This document provides an overview of geospatial semantics and interoperability. It defines key concepts like semantics, interoperability, integration, and heterogeneities. It discusses types of heterogeneities that can occur like structural, domain, data, and language conflicts between data sources. Standards and specifications aim to provide syntactic interoperability but semantic interoperability requires identifying and resolving heterogeneities. The document also introduces geospatial semantic web and observations data standards that can help achieve greater interoperability.

1. (Geospatial Semantics – Week 4)
ANUSURIYA DEVARAJU
Institute for Geoinformatics,
University of Muenster.
(anusuriya.devaraju@uni-muenster.de)

2. Here are some of the things you can expect to learn…
What i i t
Wh t is interoperability? H
bilit ? How d
does i t
interoperability diff f
bilit differ from
integration?
Types of heterogeneities
Standards vs. Interpretation
Semantic underpinning : Semantic Web Stack
Geospatial Semantic Web : Broadening the current vision of Semantic
p g
Web to include geospatial data representation
2

3. Semantics
– study of meaning ; focuses on the relation between symbols (e g
(e.g.
words) and what they stand for [wikipedia]
– Our context : relations between the computer representations and
the corresponding real world features
features.
Interoperability
– Ability of diverse ‘systems’ to operate effectively and efficiently in
conjunction, on the exchange and reuse of available resources,
services, procedures, and information, according to the intended use
of their providers … [Kavouras et al 2010]
et.al.
– ‘System’ do not refer strictly to technical systems.
3

4. Interoperability may occur at any six levels
Levels of interoperability [Bishr 2010]
4

5. Semantic Interoperability
– Focuses on preserving the semantics between communicating
p g g
entities.
– Information is properly interpreted by the ‘receiver’ in the same
sense as intended by ‘sender’.
y
Information perspective : Integration vs. Interoperability
- A fully interoperable solution also requires information integration to
y p q g
deal with interpretation of data.
Semantic Integration
– Subset of semantic interoperability [Kavouras et.al. 2010]
– Necessitates the reconciliation of mismatches, heterogeneities, etc.
5

6. Precipitation
Data is created and maintained independently
Different interests of different provider groups.
Diff ti t t f diff t id
Data is encoded in an uncoordinated way.
6

7. A Classification Scheme for Semantic and Schematic Heterogeneities in XML Data Sources
by Pluempitiwiriyawej and Hammer (2000)
– Structural conflicts, domain conflict, data conflicts
, ,
Sources and Classification of Semantic Heterogeneities by Bergman (2006)
– Potential sources of heterogeneities: structural, domain, data,
language
Changing Focus on Interoperability in Information Systems: From System, Syntax, Structure
to Semantics by Sheth (1999)
– semantic structural/representation, syntactic/format, system
semantic, structural/representation syntactic/format
heterogeneities
Overcoming the semantic and other barriers to GIS interoperability by Bishr (1998)
– D t b
Database H t
Heterogeneities: semantic, schematic, syntactic
iti ti h ti t ti
7

8. Refers to the differences in data representation (i.e.,
encodings, languages and data format)
Image source: http://en.wikipedia.org/ 8

9. Differences in the types, structures of the elements
EPA STORET – Water Quality
ESRI ArcGIS Hydro Data Model
DoD Spatial Data
p
Standards for Facilities,
Infrastructure, and
Environment
Texas Commission on Environmental Quality
9

10. Cognitive heterogeneity
– describe similar real word objects from different p p
j perspectives
Naming heterogeneity
– Synonyms, homonyms acronyms
Data Stream flow Water Level Standing Water Air Precipitation
Provider Level Temperature
DPIPWE Stream Flow River Level Met M (Meters below - Rainfall
surface)
BOM - WL - Temps RN
HT Watercourse Watercourse - - Rainfall
Discharge Level
WDS Stream Water level Bore Water Level Air Rainfall
Discharge Temperature
MRT - - Standing Water Level - -
(a)DPIPWE: Department of Primary Industries, Parks, Water and Environment; (b) BOM: Bureau of Meteorology; (c) HT: Hydro Tasmania;
(d) WDS: Water Data Services; (e) MRT: Mineral Resources Tasmania
10

11. 1. Spatial data
– Data schema to manage g g p
g geographic features
– Naming/coding schemes for features
2. Non-spatial data ( g , observations)
p (e.g., )
– Data exchange language for observations or indicators
– Vocabulary/dictionary schemes for parameters
* Lefort L (2008), Technical report CSIRO ICT Centre 08/111 11

12. Geospatial features conceptual model and schema
GML Encodes Feature Geometry and Properties*
Feature representation in ArcHydro
* http://www.w3.org/Mobile/posdep/GMLIntroduction.html 12

13. ISO 191** series (GI Metadata, Spatial Schema)
OGC - GML, Web Feature Service (WFS), etc.
DoD’s Spatial Data Standards for Facilities, Infrastructure and Environment (SDSFIE)
Facilities
INSPIRE Data Specifications
EIONET Data Dictionary
Hydrology
– USGS National Hydrography Dataset Standards
– ESRI ArcHydro
– National Hydrography Dataset Plus
– INSPIRE Data Specification on Hydrography
Meteorology
– Climate Sciences Modelling Language (CSML)
– Geo interface for Atmosphere Land Earth and Ocean netCDF (GALEON)
Geo-interface Atmosphere, Land, Earth,
Geology – GeoSciML
13

14. Observations exchange languages and vocabulary/dictionary
for observed parameters
STORET-NWIS Water-Quality Services Water observation encoding in WaterML
14

15. OGC SWE Framework (SensorML, O&M, etc.)
Hydrology
– CUAHSI (WaterOneFlow WaterML ODM master controlled vocabulary etc )
(WaterOneFlow, WaterML, vocabulary, etc.)
– USGS StreamStats
– NWIS + STORET parameter codes
– EPA Water Quality Exchange (WQX)
– French Data Reference Centre for Water (SANDRE)
– Australian National Groundwater Data Transfer Standard (ANGDTS)
– Hydro XC
– Gl b l R
Global Runoff D t C t (GRDC) D t F
ff Data Centre Data Formatt
– Australian Bureau of Meteorology - Water Data Transfer Format (WDTF)
Meteorology
– Climate Sciences Modelling Language (CSML)
– NetCDF Climate and Forecast (CF) Metadata Conventions
– NOAA NWS - National Digital Forecast Database (NDFD) Extensible Markup Language
(XML)
– NOAA Standard Hydrometeorological Exchange Format (SHEF)
15

16. Standards/specifications provide a syntactic approach to
encoding geospatial information [Doerr 2004]
– Source information needs adaptation to the standard.
– Symbols in a data schema need to be interpreted by users.
– A standard is one for its domain It cannot be optimal for all
domain.
applications.
Semantic interoperability requires [Kavouras et.al. 2010]]
p y q [
– Existing heterogeneities identification
– Their importance and priority analysis
– A systematic strategy too resolve them
16

17. The Semantic Web = a Web with a meaning
A definition by Tim Berners Lee et al. (2001)
Lee, al
“The Semantic Web is an extension of the current web in which information is given well-
defined meaning, better enabling computers and people to work in cooperation.”
Idea : to allow computer machines
to understand semantics of
contents which are distributed in
the Web
*Image : http://mmt.me.uk/slides/barcamp09/ 17

18. Unicode : character set for different human languages
URI (Uniform Resource Identifier) identifies a web resource
Reference System For GI is taught by Prof. Dr. Werner Kuhn
http://ifgi.uni-muenster.de/edu-
GEO3880 Reference
Systems for GI
ont#taughtBy
g y
http://ifgi.uni-muenster.de/csa/RefSysGI http://ifgi.uni-muenster.de/~kuhn
Image source (globe) : http://www.esf.edu/nysgisconf/2002/2002abstracts.htm 18

19. XML (eXtensible Markup Language)
– A markup language to ‘carry’ data, not to display data
– XML tags are not predefined. You must define your own tags.
– Problems : semantics is missing; no agreement on the vocabulary
naming.
g
<?xml version=“1.0”?> <?xml version=“1.0”?>
<ifgi-courses> <university name = “muenster”>
<subject code =“GEO380” <institute> IFGI
instructor =“WK”> <courses-offered> GEO380 Reference
Reference System for GI System for GI
</subject> <instructor>Prof. Dr. Werner Kuhn
....... </instructor>
</ifgi-courses> </courses-offered>
.......
</university>
19

20. Namespaces provide a method to avoid element name
conflicts.
Example* : HTML table vs. table (a piece of furniture)
* http://www.w3schools.com/xml/xml_namespaces.asp 20

21. RDF (Resource Description Framework)
– Written in XML
– Describes resources on the web in triple form
Resource Property Property Value
(anything that can have a URI) (a Resource that ( the value of a Property;
has a name) note that a property value
also can be another
Resource )
R
http://ifgi...csa/
p g edu:taughtBy http://ifgi...../~kuhn/
http://ifgi /~kuhn/
RefSysGI
Representation as RDF Graph
p p
21

22. A Statement = a Resource + a Property + a Property value
(
(known as the subject, predicate and object of a Statement)
j ,p j )
22

23. RDFS - RDF’s vocabulary description language
– Example : rdfs:Class, rdfs:Literal, rdfs:Property, rdfs:Datatype……
p , , p y, yp
23

24. 24

25. RDFS is useful, but doesn't solve all the possible
requirements; complex applications may require more
q ; p pp y q
possibilities.
OWL is an ontology language; extends RDF with more
vocabulary to describe properties and classes:
• Equivalency - owl:sameAs, owl:equivalentProperty….
• Property characteristics - owl:inverseOf, owl:TransitiveProperty….
• Property type restrictions - owl:allValuesFrom, owl:intersectionOf..
• Header ontology information – owl:imports, owl:priorVersion….
Three flavors of OWL : OWL Lite OWL DL OWL Full
Lite, DL,
25

26. Example 1: Identifying inverse properties (owl:inverseOfProperty)
This allows a reasoner to infer that :
<Course> <taughtBy> <AcademicStaffMember>
26

27. Example 2: Equivalent Individuals (owl:sameAs). The following two URI
references actually refer to the same professor
27

28. An RDF query language comprises
– Prefix declarations (PREFIX)
( )
– Dataset definitions (FROM)
– A result clause (SELECT)
– Query Pattern (WHERE)
– Query Modifiers (e.g., ORDER BY, FILTER, etc.)
Get all instances of a particular class (e g Course) :
(e.g.
PREFIX edu:<http://ifgi.uni-muenster.de/edu-ont#>
SELECT ?a
WHERE
{
?a rdf:type edu:Course.
}
Note: Declaration of rdf, rdfs prefixes omitted for clarity 28

29. An RDF query language comprises
– Prefix declarations (PREFIX)
( )
– Dataset definitions (FROM)
– A result clause (SELECT)
– Query Pattern (WHERE)
– Query Modifiers (e.g., ORDER BY, FILTER, etc.)
Get all instances of a particular class (e g Course) :
(e.g.
PREFIX edu:<http://ifgi.uni-muenster.de/edu-ont#>
SELECT ?a
WHERE
{
?a rdf:type edu:Course.
}
Note: Declaration of rdf, rdfs prefixes omitted for clarity 29

30. Tim Berners-Lee (2001) Tim, Lucy, and The Semantic Web…
An imaginary girl named Lucy, whose mother has just been told by her doctor that she needs to see
a specialist At the doctor’s office Lucy instructed her Semantic Web agent through her handheld
specialist. doctor s office,
Web browser. The agent promptly retrieved information about Mom’s prescribed treatment from the
doctor’s agent, looked up several lists of providers, and checked for the ones in-plan for Mom’s
insurance within a 20-mile radius of her home and with a rating of excellent on trusted rating
g g
services.
SPACE TIME THEME
GI usually deals entities which exists in the real world,
their physical locations and temporal properties
30

31. River in Newcastle…
Newcastle UK
Newcastle
Australia
The need for spatial component within Semantic Web
Example : geospatial data + spatial operator [Egenhofer, 2002]
31

32. What is ‘Amsterdam’ to a computer…
32

33. A definition by Thuraisingham et al. (2008)…
“Geospatial semantic web refers to an intelligent, machine understandable web where
geospatial data are encoded in a semantic rich data model to facilitate automated decision
semantic-rich
making and efficient data integration.”
One of the immediate research
priorities proposed b UCGIS
i iti d by UCGIS,
2002. *(GSWIE)
Why GSW?
y unstructured /informal
– The need to include space & GI in the Web
time dimensions to the
Semantic Web
– Web users search
exclusively for geospatial
data on the Web structured geo-databases scientific materials
(Different forms of Geographic Information on the Web)
33

34. Source : Bishr (2008), Geospatial Semantic Web: Applications 34

35. (Querying Layer) (Logical Layer)
GeoSPARQL W3C GeoOWL, GeoNames Ontology, SWEET, CSDGM, etc.
Querying : SPARQL
Ontologies : OWL
(Ontological Primitive Layer)
Taxonomies: RDFS (Individuals) SKOS
Data Interchange: RDF
Syntax: XML (Basic Relational Language Layer)
Identifiers: URI W3C Geo (GeoRDF), rdfgeom2d
(Symbol/Reference Layer) (Transport/Syntax Layer)
RFC 5870 GeoURI GML Google KML GeoRSS μFormat etc.
GML, KML, GeoRSS, μFormat, etc
OWL ontologies: http://protegewiki.stanford.edu/wiki/Protege_Ontology_Library 35

36. Building highly formalized representations (e.g., ontologies)
of the available geographic information resources
g g p
Linking and mapping informal geo-centric resources to the
highly formalized representation structures being built by the
Semantic Web
Geospatial query – formulate geospatial request, represent
the results (URI or real spatial data)
( p )
Geo-data distribution policy and legal issues
36