospatial
Support

Bibliography
24
[Perry and Herring, 2012]
Open Geospatial Consortium. OGC GeoSPARQL - A geographic query
language for RDF data. OGC Candidate Implementation Standard
(2012)

Implemented RDF Stores with
Geospatial Support
Reasoning Web 2012
Summer School

Outline
 Relational DBMS with a geospatial extension
 RDF stores with a geospatial component:
• Research prototypes
• Commercial systems
2

 Geometries are not explicitly handled by query language (SQL)
 Define datatypes that extend the SQL type system
• Model geometries using Abstract Data Type (ADT)
• Hide the structure of the data type to the user
 The interface to an ADT is a list of operations
 For spatial ADTs: Operations defined according to OGC
Simple Features for SQL
 Vendor-specific implementation irrelevant - extend SQL with
geometric functionality independently of a specific
representation/implementation
How does an RDBMS handle
geometries? (1/2)
3

Special indices needed for geometry data types
How does an RDBMS handle
geometries? (2/2)
4
Specialised query processing methods

Will examine following aspects:
 Data model
 Query language
 Functionality exposed
 Coordinate Reference System support
 Indexing Mechanisms
Implemented Systems
5

 Strabon
 Parliament
 Brodt et al.
 Perry
Research Prototypes
6

 Storage and query evaluation
module for stSPARQL
 Geometries represented using typed literals
WKT & GML serializations supported
 Spatial predicates represented as SPARQL functions
OGC-SFA, Egenhofer, RCC-8 families exposed
Spatial aggregate functions
 Support for multiple coordinate reference systems
 GeoSPARQL support
Core
Geometry Extension
Geometry Topology Extension
Strabon
7

Strabon - Implementation
8
stRDF
graphs
stSPARQL/
GeoSPARQL
queries
WKT GML
Open Source, available from http://www.strabon.di.uoa.gr/

 Storage Engine
 Developed by Raytheon BBN Technologies
 Implementation of GeoSPARQL
• Geometries represented using typed literals
WKT & GML serializations supported
• Three families of topological functions
exposed
OGC-SFA
Egenhofer
RCC-8
• Multiple CRS support
Parliament
9

 Rule engine included
 Paired with query
processor
 R-tree used
Parliament - Implementation
10
Open Source, available from
http://www.parliament.semwebcentral.org

 Built on top of RDF-3X
 Implemented at University of Stuttgart
 No formal definitions of data model and
query language given
 Geometries expressed according to OGC-SFA
Typed Literals
WKT serialization supported
Expressed in WGS84
 Spatial predicates represented as SPARQL
filter functions
OGC-SFA functionality exposed
Brodt et al.
11

Focus on spatial query
processing and spatial
indexing techniques for
spatial selections
e.g. "Retrieve features
located inside a given
polygon"
Naive spatial selection
operator
Placed in front of the execution
plan which the planner
returns
Spatial index
(R-Tree) implemented
Only utilized in spatial
selections
Brodt et al. - Implementation
12
Available upon request

 Built on top of Oracle 10g
 Implemented at Wright State University
 Implementation of SPARQL-ST
Upper-level ontology imposed
 Geometries expressed according to GeoRSS GML
 Spatial and temporal variables introduced
 Spatial and temporal filters used to filter results with
spatiotemporal constraints
RCC-8 calculus
Allen’s interval calculus
Perry
13

 Spatiotemporal operators implemented using Oracle's
extensibility framework
 Three spatial operators defined
 Strictly RDF concepts implemented using Oracle’s RDF
storage and inferencing capabilities
 R-Tree used for indexing spatial objects
Perry - Implementation
14
Available upon request

 AllegroGraph
 OWLIM
 Virtuoso
 uSeekM
Commercial RDF Stores
15

 Well-known RDF store, developed by Franz Inc.
 Two-dimensional point geometries
Cartesian / spherical coordinate systems supported
 GEO operator introduced for querying
Syntax similar to SPARQL’s GRAPH operator
Available operations:
Radius / Haversine (Buffer)
Bounding Box
Distance
 Linear Representation of data
 X and Y ordinates of a point are combined into a single datum
 Distribution sweeping technique used for indexing
• Strip-based index
 Closed source, available from
http://www.franz.com/agraph/allegrograph/
AllegroGraph
16

 Semantic Repository, developed by Ontotext
 Two-dimensional point geometries supported
Expressed using W3C Geo Vocabulary
Point Geometries
WGS84
 Spatial predicates represented as property functions
Available operations:
Point-in-polygon
Buffer
Distance
 Implemented as a Storage and Inference Layer for Sesame
 Custom spatial index used
 Closed Source
Free version available for evaluation purposes
http://www.ontotext.com/owlim
OWLIM
17

 Multi-model data server, developed by OpenLink
 Two-dimensional point geometries
Typed literals
WKT serialization supported
Multiple CRS support
 Spatial predicates represented as functions
Subset of SQL/MM supported
 R-Tree used for indexing
 Spatial capabilities firstly included in Virtuoso 6.1
 Closed Source
Open Source Edition available from
http://virtuoso.openlinksw.com/
Does not include the spatial capabilities extension
Virtuoso
18

 Add-on library for Sesame-enabled semantic repositories,
developed by OpenSahara
 Geometries expressed according to OGC-SFA
WKT serialization
Only WGS84 supported
 Spatial predicates represented as functions
OGC-SFA functionality exposed
Additional functions
e.g. shortestline(geometry,geometry)
 Implemented as a Storage and Inference Layer (SAIL) for Sesame
May be used with RDF stores that have a Sesame Repository/SAIL layer
 R-tree-over-GiST index used (provided by PostGIS)
 Open Source, Apache v2 License
 Available from https://dev.opensahara.com/projects/useekm
uSeekM
19

System Language Index Geometries CRS support Comments on
Functionality
Strabon stSPARQL/
GeoSPARQL*
R-tree-over-
GiST
WKT / GML
support
Yes • OGC-SFA
• Egenhofer
• RCC-8
Parliament GeoSPARQL R-Tree WKT / GML
support
Yes •OGC-SFA
•Egenhofer
•RCC-8
Brodt et al.
(RDF-3X)
SPARQL R-Tree WKT support No OGC-SFA
Perry SPARQL-ST R-Tree GeoRSS
GML
Yes RCC-8
AllegroGraph Extended
SPARQL
Distribution
sweeping
technique
2D point
geometries
Partial •Buffer
•Bounding Box
•Distance
OWLIM Extended
SPARQL
Custom 2D point
geometries
(W3C Basic Geo
Vocabulary)
No •Point-in-polygon
•Buffer
•Distance
Virtuoso SPARQL R-Tree 2D point
geometries
(in WKT)
Yes SQL/MM
(subset)
uSeekM SPARQL R-tree-over
GiST
WKT support No OGC-SFA

21
Conclusions
 Semantic Geospatial Systems:
 Research Prototypes
 Commercial Systems
 Next topic: Geospatial information with
description logics, OWL and rules

Bibliography
22
[Kyzirakos et al, 2010]
K. Kyzirakos , M. Karpathiotakis, M. Koubarakis: Developing
Registries for the Semantic Sensor Web using stRDF and stSPARQL
(short paper). In: Proceedings of the 3rd International Workshop
on Semantic Sensor Networks (SSN10) (2010)
[Kyzirakos et al, 2012]
K. Kyzirakos , M. Karpathiotakis, M. Koubarakis: Strabon: A Semantic
Geospatial DBMS. In: Proceedings of the 11th International
Semantic Web Conference (2012)
[Battle and Kolas, 2011]
Battle, R., Kolas, D.: Enabling the Geospatial Semantic Web with
Parliament and GeoSPARQL (2011)

Bibliography
23
[Brodt et al, 2010]
A. Brodt, D. Nicklas, and B. Mitschang. Deep integration of spatial
query processing into native rdf triple stores. In ACM SIGSPATIAL,
2010.
[Perry, 2007]
Matthew Perry. A Framework to Support Spatial, Temporal and
Thematic Analytics over Semantic Web Data. PhD thesis, Wright
State University, 2008

Geospatial Information with
Description Logics, OWL, and
Rules
Presenter: Charalampos Nikolaou
Reasoning Web 2012
Summer School

Outline
§  Geospatial information with description logics
and OWL
§  OWL reasoners with geospatial capabilities
§  Geospatial information with SWRL rules
2

Geospatial information with DLs and
OWL
Three main approaches:
1.  Use a DL as it is
2.  Define a spatial DL (concrete domain approach)
3.  Hybrid: OWL + Spatial ABox
3

Use a DL as it is
4

Use a DL as it is
5
Use OWL-DL
§  Regions are represented by concepts
§  Points are represented by individuals
§  RCC-8 relations among regions expressed by DL axioms
Translation of PO(X, Y) as
X
Y
Z1 Z3 Z2
TBox
ABox

Use a DL as it is
7
Use OWL-DL
Discussion
§  Impractical when implemented in a reasoner
[Stocker-Sirin, OWLED’09]
§  Unnatural modeling?
§  Can we generalize the approach?
§  For example, can we define the concept of a dream house as one
that is located inside a forest?
§  How do we express disjunctions of RCC-8 relations (indefinite
information)?

Define a spatial DL
(concrete domain approach)
11

Concrete domains
§  Reason about specific domains
(real numbers, time intervals, spatial regions)
§  Formalization of a concrete domain using a first-order
theory
§  From roles to features: associate an individual to a
value from a concrete domain
§  Notation:
12

Concrete domains
Examples:
§  Reals with order ( )
Domain: the set of real numbers
Predicates: < interpreted by the “less-than” relation
§  Allen’s Interval Calculus
Domain: the set of time intervals
Predicates: Allen’s basic interval relations (before, starts, etc.)
and Boolean combinations of them
§  RCC-8 Calculus
Domain: the set of non-empty, regular closed subsets of
Predicates: basic RCC-8 relations (EQ, PO, etc.) and Boolean
combinations of them
13

TBox
Concept equivalences/inclusions can include features and
concrete domain predicates
ABox
Assertions can associate an individual to values from a
concrete domain
14
Concrete domains

Two state of the art approaches
§  : with RCC-8 calculus as the concrete domain
§  extension of model-theoretic semantics of
§  ω-admissibility property
§  tableau-based technique
15
Concrete domains

Two state of the art approaches
§  : with RCC-8 calculus as the concrete domain
§  extension of model-theoretic semantics of
§  ω-admissibility property
§  tableau-based technique
§  : DL-Lite with RCC-8 calculus as the concrete
domain
§  extension of model-theoretic semantics of DL-Lite
§  FOL-rewritability for unions of conjunctive queries
16
Concrete domains

An Example
§  DreamHouse
One that is located inside a pine forest and borders a lake
17

An Example
§  DreamHouse
18

An Example
§  DreamHouse
19

An Example
§  DreamHouse
20

An Example
§  DreamHouse
21

An Example (classification)
§  ABox
22

§  ABox
§  Question: Is individual h a DreamHouse?
23

§  ABox
§  Answer: Yes.
24

§  ABox
§  Answer: Yes.
§  Why?
25
☐
☐

§  ABox
26
☐
☐

§  ABox
27
☐
☐

§  ABox
28
☐
☐

§  ABox
29
☐
☐

Hybrid: OWL + Spatial ABox
30

31
General architecture
KB
TBox ABox
DL
DL Reasoning

32
KB
TBox ABox
DL
DL Reasoning

33
KB
TBox ABox
DL
DL Reasoning
Spatial
ABox
Spatial Reasoning

34
1. Grutter et al.
2. Reasoner RacerPro (DL/OWL + Spatial ABox)
3. Reasoner PelletSpatial (DL/OWL + Spatial ABox)

35
Domain Knowledge (TBox)
§  Introduction of roles (e.g., partiallyOverlaps) for RCC relations
(e.g., PO)
§  spatiallyRelated: top role for topological relations
§  Role inclusion axioms for RCC relations
Assertions (ABox)
§  Assertion of the “connectsWith” relation, connectsWith(a, b),
between two regions (individuals)
[Grütter et al., ISWC‘08]

36
RCCBox
§  Definition of RCC relations based on the “connectsWith” relation
§  Axioms for composition tables of RCC
Predicate C(x, y)
corresponds to role
connectsWith(x, y) in ABox

38
Application
1.  Input: a set of geometries (polygons in )
2.  Compute assertions of the form connectsWith(a, b)
3.  Update ABox with new spatial relations according to definitions in RCCBox
1.  Should DC(a, b) be inferred in RCCBox, then
2.  the role assertion disconnectedWith(a, b) is inserted in ABox
4.  Check spatial consistency of ABox using path consistency on the RCC network
constructed from the spatial role assertions of the ABox

The reasoner RacerPro
39
§  Description Logic:
§  Spatial Extension: the ABox is associated to a spatial
representation layer (RCC substrate)
§  RCC substrate: offers representation and querying facilities for
RCC networks
Features
§  Representation of indefinite information: disjunctions of RCC
relations can be used between two individuals
§  Consistency checking of RCC networks
§  Querying of asserted and entailed RCC relations using the query
language nRQL
[Möller et al.][Wessel-Möller, JAPLL’09]
Available from
http://www.racer-systems.com/products/racerpro/

40
§  Spatial regions: a, b, and c
§  Region a contains b
(rcc-related a b ((:ntppi :tppi)))
§  Region a is disjoint with c
(rcc-related a c (:dc))
bX
a
c
bX
a
RacerPro: ABox Reasoning

41
(?) Which regions are disjoint?
bX
a
c
bX
a

42
?(retrieve (?x ?y) (and (?x ?y :dc)))
bX
a
c
bX
a

43
?(retrieve (?x ?y) (and (?x ?y :dc)))
(a, c) and (c, b)
bX
a
c
bX
a

Dream House (definition)
§  DreamHouse
44

§  DreamHouse
45
(implies DreamHouse
(and
(all hasForest PineForest)
(all hasLake Lake)))

§  DreamHouse
46
(implies DreamHouse
(and
(all hasForest PineForest)
(all hasLake Lake)))
?

§  ABox
47
Dream House (ABox reasoning)

§  ABox
§  Question: What are the houses that are threatened?
48

§  ABox
§  Answer: House h.
49

§  ABox
§  Answer: House h.
§  Why?
50

51

52
NTPP

53
NTPP
EC

54
NTPP
NTPP
EC

55
NTPP
EC
Composition of edge
(vh, vn) and (vn, vf)
NTPP

56
NTPP
EC
Composition of edge
(vh, vn) and (vn, vf)
NTPP

57
NTPP
PO, TPP, NTPP
EC
NTPP

58
NTPP
PO, TPP, NTPP
EC
NTPP

The reasoner PelletSpatial
59
§  Description Logic: OWL 2 ( )
§  Spatial Extension: Separate ABox for spatial data
§  Spatial ABox: Topological relations are managed as a basic
RCC-8 network (a single relation between two nodes)
Features
§  Representation of definite information only
§  Consistency checking of basic RCC-8 networks (path consistency)
§  Querying of asserted and entailed basic RCC-8 relations using a
subset of SPARQL (BGPs and operator AND)
[Stocker-Sirin, OWLED‘09]
Available from
http://clarkparsia.com/pellet/spatial

SWRL Rules
60

Geospatial information with SWRL
rules
61
Extension of OWL for the representation of qualitative and
quantitative spatial information (SOWL)
§  RCC-8
§  Directional relations (e.g., East, North-West), and
§  Distance relations (e.g., “3Km away from Vienna”)
[Batsakis et al., RuleML’11]

Modeling
rules
62
Footprint
Polyline MBRLinePoint
X Y Xmin
Ymin
Xmax
Ymax
Reg2
DistanceReg1-
Reg2
Reg1
3
Location
WestOf
class
instance
datatype
subclass
property
Legend

rules
63
Spatial assertions
§  RCC-8 relations between two regions
§  Directional relations between two regions
§  Distance relations between two regions
§  Geometry of regions (in subclasses of Footprint)

rules
64
Implementation of the previous framework using OWL
1. OWL 2 property axioms for expressing inverse, symmetry, and transitivity
for spatial relations

rules
65
2. SWRL rules to
§  encode composition of spatial relations
§  compute the intersection of two sets of spatial relations
§  check spatial consistency (using Pellet)

rules
66
2. SWRL rules to

rules
67
2. SWRL rules to
denotes disjunction of
relations DC and EC

rules
68
2. SWRL rules to
relations DC and EC

rules
69
2. SWRL rules to
relations DC and EC

rules
70
2. SWRL rules to
Current relation between regions x and y
relations DC and EC

rules
71
2. SWRL rules to
Composition of Rj with Rk
relations DC and EC

rules
72
2. SWRL rules to
Composition of Rj with Rk
New relation between x and y
relations DC and EC

rules
73
§  Implementation of SOWL is available at
http://www.intelligence.tuc.gr/prototypes.php

Conclusions
75
§  We talked about
§  Geospatial information with description logics and
OWL
§  OWL reasoners with geospatial capabilities
§  Geospatial information with SWRL rules
§  Next topic: conclusions, questions, discussion

Bibliography
76
[Katz et al., OWLED’05]
Yarden Katz, Bernardo Cuenca Grau: Representing Qualitative Spatial
Information in OWL-DL. OWLED 2005
[Lutz-Milicic, JAR‘07]
Carsten Lutz, Maja Milicic: A Tableau Algorithm for Description Logics
with Concrete Domains and General TBoxes. J. Autom. Reasoning
(JAR) 38(1-3):227-259 (2007)
[Özçep-Möller, DL‘12]
Özgür L. Özçep, Ralf Möller: Combining DL-Lite with Spatial Calculi
for Feasible Geo-thematic Query Answering. Description Logics 2012
Rolf Grütter, Thomas Scharrenbach, Bettina Bauer-Messmer:
Improving an RCC-Derived Geospatial Approximation by OWL
Axioms. International Semantic Web Conference 2008:293-306

Bibliography
77
[Wessel-Möller, JAPLL’09]
Michael Wessel, Ralf Möller: Flexible software architectures for
ontology-based information systems. J. Applied Logic (JAPLL) 7(1):
75-99 (2009)
[Stocker-Sirin, OWLED‘09]
Markus Stocker, Evren Sirin: PelletSpatial: A Hybrid RCC-8 and RDF/
OWL Reasoning and Query Engine. OWLED 2009
[Batsakis-Petrakis, RuleML’11]
Sotiris Batsakis, Euripides G. M. Petrakis: SOWL: A Framework for
Handling Spatio-temporal Information in OWL 2.0. RuleML Europe
2011:242-249

Conclusions
Reasoning Web 2012
Summer School

2
What we talked about
 Introduction
 Background in geospatial data modeling
 Geospatial data in the Semantic Web
(extensions to RDF, stSPARQL and
GeoSPARQL, spatial DLs, rules)
 Implemented systems (RDF stores,
spatial DL reasoners, rule-based)

3
What we did not talk about: Tools
• Tools for translating GIS data (e.g.,
shape files or tables from a geospatial
DBMS) into the geospatial extensions of
RDF that we presented.

4
What we did not talk about:
Representational issues
• What are appropriate vocabularies and
ontologies for representing geospatial
information? (GeoSPARQL only)
• Is the GeoSPARQL vocabularies/ontologies
always appropriate?
• Is using the WKT/GML encoding of a spatial
object always a good idea?

5
What we did not talk about: Theory
• Semantics: How do we extend the semantics
of SPARQL, to give semantics to stSPARQL and
GeoSPARQL?
• Computational complexity of query
processing: What is the complexity of
stSPARQL or GeoSPARQL querying?
• Same questions for DLs, OWL and rules.

6
Some open research questions
• More efficient geospatial RDF stores (can you beat
Strabon?)
• Federations of geospatial RDF stores
• More expressive/efficient spatial DL reasoners
• Theory (extensions of SPARQL, extensions of DLs,
extensions of SWRL)
• OWL 2 and geospatial (e.g., new data types)
• More efficient SWRL+spatial implementations

7
Thank you for Attending!
• Questions?
• Feedback?

Geo Data Models and Query Languages

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