ESWC2015 - Tutorial on Publishing and Interlinking Linked Geospatial Data

Publishing and Interlinking
Linked Geospatial Data
Tutorial in Conjunction with the
12th Extended Semantic Web Conference
http://event.cwi.nl/eswc2015-geo/

Tutorial organization
9:00-9:15 Introduction
9:15-10:30 Background in geospatial data modeling, representing
geospatial information in the Semantic Web, and querying linked
geospatial data.
10:30-11:00 coffee break
11:00-12:00 Publishing geospatial information as RDF graphs
12:00-12:30 Discovering Spatial and Temporal Links among RDF graphs
12:30-14:00 Lunch break
14:00-14:30 Discovering Spatial and Temporal Links among RDF graphs
14:30-15:30 Hands-on session: Publishing geospatial information as RDF
graphs
15:30-16:00 coffee break
16:00-17:00 Hands-on session: Discovering Spatial and Temporal Links
among RDF graphs
17:00-17:10 Conclusions
http://event.cwi.nl/eswc2015-geo/

Part 1:
Background in geospatial data
modeling
ESWC 2015 Tutorial
Publishing and Interlinking Linked Geospatial Data
Dept. of Informatics and Telecommunications
National and Kapodistrian University of Athens

ESWC 2015 Tutorial 2
Outline
• Basic GIS concepts and terminology
• Representing geometries
• Representing topological information
• Geospatial data standards

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

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

• 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 regional units (e.g., Heraklion)
• Each regional unit consists of municipalities (e.g.,
Dimos Chersonisou)
• …

• 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 regional unit 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

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.

Encoding Geometries: Vector
Representation
• In this encoding 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.

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

Encoding Geometries: 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

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.

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.

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)

DE-9IM
• The dimensionally extended 9-intersection model
(DE-9IM) of Clementini and Felice.
• It is based on the point-set topology of R2.
• It deals with simple, closed and connected
geometries (areas, lines, points).
• It is an extension of earlier approaches: the 4-
intersection (4IM) and 9-intersection (9IM)
models by Egenhofer and colleagues.

Topological Relations in 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).

Example
I(C) B(C) E(C)
I(A) -1 -1 2
B(A) -1 -1 1
E(A) 2 1 2
A
C

Topological Relations in DE-9IM
• The following five named relationships between two different
geometries can be distinguished: disjoint, touches, crosses,
within and overlaps.
• The named relationships have a reasonably intuitive meaning
for users. They are jointly exclusive and pairwise disjoint
(JEPD).
• The model can also be defined using an appropriate calculus of
geometries that uses these 5 binary relations and boundary
operators.

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 }

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***

DE-9IM Relation Definitions

The Region Connection Calculus (RCC)
• The primitives of the calculus are spatial regions. These are
non-empty, regular closed 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.

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

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.

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.

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

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.

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.

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.

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

Coordinate Reference Systems
(cont’d)
• There are well-known ways to translate between co-
ordinate reference systems.
• See the list of coordinate reference systems of the
European Petroleum Survey Group: http://www.epsg-
registry.org/

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 Features Access

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.

WKT Class Hierarchy

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))
)

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.

GML Simple Features:
Class Hierarchy

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>

OpenGIS Simple Features Access
• 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.

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.

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

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

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

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

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.

• The book Geographic Information Systems and Science is a nice introduction to GIS. See:
http://eu.wiley.com/WileyCDA/WileyTitle/productCd-EHEP001475.html
• The following papers present the DE-9IM model:
Eliseo Clementini, Paolino Di Felice and Peter van Oosterom.
A Small Set of Formal Topological Relationships Suitable for End-User Interaction. SSD
1993: 277-295
http://link.springer.com/chapter/10.1007%2F3-540-56869-7_16
E. Clementini and P. Felice. A Comparison of Methods for Representing Topological
Relationships. Information Sciences 80 (1994), pp. 1-34.
http://www.sciencedirect.com/science/article/pii/106901159400033X The paper
• The paper below surveys a lot of interesting results on the RCC calculus:
J. Renz, B. Nebel, Qualitative Spatial Reasoning using Constraint
Calculi, in: M. Aiello, I. Pratt-Hartmann and J. van Benthem (eds.),
Handbook of Spatial Logics, pp. 161–215, 2007, Springer.
http://users.cecs.anu.edu.au/~jrenz/papers/renz-nebel-los.pdf
• The two OGC standards mentioned in the slides.
Readings

Part 2:
Spatial and Temporal Data in RDF:
stRDF/stSPARQL and GeoSPARQL
ESWC 2015 Tutorial
Dept. of Informatics and Telecommunications
National and Kapodistrian University of Athens

Common Approach
• The two proposals (stRDF/stSPARQL and
GeoSPARQL) offer constructs for:
o Developing ontologies for spatial
and temporal data.
o Encoding spatial and temporal
data that use these ontologies in
RDF.
o Extending SPARQL to query spatial
and temporal data.

Two Proposals
• stRDF/stSPARQL
• GeoSPARQL

The data model stRDF
 An extension of RDF for the representation of
geospatial information that changes over
time.
 Geospatial dimension:
 Spatial data types are introduced.
 Geospatial information is representing using
spatial literals of these datatypes.
 OGC standards WKT and GML are used for
the serialization of spatial literals.
 Temporal dimension (later)
 Proposed independently and around the same time
as GeoSPARQL (starting with an ESWC 2010 paper
by Koubarakis and Kyzirakos).
[ Kyzirakos, Karpathiotakis
& Koubarakis 2012 ]

strdf:geometry rdf:type rdfs:Datatype;
rdfs:subClassOf rdfs:Literal.
strdf:WKT rdf:type rdfs:Datatype;
rdfs:subClassOf strdf:geometry.
strdf:GML rdf:type rdfs:Datatype;
rdfs:subClassOf strdf:geometry.
Spatial Datatypes

Example Ontology: Administrative
Geography of Greece
Geometry
property
strdf:geometry
strdf:GML strdf:WKT

Example Ontology: Administrative
Geography of Greece
strdf:geometry
strdf:GML strdf:WKT
Geometry
property

Example Data in stRDF
gag:Olympia
gag:name "Ancient Olympia";
rdf:type gag:MunicipalCommunity .
Spatial
data type
gag:Olympia gag: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
Coordinate
Reference
System
Geometry
Property

ESWC 2015 Tutorial
gag:Olympia
rdf:type gag:MunicipalCommunity;
gag:population "184"^^xsd:int;
gag:hasGeometry "POLYGON
(((25.37 35.34,…)))"^^strdf:WKT.
gag:OlympiaMUnit
rdf:type gag:MunicipalityUnit;
gag:name "Municipality Unit of
Ancient Olympia".
gag:OlympiaMunicipality
rdf:type gag:Municipality;
gag:name "Municipality of
Ancient Olympia".
gag:Olympia gag:belongsTo gag:OlympiaMUnit .
gag:OlympiaMUnit gag:belongsTo gag:OlympiaMunicipality.
9
Example (cont’d)

More Examples
 Corine Land Use/Land Cover
(http://www.eea.europa.eu/publications/COR0-landcover )
 Burnt Area Products (project TELEIOS,
http://www.earthobservatory.eu/ )

Corine Land Use/Land Cover

Corine Land Use/Land Cover in stRDF
(http://www.linkedopendata.gr )
clc:Area_24015134
rdf:type clc:Area ;
clc:hasCode "312"^^xsd:decimal;
clc:hasID "EU-203497"^^xsd:string;
clc:hasArea_ha "255.5807904"^^xsd:double;
clc:hasGeometry "POLYGON((15.53 62.54,
…))"^^strdf:WKT;
clc:hasLandUse clc:ConiferousForest .
Geometry
Property

Burnt Area Products
(http://www.earthobservatory.eu/ontologies/noaOntology.owl)

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;
noa:hasGeometry "MULTIPOLYGON(((
393801.42 4198827.92, ..., 393008 424131)));
<http://www.opengis.net/def/crs/
EPSG/0/2100>"^^strdf:WKT.
Geometry
Property

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)
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)

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

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:convexHull(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, xsd:anyURI units)
 Spatial metric functions
xsd:float strdf:distance(strdf:geometry A, strdf:geometry B, xsd:anyURI units)
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)

Select clause
 Construction of new geometries (e.g., strdf:buffer(?geo, 0.1, uom:metre))
 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)

stSPARQL: An example (1/3)
SELECT ?name
WHERE {
?comm rdf:type gag:LocalCommunity;
gag:name ?name;
gag:hasGeometry ?commGeo .
?ba rdf:type noa:BurntArea;
noa:hasGeometry ?baGeo .
FILTER(strdf:overlaps(?commGeo,?baGeo))
}
Spatial
Function
Return the names of local communities that have
been affected by fires

SELECT ?ba ?baGeom
WHERE {
?r rdf:type clc:Region;
clc:hasGeometry ?rGeom;
clc:hasCorineLandUse ?f.
?f rdfs:subClassOf clc:Forest.
?c rdf:type gag:LocalCommunity;
gag:hasGeometry ?cGeom.
noa:hasGeometry ?baGeom.
FILTER( strdf:intersects(?rGeom,?baGeom) &&
strdf:distance(?baGeom,?cGeom,uom:metre) < 200)}
Spatial
Functions
Find all burnt forests near local communities

ESWC 2015 Tutorial
Spatial
Function
21
SELECT ?burntArea
(strdf:intersection(?baGeom,
strdf:union(?fGeom))
AS ?burntForest)
WHERE {
?burntArea rdf:type noa:BurntArea;
noa:hasGeometry ?baGeom.
?forest rdf:type clc:Region;
clc:hasLandCover clc:ConiferousForest;
clc:hasGeometry ?fGeom.
FILTER(strdf:intersects(?baGeom,?fGeom))
}
GROUP BY ?burntArea ?baGeom
Compute the parts of burnt areas that lie in
coniferous forests.
Spatial
Aggregate

ESWC 2015 Tutorial
Time dimensions in Linked Data
User-defined time: A time value (literal) with no special
semantics.
Valid time: The time when a fact (represented by a triple) is true
in the modeled reality.
Transaction time: The time when the triple is current in the
database.

ESWC 2015 Tutorial
The time dimension of stRDF: The valid
time of triples
The following extensions are introduced in stRDF:
• Timeline: the (discrete) value space of the datatype xsd:dateTime of
XML-Schema
• Two kinds of time primitives are supported: time instants and time periods.
• A time instant is an element of the time line.
• A time period is an expression of the form [B, E) or [B, E] or (B, E] or (B, E) where B and E
are time instants called the beginning and ending time of the period.
• The new datatype strdf:period is introduced.
23
rdfs:Literal
strdf:WKT strdf:GML
strdf:period
strdf:geometry

ESWC 2015 Tutorial
The time dimension of stRDF (cont’d)
• Triples are extended to quads.
• A temporal triple (quad) is an expression of the form
s p o t.
where s p o. is an RDF triple and t is a time instant or time
period called the valid time of the triple.
• The temporal constants NOW and UC (“until changed”) are
introduced.
24

ESWC 2015 Tutorial
An example with valid time
25
Forest

Forest
clc:region1 clc:hasLandCover clc:Forest
"[2006-08-25T11:00:00+02, "UC")"^^strdf:period .

ESWC 2015 Tutorial
27
Forest
"[2006-08-25T11:00:00+02, "UC")"^^strdf:period .
Burnt area

Forest Burnt area
noa:ba1 rdf:type noa:BurntArea
"[2007-08-25T11:00:00+02, "UC")"^^strdf:period .
"[2006-08-25T11:00:00+02, "UC")"^^strdf:period .

Forest Burnt area
"[2007-08-25T11:00:00+02, "UC"))"^^strdf:period .
"[2006-08-25T11:00:00+02,2007-08-25T11:00:00+02)"^^strdf:period .

Forest Burnt area Agricultural
area
clc:region1 clc:hasLandCover clc:AgriculturalArea
"[2009-08-25T11:00:00+02, "UC")"^^strdf:period .
"[2007-08-25T11:00:00+02,2009-08-25T11:00:00+02)"^^strdf:period .
"[2006-08-25T11:00:00+02,2007-08-25T11:00:00+02)"^^strdf:period .

ESWC 2015 Tutorial
The time dimension of stSPARQL
The following extensions are introduced:
• Triple patterns are extended to quad patterns (the last component is a temporal
term: variable or constant)
• Temporal extension functions are introduced:
• Allen's temporal relations (e.g., strdf:after)
• Period constructors (e.g., strdf:period_intersect)
• Temporal aggregates (e.g., strdf:maximalPeriod)
31

ESWC 2015 Tutorial
• Find the current land cover of all areas in the dataset
SELECT ?clc
WHERE {
?R rdf:type clc:Region .
?R clc:hasLandCover ?clc ?t1 .
FILTER(strdf:during ("NOW", ?t1))
}
Temporal extension function
Temporal constant
Example Query
32
Quad Pattern

Two Proposals
• stRDF/stSPARQL
• GeoSPARQL

GeoSPARQL
GeoSPARQL is an OGC standard.
Functionalities similar to stRDF/stSPARQL:
 Geometries are represented using literals of spatial datatypes.
 Literals are serialized using WKT and GML.
 The same families of functions are offered for querying geometries.
Functionalities beyond stSPARQL:
 High level ontologies inspired from GIS terminology.
 Topological relations can now be asserted as well so that reasoning and querying on
them is possible.
 A query rewriting mechanism.
Functionalities of stSPARQL that are not included in GeoSPARQL:
• Geospatial aggregate functions
• Temporal dimension

ESWC 2015 Tutorial
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

GeoSPARQL Core
Defines two top level
classes that can be used to
organize geospatial data.

GeoSPARQL Geometry Extension
Provides vocabulary for asserting and querying data
about the geometric attributes of a feature.

Example Ontology: Greek
Administrative Geography

Greek Administrative Geography

Example Data
gag:Olympia
gag:population "184"^^xsd:int;
geo:hasGeometry ex:polygon1.
ex:polygon1
rdf:type geo:Geometry;
geo:asWKT "http://www.opengis.net/def/crs/OGC/1.3/CRS84
POLYGON((21.5 18.5,23.5 18.5,
23.5 21,21.5 21,21.5 18.5))"
^^sf:wktLiteral.
Datatype from
Geometry
extension
Geometry
literal
Property from
Geometry
extension
Property from
Geometry
extension
Class from
Geometry
extension

Non-Topological Query Functions of the
Geometry Extension
 The following non-topological query functions are also offered:
 geof:distance
 geof:buffer
 geof:convexHull
 geof:intersection
 geof:union
 geof:difference
 geof:symDifference
 geof:envelope
 geof:boundary

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

ESWC 2015 Tutorial
gag:Olympia
gag:name "Ancient Olympia".
gag:OlympiaMUnit
rdf:type gag:MunicipalityUnit;
gag:name "Municipality Unit of
Ancient Olympia".
gag:OlympiaMunicipality
rdf:type gag:Municipality;
gag:name "Municipality of
Ancient Olympia".
gag:Olympia geo:sfWithin gag:OlympiaMUnit .
gag:OlympiaMUnit geo:sfWithin gag:OlympiaMunicipality.
44
Simple Features
topological
relation

GeoSPARQL: An example
SELECT ?m
WHERE {
?m rdf:type gag:MunicipalityUnit.
?m geo:sfContains gag:Olympia.
}
Find the municipality unit that contains
the community of Ancient Olympia
Simple Features
topological relation
Answer: ?m = gag:OlympiaMUnit

GeoSPARQL: An example
SELECT ?m
WHERE {
?m rdf:type gag:Municipality.
?m geo:sfContains gag:Olympia.
}
Find the municipality that contains the
community of Ancient Olympia
Answer?

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

The Geometry Topology Extension
• Offers vocabulary for querying topological
properties of geometry literals.
• Simple Features
• geof:relate
• geof:sfEquals
• geof:sfDisjoint
• geof:sfIntersects
• geof:sfTouches
• geof:sfCrosses
• geof:sfWithin
• geof:sfContains
• geof:sfOverlaps
• Egenhofer (e.g., geof:ehDisjoint)
• RCC-8 (e.g., geof:rcc8dc)

Example Query
SELECT ?name
WHERE {
?comm rdf:type gag:LocalCommunity;
gag:name ?name;
geo:hasGeometry ?commGeo .
geo:hasGeometry ?baGeo .
FILTER(geof:sfOverlaps(?commGeo,?baGeo))
}
Geometry Topology
Extension Function
Return the names of local communities that have
been affected by fires
Geometry
Extension
Property
Geometry
Extension
Property

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 gag:Athens and
gag: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
gag:Athens geo:sfWithin gag:Greece
 One possible implementation is to re-write a given SPARQL
query.

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

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

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

ESWC 2015 Tutorial
 Specifies the RDFS entailments that follow from the class and
property hierarchies defined in the other components e.g., the
Geometry Extension.
 Systems should use an implementation of RDFS entailment to
allow the derivation of new triples from those already in a graph.
54
GeoSPARQL RDFS Entailment Extension

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 .

ESWC 2015 Tutorial
Readings
56
• Material from the Strabon web site (http://strabon.di.uoa.gr ).
• The following tutorial paper which introduces to the topic of linked geospatial data:
M. Koubarakis, M. Karpathiotakis, K. Kyzirakos, C. Nikolaou and M. Sioutis. Data Models and
Query Languages for Linked Geospatial Data. Reasoning Web Summer School 2012.
http://strabon.di.uoa.gr/files/survey.pdf
• The following paper which introduces stSPARQL and Strabon:
K. Kyzirakos, M. Karpathiotakis and M. Koubarakis. Strabon: A Semantic Geospatial DBMS.
11th International Semantic Web Conference (ISWC 2012). November 11-15, 2012. Boston,
USA.
http://iswc2012.semanticweb.org/sites/default/files/76490289.pdf
• The following paper which introduces the temporal features of stSPARQL and
Strabon:
K. Bereta, P. Smeros and M. Koubarakis. Representing and Querying the Valid Time of
Triples for Linked Geospatial Data. In the 10th Extended Semantic Web Conference (ESWC
2013). Montpellier, France. May 26-30, 2013.
http://www.strabon.di.uoa.gr/files/eswc2013.pdf
• The GeoSPARQL standard found at http://www.opengeospatial.org/standards/geosparql

ESWC 2015 Tutorial
Readings (cont’d)
57
• The following paper which introduces the RDFi framework:
Charalampos Nikolaou and Manolis Koubarakis. Incomplete Information in RDF.
In the 7th International Conference on Web Reasoning and Rule Systems (RR
2013). Mannheim, Germany. July 27-29, 2013.
http://cgi.di.uoa.gr/~koubarak/publications/rr2013.pdf
• The following paper which introduces the benchmark Geographica:
G. Garbis, K. Kyzirakos and M. Koubarakis. Geographica: A Benchmark for
Geospatial RDF Stores. In the 12th International Semantic Web Conference
(ISWC 2013). Sydney, Australia. October 21-25, 2013.
http://cgi.di.uoa.gr/~koubarak/publications/Geographica.pdf

Publishing
geospatial information
as RDF graphs
Kostis Kyzirakos, Dimitrianos Savva

Outline
Mapping relational data to RDF graphs
Mapping non-relational data to RDF graphs
Geospatial Extensions for mapping geospatial data
to RDF graphs
Implemented Systems
Demonstration
2

Mapping relational data to RDF graphs
Sitecode Sitename ReleaseDate …
DE0916391 NTP S-H W 2011-01-27
DE1003301 DOGGERB
ANK
2011-01-27
ProtectedArea
?
Natura 2000 is an ecological network
designated under the Birds Directive and
the Habitats Directive which form the
cornerstone of the nature conservation
policy of the European Union.
http://ec.europa.eu/environment/nature/natura2000/index_en.htm
http://www.eea.europa.eu/data-and-maps/data/natura-6

Direct Mapping
W3C Recommendation from 2012
http://www.w3.org/TR/rdb-direct-mapping/
Relational tables are mapped to classes defined by
an RDF vocabulary.
Attributes of each table are mapped to RDF
properties that represent the relation between
subject and object resources.
Identifiers, class names, properties and instances
are generated automatically following the labels of
the input data. 4

Direct Mapping - Example
DE0916391 NTP S-H W 2011-01-27
DE1003301 DOGGERB
ANK
2011-01-27
ProtectedArea Protected
Area
xsd:string xsd:date
ReleaseDateSitename
@base <http://foo.example/DB/> .
@prefix rdf: <http://www.w3.org/1999/02-22-rdf-syntax-ns#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
<ProtectedArea/Sitecode=DE0916391> rdf:type <ProtectedArea> .
<ProtectedArea/Sitecode=DE0916391> <ProtectedArea#Sitename> "NTP S-H W" .
<ProtectedArea/Sitecode=DE0916391> <ProtectedArea#ReleaseDate>
"2011-01-27"^^xsd:date .
<ProtectedArea/Sitecode=DE1003301> <ProtectedArea#Sitename> "DOGGERBANK" .
<ProtectedArea/Sitecode=DE1003301> <ProtectedArea#ReleaseDate>
"2011-01-27"^^xsd:date .

The language R2RML
R2RML is a language for expressing customized
mappings from relational databases to RDF graphs
R2RML is a W3C Recommendation from 2012
http://www.w3.org/TR/r2rml/
R2RML mappings provide the user with the ability
to express the desired transformation of existing
relational data into the RDF data model, following a
structure and a target vocabulary that is chosen by
the user.
6

The language R2RML (cont’d)
LogicalTable
PredicateObject
Map
GraphMap
TriplesMap SubjectMap
ObjectMap
PredicateMap
RefObjectMap Join
TermMap
Constant
Column
Template
Child
Parent

A logical table can be
a relational table that is explicitly stored in the
database
an SQL view
an SQL select query
A triples map is a rule that defines how each
tuple of the logical table will be mapped to a set
of RDF triples. It consists of
a subject map
zero or more predicate-object maps.
8

A subject map is a rule that defines how to
generate the URI that will be the subject of each
generated RDF triple.
A predicate-object map consists of predicate maps
and object maps.
A predicate map defines the RDF property to be
used to relate the subject and the object of the
generated triple.
An object map defines how to generate the object
of the triple which originates from the current row
of the logical table.
9

Subject, predicate, object and graph maps are
term maps. A term map is a function that
generates an RDF term from a logical table.
Three types of term maps are defined:
constant-valued term maps
column-valued term maps
template-valued term maps
10

A referencing object map allows using the
subjects of another triples map as the objects
generated by a predicate-object map.
Optionally, it has one or more join condition
properties.
11
Predicate
ObjectMap
RefObjectMap
TriplesMap
JoinCondition
column name
column name
source: http://www.w3.org/TR/r2rml/#dfn-predicate-map
rr:child
rr:parent
rr:join
Condition*
rr:parent
TriplesMaprr:object
Map

The language R2RML – Example
DE0916391 NTP S-H W 2011-01-27
DE1003301 DOGGERB
ANK
2011-01-27
ProtectedArea Protected
Area
xsd:string
Site
name
@base <http://foo.example/DB/> .
<NaturaMapping>
rr:subjectMap [
rr:template "ProtectedArea/SiteCode={SiteCode}";
rr:class <ProtectedArea> ];
rr:predicateObjectMap [
rr:predicate ProtectedArea:SiteName;
rr:objectMap [ rr:column "SiteName"; ]; ] .
<ProtectedArea/Sitecode=DE0916391> <ProtectedArea#Sitename> "NTP S-H W" .
<ProtectedArea/Sitecode=DE1003301> <ProtectedArea#Sitename> "DOGGERBANK" .

<ogr:FeatureCollection>
<gml:featureMember>
<ogr:waterways fid="waterways.128">
<ogr:osm_id>8108139</ogr:osm_id>
<ogr:name>Lech</ogr:name>
<ogr:type>river</ogr:type>
<ogr:geometryProperty>
<gml:LineString>
<gml:coordinates>
10.9034096,47.7996669
10.9037025,47.8003338 …
</gml:coordinates>
</gml:LineString>
</ogr:geometryProperty>
</ogr:waterways>
</gml:featureMember>
</ogr:FeatureCollection>
Mapping non-relational data to RDF graphs
?
OpenStreetMap is a collaborative project
for publishing free maps of the world. OSM
maintains a community-driven global
editable map that gathers map data in a
crowdsourcing fashion.
http://www.openstreetmap.org/

RDF Mapping Language (RML)
 RML is a recently proposed mapping language that defines how to
map heterogeneous sources into RDF.
http://semweb.mmlab.be/rml/spec.html
 RML is defined as a superset of the W3C-standard R2RML
R2RML RML
Logical Table rr:logicalTable Logical Source rml:logicalSource
Table Name rr:tableName URI rml:source
column rr:column reference rml:reference
SQL Reference Formulation rml:referenceFormulation
per row iteration defined iterator rml:iterator
source: http://semweb.mmlab.be/rml/RML_R2RML.html

RML Overview
LogicalSource
PredicateObject
Map
GraphMap
ObjectMap
PredicateMap
RefObjectMap Join
Child
Parent
TermMap
Constant
Reference
Template
Source
Iterator
Reference
Formulation

RML extensions
A logical source refers to the input dataset that will
be converted to an RDF graph.
Each logical source has
 a source property pointing to input data
 a logical iterator that defines the iteration pattern over
the input data source
 an optional reference formulation property that defines
the query language that may be used (e.g., SQL2008,
XPath, JSONPath)
An RML reference is a term map that refers to a
column name (SQL, CSV), an XML element or
attribute, or an JSON object.

<gml:featureMember>
<ogr:name>Lech</ogr:name>
<ogr:type>river</ogr:type>
<gml:LineString>
<gml:coordinates>
10.9034096,47.7996669
10.9037025,47.8003338 …
</gml:coordinates>
</gml:LineString>
</ogr:waterways>
RML Example <#waterways>
rml:logicalSource [
rml:source "/home/leo/osm.gml";
rml:referenceFormulation ql:XPath;
rml:iterator "/ogr:FeatureCollection
/gml:featureMember
/ogr:waterways";
];
rr:subjectMap [
rr:template
"http://www.example.com/id/{@fid}";
rr:class onto:waterways;
];
rr:predicate onto:hasOgr-Name;
rr:objectMap [
rr:datatype xsd:string;
rml:reference "ogr:name";
]; ] .
ex_id:waterways.128 rdf:type onto:waterways ;
onto:hasOgr-Name "Lech" ;
onto:hasFid "waterways.128"^^xsd:ID ;
onto:hasOgr-Osm_id "8108139" ;
onto:hasOgr-Type "river" .

Mapping geospatial data to RDF graphs
Geospatial data are available in formats such
as:
• ESRI shape files
• KML documents
• GeoJSON documents
• XML documents
Geospatial data may also be stored in
spatially-enabled relational databases.

Extending R2ML with transformation-valued
term maps
LogicalTable
PredicateObject
Map
GraphMap
ObjectMap
PredicateMap
RefObjectMap Join
TermMap
Constant
Column
Template
Child
Parent
Function
Argument
Map
Argument
Map
Function

Extending RML with transformation-valued
term maps
LogicalSource
PredicateObject
Map
GraphMap
ObjectMap
PredicateMap
RefObjectMap Join
TermMap
Source
Iterator
Reference
Formulation
Constant
Column
Template
Child
Parent
Function
Argument
Map
Argument
Map
Function

Transformation-valued term maps
A transformation-valued term maps is a term map
that generates an RDF term by applying a SPARQL
extension function on one or more term maps.
A transformation-valued term map has
 exactly one rrx:function property that defines a
SPARQL extension function that performs the desired
transformation
 one rrx:argumentMap property that has as range an
rdf:List of term maps that define the arguments to
be passed to the transformation function

Transformation-valued term maps (cont’d)

Extending join conditions
Predicate
ObjectMap
RefObjectMap
TriplesMap
JoinCondition
column name
column name
rr:child
rrx:
function
rr:join
Condition*
rr:parent
TriplesMaprr:object
Map
rdf:List
IRIrefOr
Function
rr:parent
rrx:
argument
Map

Example
Sitecode Sitename Geometry …
DE0916391 NTP S-H W POLYGON((…))
DE1003301 DOGGERB
ANK
POLYGON((…))
ProtectedArea
Protected
Area
xsd:string
geo:hasGeometry
<NaturaGeometryMapping>
rr:subjectMap [
rr:template "ProtectedArea/Geometry/SiteCode={SiteCode}";
rr:class geo:Geometry ];
rr:predicate geo:dimension;
rr:objectMap [
rrx:function strdf:dimension;
rrx:argumentMap ( [rr:column "`Geom`"] ); ]; ] .
<ProtectedArea/Geometry/Sitecode=DE0916391>
rdf:type <ProtectedArea> ;
geo:dimension "2"^xsd:integer .
geo:Geometry
geo:
dimension geo:asWKT
geo:wktLiteral

Example
Sitecode Sitename Geom …
DE0916391 NTP S-H W POLYGON((…))
DE1003301 DOGGERB
ANK
POLYGON((…))
ProtectedArea
<NaturaGeometryMapping>
rr:subjectMap [
rr:template
"ProtectedArea/Geometry/SiteCode={SiteCode}";
rr:class geo:Geometry ];
rr:predicate geo:sfIntersects;
rr:objectMap [
rr:parentTriplesMap <#waterwaysGeom> ;
rr:joinCondition [
rrx:function geof:intersection;
rrx:argumentMap (
[rr:column "`Geom`"] ;
[rml:reference "ogr:geometryProperty“;
rr:parentTriplesMap <#waterwaysGeom>]
); ] ; ]; ] .
natura:DE0916391 geo:sfIntersects osm-id:waterways.128 .
<gml:featureMember>
<gml:LineString>
<gml:coordinates>
10.9034096,47.7996669 …
</gml:coordinates>
</gml:LineString>
</ogr:waterways>
OSM Waterways
<#waterwaysGeom>
rml:logicalSource [
rml:source "/home/leo/osm.gml";
rml:referenceFormulation ql:XPath;
rml:iterator "/ogr:FeatureCollection
/gml:featureMember
/ogr:waterways";
];
rr:subjectMap [
rr:template
"http://www.osm.org/id/{@fid}";
rr:class onto:waterways;
].

Implemented Systems
 Direct Mapping processors:
 SquirellRDF
 R2RML processors:
 D2RQ Platform
 OpenLink Virtuoso
 Ultrawrap
 Morph
 Ontop
 Oracle
 RML processor
 Processor by iMinds Lab, Ghent University
 Other Mapping Language:
 Triplify
 Geospatial capabilities
 So far:
 Geometry2RDF
 Sparqlify
 TripleGeo
 GeoTriples
26

Custom
Mapping
Language
Direct
Mapping
R2RML RML
SPARQL
query
evaluation
Automatic
Mapping
Generation
Geospatial
support
OpenLink
Virtuoso
✔ ✖ ✔*
✖
✔ ✖ ✖
RDF-RDB2RDF ✖ ✔ ✔ ✖ ✖ ✖ ✖
D2RQ Platform ✔ ✖ ✔ ✖ ✔ ✔ ✖
Db2triples ✖ ✔ ✔ ✖ ✖ ✖ ✖
Morph ✔ ✖ ✔ ✖ ✔ ? ✖
Sparqlify ✔ ✖ ✖* ✖ ✔ ✖ ✔
Ontop ✔ ✖ ✔ ✖ ✔ ✖ ✖*
Ultrawrap ✔* ✔ ✔ ✖ ✔ ✖ ✖
Oracle ✖ ✔ ✔ ✖ ✔ ✔ ✖
Geometry2RDF ✖ ✔ ✖ ✖ ✖ ✖ ✔*
TriplesGeo ✖ ✔ ✖ ✖ ✖ ✖ ✔*
iMinds lab RML
processor
✖ ✖ ✔ ✔ ✖ ✖ ✖
GeoTriples ✖ ✖ ✔ (✔) ✖* ✔ ✔

Comparison of Geo2RDF tools
Direct
Mapping
R2RML RML
Automatic
Mapping
Generation
GeoSPARQL
compliance
RDBMS
ESRI
Shape
file
GML
Geo
JSON
Geometry2RDF ✔ ✖ ✖ ✖ ✖ ✔ ✖ ✖ ✖
TriplesGeo ✔ ✖ ✖ ✖ (✔) ✔ ✔ ✖ ✖
GeoTriples ✖ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔

GeoTriples
Open Source software
Released under Mozilla Public Licence v2.0
Available at:
https://github.com/LinkedEOData/GeoTriples
Extends the D2RQ Platform
Extends the iMinds lab RML processor
Provides both a graphical user interface and a
command line interface
29

Architecture of GeoTriples
30
Earth Obsevation
Acquisitions

Automatic generation
of R2RML mappings (cont’d)
Generate two triples maps for each table that has a
geometry column.
 Thematic triples map for the non-geometric information
 Spatial triples map for the geometric information
The spatial triples map contains multiple
transformation functions over the input geometries
in order to generate a GeoSPARQL compliant
dataset.
31
NaturaGeometryNaturaArea
geo:
hasGeometry
(rr:joinCondition)

Automatic generation of RML mappings
for GML documents
 Each geometric object is mapped to a geo:Geometry
instance
 For each geometric object we generate a set of predicate
object maps that use the appropriate transformation
functions for producing a GeoSPARQL compliant dataset
 Each simple element is mapped to a predicate object map
 Each non simple element is mapped to a triples map
 Appropriate mappings are generated for linking nested
elements
32
Mapping
GeneratorXSD
RML
mapping

Discovering Spatial and Temporal Links
among RDF Graphs
In Conjunction with the 12th Extended Semantic Web Conference
Portoroz, Slovenia, 1st June 2015
Presenter: Panayiotis Smeros

01/06/2015 Discovering Spatial and Temporal Links among RDF Graphs 2
Outline
• Introduction to Entity Resolution and Link
Discovery
– Examples, Definitions, Common Problems
• Spatial Entity Resolution
• Spatial and Temporal Link Discovery
– Background and Developed Methods
– Extensions to the Silk Framework
– Hands-on

Entities in Real-World
source
source
Most of our knowledge about the world is based on entities
and their relations:

Entities in Data-World
Portoroz Portorož ‫بورتوروز‬ Порторож Πορτορόζ
Portorose Портороз Порторожу Portorožu
Порторож
Portorož (Italian: Portorose, literally "Port of
Roses"), is an Adriatic - Mediterranean coastal
settlement in the Municipality of Piran in
southwestern Slovenia. Its modern development
began in the late 19th century with appearance of
first health resorts.
http://www.geonames.org/3192682/portoroz.html
http://en.wikipedia.org/wiki/Portoroz
http://www.portoroz.si/en/
…
source
Many names, descriptions or IDs (URIs) are used for the
same real-world entity:

Content Providers
News about Portoroz
Reviews of hotels in Portoroz
Pictures about Portoroz
Videos for Portoroz
Wiki pages about Portoroz
Social networks in Portoroz
Many applications provide valuable information about each of
these entities:

Content Providers
News about Portoroz
Reviews of hotels in Portoroz
Pictures about Portoroz
Videos for Portoroz
Wiki pages about Portoroz
Social networks in Portoroz
Many applications provide valuable information about each of
these entities:
Solution?

Entity Resolution
Problem of understanding that two (or more) entities in data-world
are references of the same real-world entity. [Christen, TKDE’11]

Entity Resolution (Example)
DBpedia
Entity
name = PORTOROZ
population = 2,849
GeoNames
Entity
name = Portorose
population = 2,851

Entity Resolution (Example)
DBpedia
Entity
name = PORTOROZ
population = 2,849
GeoNames
Entity
name = Portorose
population = 2,851
sameAs

Spatial Entity Resolution (Example)
DBpedia
Entity
name = PORTOROZ
population = 2,849
GeoNames
Entity
name = Portorose
population = 2,851
sameAs
location = 45.51663, 13.57996 location = 45.51661, 13.57998

Entity Resolution (Definition)
Let 𝑆 and 𝑇 be two sets of entities. We define a distance
(similarity) function 𝑑 𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 and a distance (similarity)
threshold 𝜃 𝑑 𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦
as follows:
𝑑 𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦: 𝑆 × T → [0,1] , 𝜃 𝑑 𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦
∈ 0,1
We define the set of discovered similarity links 𝐷𝐿 𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 as
follows:
𝐷𝐿 𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 = s, sameAs, t 𝑠 ∈ 𝑆 𝑡 ∈ 𝑇 𝑑 𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 𝑠, 𝑡 < 𝜃 𝑑 𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦
}

Link Discovery
Source Source
Link Discovery is the fourth and the most important Linked Data
Principle.
Establish semantic relations between entities in order to enrich the
information that is known about them. [Bizer et al., IJSWIS’06]

Link Discovery (Definition)
Let 𝑆 and 𝑇 be two sets of entities and 𝑅 the set of relations
that can be discovered between entities. For a relation 𝑟 ∈ 𝑅,
w.l.o.g., we define a distance function 𝑑 𝑟 and a distance
threshold 𝜃 𝑑 𝑟
as follows:
𝑑 𝑟: S × T → [0,1] , 𝜃 𝑑 𝑟
∈ 0,1
We define the set of discovered links for relation 𝑟 (𝐷𝐿 𝑟) as
follows:
𝐷𝐿 𝑟 = s, r, t 𝑠 ∈ 𝑆 𝑡 ∈ 𝑇 𝑑 𝑟 𝑠, 𝑡 < 𝜃 𝑑 𝑟
}

Link Discovery (Example)
Natura (2000) - Fields Fields - OSM Water Bodies

contains
intersects
Natura (2000) - Fields
Link Discovery (Example)
Fields - OSM Water Bodies

Main Problem: Heterogeneity
• Different Data Providers create Heterogeneous
Datasets
– Example: Literal Heterogeneity (case, language, etc).
• We focus on:
– Heterogeneity in the Representation of Geospatial
Information in RDF
– Heterogeneity in the Representation of Temporal
Information in RDF
name = PORTOROZ name = Portorose

Heterogeneity in the Representation of
Geospatial Information in RDF
_:1 rdf:type geo:Geometry .
_:1 geo:hasGeometry
"<http://www.opengis.net/def/crs/EPSG/0/4326>
POINT(10 20)"^^geo:wktLiteral .
_:1 rdf:type strdf:Geometry .
_:1 strdf:hasGeometry
"<gml:Point crsName="EPSG:2100"><gml:coordinates>10,20
</gml:coordinates></gml:Point>"^^strdf:GML .
_:1 rdf:type wgs84Geo:Point .
_:1 wgs84Geo:lat “10“^^xsd:double .
_:1 wgs84Geo:long “20“^^xsd:double .

_:1 geo:hasGeometry
• Different Vocabularies

_:1 geo:hasGeometry
• Different Serializations of Geometries

_:1 geo:hasGeometry
• Different Serializations of Geometries
• Geometries expressed in Different Coordinate
Reference Systems (CRS)

source

• Different Sampling Values
• Different Granularity
• Different Rounding Effects
source

Temporal Information in RDF
_:1 ex:hasBirthday "1989-09-
24T11:05:00+01:00"xsd:dateTime .
_:1 ex:hasAffiliation ex:UoA
"[2007-10-15T00:00:00+03:00,
2013-10-15T00:00:00+04:00)"^^strdf:Period .

24T11:05:00+01:00"xsd:dateTime .
"[2007-10-15T00:00:00+03:00,
2013-10-15T00:00:00+04:00)"^^strdf:Period .

24T11:05:00+01:00"xsd:dateTime .
"[2007-10-15T00:00:00+03:00,
2013-10-15T00:00:00+04:00)"^^strdf:Period .
• Different Time Zones

24T11:05:00+01:00"xsd:dateTime .
"[2007-10-15T00:00:00+03:00,
2013-10-15T00:00:00+04:00)"^^strdf:Period .
• Different Time Zones
• Time Instants and Periods

Outline
Discovery
– Hands-on

Spatial Entity Resolution (Example
Revisited)
DBpedia
Entity
name = PORTOROZ
population = 2,849
GeoNames
Entity
name = Portorose
population = 2,851
sameAs
location = 45.51663, 13.57996 location = 45.51661, 13.57998

Spatial Entity Resolution (1/4)
• Location Name Similarity
– Edit, Jaccard distance
• Location Similarity
– Euclidean distance
• Location Type Similarity
– (e.g. type “river” is similar to type “stream”)
Combines the above similarities to compute the
overall similarity between entities

• Similarity measure: Hausdorff Distance
– Intuitively Hausdorff Distance is defined as the
largest distance between the closest points of
two geometric shapes
• Handling Geospatial Heterogeneity
– Converts geometries to a common
vocabulary (NeoGeo)
– Assumes WGS-84 CRS
• Optimization
– Simplifies Geometries with Ramer-Douglas-Peucker algorithm

• Heuristic Combination of:
– URI Similarity
– Label Similarity
• Considering the language of the labels
– Location Similarity
• Assuming the W3C Geo vocabulary
– Geometric Similarity
• Minimum Distance between two Geometries

• Non-Spatial Criteria
– Implemented within the LIMES framework
• Geometric Similarity
– Hausdorff Distance
– Optimizations
• Bounding Circle: Avoids useless comparisons
μ(s, t) = δ(ζ(s), ζ(t)) − r (s) − r (t) > θ ⇒ δ(s, t) > θ
• Space tiling: Reduces the quadratic number of comparisons

Spatial Entity Resolution
• [Sehgal et al. GIS’06]
– Spatial and non-Spatial Criteria
– Only Location Similarity
• [Salas et al., TerraCognita’11]
– Only Spatial Criteria
– Complex Geometric Similarity Methods
• [Vilches-Blázquez et al., AGILE’12]
– Simple Geometric Similarity Methods
• [Ngonga Ngomo, ISWC’13]
– Complex Geometric Similarity Methods
– Reduced number of comparisons

Outline
Discovery
– Hands-on

Link Discovery (reminder)
Source Source
Link Discovery is the fourth and the most important Linked Data
Principle.
Establish semantic relations between entities in order to enrich the
information that is known about them. [Bizer et al., IJSWIS’06]

Background on Spatial Relations (1/2)
• Dimensionally Extended 9-Intersection Model
[Clementini et al., SSD'93]
– Captures topological relations in ℝ2, by considering the
dimension (dim) of the intersections involving the
interior (I), the boundary (B) and the exterior (E) of the
two geometries.
– Examples: Intersects, Equals, Touches, Disjoint,
Contains, Crosses, Covers, CoveredBy and Within

Background on Spatial Relations (2/2)
• Region Connection Calculus [Randell et al. KR’92]
– RCC-8: a well-known subset of RCC, which is based on
eight topological relations
– DC stands for DisConnected, EC for Externally
Connected, TPP for Tangential Proper Part, NTPP, for
Non Tangential Proper Part, and TPPi and NTPPi are
the inverse relations of TPP and NTPP

Background on Temporal Relations
• Allen’s Interval Calculus [Allen, Commun. ACM’83]
– thirteen jointly exclusive and pairwise disjoint qualitative
relations

Spatial and Temporal Relations
• We consider the previous Spatial (𝑅 𝑠) and Temporal (𝑅𝑡)
relations as Boolean relations (𝑅 𝐵) i.e., either they hold or
they do not:
𝑅 𝑠, 𝑅𝑡 ⊂ 𝑅 𝐵
• 𝑅 𝐵 constitutes a special subset of 𝑅. The distance function
𝑑 𝑟 and the distance threshold 𝜃 𝑑 𝑟
for a relation 𝑟 ∈ 𝑅 𝐵 are
defined as follows:
𝑑 𝑟(s,t) =
0 𝑖𝑓 𝑟 ℎ𝑜𝑙𝑑𝑠
1 𝑒𝑙𝑠𝑒𝑤ℎ𝑒𝑟𝑒
, 𝜃 𝑑 𝑟
= 1

Spatial and Temporal Transformations
(1/2)
• CRS Transformation. The geometries of a dataset can be expressed
in a Coordinate Reference System that is more precise for the
geographic area that they describe (e.g., the GGRS87 for Greece).
This transformation converts the CRS of a geometry to the World
Geodetic System (WGS 84)
• Vocabulary Transformation. This transformation converts geometry
literals from GeoSPARQL, stRDF or W3C GEO to a common
vocabulary (GeoSPARQL)
• Serialization Transformation. This transformation converts the
geometries of a dataset to a common serialization (WKT)
• Time-Zone Transformation. This transformation converts the time
zone of a given time interval to Coordinated Universal Time (UTC)
• Period Transformation. This transformation converts a time instant to
a period with the same starting and ending point

(2/2)
• Simplification Transformation. Some datasets have very complex
geometries, which makes the computation of spatial relations inefficient. This
transformation simplifies a geometry according to a given distance tolerance,
ensuring that the result is a valid geometry having the same dimension and
number of components as the input
• Envelope Transformation. This transformation computes the envelope (i.e.,
the minimum bounding rectangle) of a geometry and it is useful in cases that
we want to compute approximate spatial relations between two datasets
• Area Transformation. In some cases it is enough to compare just the areas of
two geometries to infer whether they are the same or not. This transformation
computes the area of a given geometry in square metres
• Points-To-Centroid Transformation. In crowdsourcing datasets like
OpenStreetMap, multiple users can define the position of the same placemark.
As a better approximation of the real position of this placemark we can
compute the centroid of these positions. This transformation computes the
centroid of a cluster of points

Techniques for Checking the Relations
• Cartesian Product Technique (Naive)
– Performs exhaustive checks between the pairs of the entities
of datasets
– Complete
– Complexity: O(|S||T|) checks
• Blocking Technique [Isele et al., WebDB’11, Papadakis et al, TKDE’13]
– Divides the entities into blocks
– Decreases the number of checks
– Complete
– Complexity: O(|S||T|) checks (worst case), O(|L|) checks
(best case)
* |S|, |T|: number of entities in datasets S and T; |L|: number of links between datasets S and T

Blocking Technique for Spatial Relations
• Divide the surface of the earth
into curved rectangles (blocks)
• Adjust the area of the blocks
with a blocking factor (bf)
(blockArea:
1
𝑏𝑓2
𝑜2
)
• If the MBB of a geometry spatially intersects with a block, then
insert it in this block
• Check for a spatial relation only within each block
(independently)
• Construct the set of discovered links (𝐷𝐿 𝑟) by aggregating the
respective links that have been discovered within each block

Blocking Technique for Temporal
Relations
• Divide the time into
intervals (blocks)
• Adjust the length of the
blocks with a blocking factor (bf)
(blockLength:
1
𝑏𝑓
𝑡𝑖𝑚𝑒 𝑢𝑛𝑖𝑡𝑠)
• If a time period or instant temporally intersects with a block, then
insert it in this block
• Check for a temporal relation only within each block
(independently)
• Construct the set of discovered links (𝐷𝐿 𝑟) by aggregating the
respective links that have been discovered within each block

Blocking Technique
• Fully parallelizable with respect to the blocks
• Proven sound and complete
• 100% accurate links
• 100% precision, recall, F-measure

Extensions to the Silk Framework:
Spatial and Temporal Relations
Silk

Silk
Extensions to the Silk Framework:

Extensions to the Silk Framework
• Spatial and Temporal Extensions for Silk implemented as
Plugins
• Transparent to all the applications of Silk
– Single Machine
– MapReduce
– Workbench
Silk

• Download: https://github.com/silk-framework/silk
• Workbench application pre-installed in the VM
• Discover the following links:
All the datasets will be first converted to RDF with GeoTriples!
Hands-on Silk
Source Dataset Relation Target Dataset
Field Boundaries Contains Raster Cells
OSM Water
Bodies
Intersects Natura (2000)
Natura (2000) Within Federal States of
Germany

References (1/3)
• [Bizer et al., IJSWIS’06]
Bizer, C., Heath, T., Berners-Lee, T.: Linked Data - The Story So Far. International
Journal on Semantic Web and Information Systems 5(3), 1–22 (2009)
• [Christen, TKDE’11]
P. Christen, " A survey of indexing techniques for scalable record linkage and
deduplication.” in IEEE TKDE 2011.
• [Auer, RW’13]
Auer, S., Lehmann, J., Ngomo, A.C.N., Zaveri, A.: Introduction to Linked Data and Its
Lifecycle on the Web. In: Rudolph, S., Gottlob, G., Horrocks, I., van Harmelen, F. (eds.)
Reasoning Web. Lecture Notes in Computer Science, vol. 8067, pp. 1–90. Springer
(2013)
• [Salas et al., TerraCognita’11]
Salas, J., Harth, A.: Finding spatial equivalences accross multiple RDF datasets. In:
Proceedings of the Terra Cognita Workshop on Foundations, Technologies and
Applications of the Geospatial Web. pp. 114–126. Citeseer (2011)
• [Sehgal et al. GIS’06]
Sehgal, V., Getoor, L., Viechnicki, P.D.: Entity resolution in geospatial data integration. In:
Proceedings of the 14th annual ACM international symposium on Advances in
geographic information systems. pp. 83–90. ACM (2006)

References (2/3)
• [Vilches-Blázquez et al., AGILE’12]
Vilches-Blázquez, L.M., Saquicela, V., Corcho, O.: Interlinking geospatial information in
the web of data. In: Bridging the Geographic Information Sciences, pp. 119–139.
Springer (2012)
• [Ngonga Ngomo, ISWC’13]
Ngonga Ngomo, A.C.: Orchid - reduction-ratio-optimal computation of geo-spatial
distances for link discovery. In: Proceedings of ISWC 2013 (2013)
• [Clementini et al., SSD'93]
Clementini, E., Di Felice, P., van Oosterom, P.: A small set of formal topological
relationships suitable for end-user interaction. In: Abel, D., Chin Ooi, B. (eds.) Advances
in Spatial Databases, Lecture Notes in Computer Science, vol. 692, pp. 277–295.
Springer Berlin Heidelberg (1993), http://dx.doi.org/10.1007/3-540-56869-7_16
• [Randell et al. KR’92]
Randell, D.A., Cui, Z., Cohn, A.G.: A spatial logic based on regions and connection. In:
KR. pp. 165–176 (1992)
• [Allen, Commun. ACM’83]
Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM 26(11), 832–
843 (Nov 1983)

References (3/3)
• [Isele et al., WebDB’11]
Isele, R., Jentzsch, A., Bizer, C.: Efficient multidimensional blocking for link discovery
without losing recall. In: WebDB. Citeseer (2011)
• [Papadakis et al, TKDE’13]
Papadakis, G., Ioannou, E., Palpanas, T., Niederée, C., Nejdl, W.: A blocking framework
for entity resolution in highly heterogeneous information spaces. Knowledge and Data
Engineering, IEEE Transactions on 25(12), 2665–2682 (2013)

Thanks for your attention!
Questions?

Transforming Natura2000
Shapefile into RDF
Kostis Kyzirakos and Dimitrianos Savva

GeoTriples GUI
• From terminal execute: geotriples-gui
1. Connect to Natura2000 Shapefile
2. Adjust class/predicate names to ontology
3. Generate mapping
4. Dump RDF

GeoTriples Layout
Mapping Builder (Left)
1. Adjust triples maps
2. Change DataTypes
3. Change Predicates
Bottom Toolbar
1. Generate Mapping
2. Select your preferred
geo-vocabulary
3. Define CRS
4. Select output format
5. Dump RDF
Mapping Editor (Right)
1. Change the mapping
by hand

Dump RDF
/home/leo/datasets/naturatriples.n3

Store RDF graph to Strabon
# endpoint store
http://localhost:8080/strabonendpoint
N-Triples -t
/home/leo/datasets/naturatriples.n3

Transforming OpenStreetMaps GML
document into an RDF graph (1/4)
# cd ~/DEMO_ESWC15
# ./osmmapping.sh
--
geotriples-cmd generate_mapping
-o OSM/automatic-mapping.rml.ttl
-b http://data.linkedeodata.eu/waterways
-r waterways
-rp /ogr:FeatureCollection/gml:featureMember
-ns "gml|http://www.opengis.net/gml,
ogr|http://ogr.maptools.org/"
-null -onto OSM/automatic-ontology.txt
-x OSM/osm_waterways.xsd OSM/osm_waterways.gml

# cp OSM/automatic-mapping.rml.ttl
OSM/altered-mapping.rml.ttl
# gedit OSM/altered-mapping.rml.ttl

1. Change the class definition for the triples map
<#ogr:waterwaysogr:geometryProperty>
1. Replace the class onto:LineStringPropertyType
with ogc:Geometry
2. Change the predicate that will link the thematic
data with the geometric data.
1. Find the triples map <#waterways>
2. Replace the text onto:has_geometryProperty
with ogc:hasGeometry

# ./osmdump.sh
--
geotriples-cmd dump_rdf -rml
-o OSM/osmtriples.n3
-ns osm-namespaces.ns
OSM/altered-mapping.ttl
--
# endpoint store
http://localhost:8080/strabonendpoint N-
Triples -t
/home/leo/DEMO_ESWC15/OSM/osmtriples.n3

Store TalkingFields datasets to Strabon
# endpoint store
N-Triples -t /home/leo/datasets/fb.n3
# endpoint store
N-Triples -t /home/leo/datasets/rc.n3

• Download: https://github.com/silk-framework/silk
• Workbench application pre-installed in the VM
• Discover the following links:
All the datasets will be first converted to RDF with GeoTriples!
Hands-on Silk
Source Dataset Relation Target Dataset
Field Boundaries Contains Raster Cells
OSM Water
Bodies
Intersects Natura (2000)
Natura (2000) Within Federal States of
Germany

Import the project that you will find in
the Desktop of the VM

Examing Generated Links
$ less
/home/leo/Desktop/FieldBounda
riesRasterCellsLinks.nt

ESWC2015 - Tutorial on Publishing and Interlinking Linked Geospatial Data

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