Earth Sciences Sector

Semantic WebSemantic Web

……vers l’interopérabilité sur le Webvers l’interopérabilité sur le Web

Jean BrodeurJean Brodeur

Journée INNOVATION en Géomatique - 6e ÉditionCentre d’information topographique - Sherbrooke

8 novembre 2007

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Déroulement de la présentationDéroulement de la présentation

• Contexte

• Description

• Ontologie

• Technologies du W3C

• Conclusion

Earth Sciences Sector

Web SémantiqueWeb Sémantique

ContexteContexte

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Interoperability of informationInteroperability of information

• Concerns the understanding and usage of information

• Increases the availability, access, integration, and sharing of information

• Concerns the establishment of data infrastructures at local, regional and global level

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Source Encoder Signal Decoder Destination

Feedback

Knowledge Knowledge

Source Destination Signal

……between peoplebetween people

• Is based on– the communication process;

– People knowledge and the commonness.

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… … through the communication paradigmthrough the communication paradigm

<Factory> <name>FactoryA</name> …

User’s request with his own concepts in memory(e.g. Factory, Mill,

Plant, etc.)

(Communication channel)(Communication channel)

““Factories Factories withinwithin

Kyoto?”Kyoto?”

<Factory> <name>FactoryA</name> …

5. Data encoding(message production)

6. Data transmission

7. Data reception

8. Data decoding (message recognition)

R

R’’’’

R’’

R’’’R’

2. Request transmission

1. Request encoding (message production)

Cr = f (C)

ProviderProviderUserUserAdministrative Administrative area (Kyoto)area (Kyoto)

Building (factory)Building (factory)

(Communication channel)(Communication channel)

<Factory> <name>FactoryA</name> <location> <GPL_CoordinateTuple>   <tuple CrsName="urn:EPSG::21418"> 1259753 18503245 …

Interoperability = correspondence of received data

with the initial request.

= T|S|

-FactoryA-FactoryA-EPSG:21418 -EPSG:21418 -1259753, 18503245 -1259753, 18503245

-Factory-Factory-Kyoto-Kyoto

--FactoryFactory-Kyoto-Kyoto

4. Request decoding (message recognition)

3. Request reception -Building (factory)-Building (factory) -Factory-Factory

-Administrative -Administrative -Kyoto-Kyoto area (Kyoto) area (Kyoto)

|S| = T

Request recognition from database’s geographic concepts

then search of corresponding geographic information.

Recognition = f ({C1, ... ,Cn}, Cr)

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Heterogeneity of informationHeterogeneity of information

• A major barrier to interoperability• Types of heterogeneity

– System (i.e. interaction between computers of different OS and databases of different DBMS)

– Syntactic (i.e. differences between formats such as a GML document and a Shapefile)

– Schematic (i.e. differences in conceptual schemas such as street may be defined as a class or as a value of an attribute of a road class)

– Semantic (i.e. difference of meaning given to a signal, e.g. chair means either a seat or a position of authority, or the various signal that have a similar meaning, e.g. watercourse vs. river/stream)

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Current WebCurrent Web

• Information is mainly based on Web documents

• A Google search lists Web documents that correspond to keywords – e.g. “Semantic Web”

• Web documents are intended to human beings, which have to figure out the nature and usefulness of their contents

• It is not designed for the use of information by software

Earth Sciences Sector

Web SémantiqueWeb Sémantique

Une descriptionUne description

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Semantic WebSemantic Web

• An idea introduced byT. Burners-Lee

• From a Web of documents for humans to a Web of data and information processable by computers

• Published the first time in 2001– T. Berners-Lee, J. Hendler, and O.

Lassila, “The Semantic Web,” Scientific Am., May 2001, pp. 34–43.

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Semantic WebSemantic Web

• Is about a Web that answers questions instead of returning Web pages about topics of interests

• Is about data that is application independent, composeable, classified, and part of a larger information structure

• Is about data that is understandable and processable by machines

–Needs to make the data smarter

Text and DB records

XML withmixed vocabularies

XML and singledomain vocabularies

Ontologies and rules

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Data, information, and knowledge pyramidData, information, and knowledge pyramid

from semanticweb.org

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Semantic WebSemantic Web

• Is seen as a solution to

– information overload specially with the propagation of the Internet

–breaking stovepipe systems and allowing sharing information

–aggregating information from multiple sources

–enabling users to retrieve the data they need more efficiently based on their own vocabulary (concepts) and data specific vocabulary (concepts)

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Semantic Web deals with…Semantic Web deals with…

• Common formats– XML is the syntactic foundation

(RDF, RDF-S, OWL, RIF, SPARQL)

– Oriented toward integration and combination of data from various sources (Web)

– As opposed to the original Web that is oriented toward the interchange of documents

• Language– Capturing how the data relates to

real world objects (RDF-S and OWL).

Berners-Lee, T., 2006. Artificial Intelligence and the Semantic Web,AAAI Conference keynote, 2006-07-18.

http://www.w3.org/2006/Talks/0718-aaai-tbl/Overview.html

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Semantic Web… What is needed?Semantic Web… What is needed?

• Logical assertions– connect subject to an object with a verb– RDF

• Classification of concepts– Taxonomies/ontologies

• Formal models– Concepts, their properties, and relationships– OWL– For reasoning

• Rules– Inference rules to derive conclusion– RIF

• Trust– Provide access to resources only to trusted agents. An agent can be asserted

“trusted” from another via a digital signature

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Web services and Semantic WebWeb services and Semantic Web

• Based on URI

• XML

• Smart data

• Semantic Web to discover Web services (Semantic Web-enabled Web services)

• Semantic Web to support interaction between Web services

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Geospatial Semantic WebGeospatial Semantic Web

• Developed by– Max J. Egenhofer, 2002. Toward the Semantic Geospatial Web, Proceedings of the

10th ACM international symposium on Advances in geographic information systems, p.1-4, November 08-09, 2002, McLean, Virginia, USA

– Frederico Fonseca and Amit Sheth, 2002. The Geospatial Semantic Web, UCGIS White Paper, 2002. http://www.ucgis4.org/priorities/research/2002researchagenda.htm

• Challenges– Ontologies of spatial concepts use across disciplines

geospatial-relations ontologyGeospatial feature ontology

– Ontology management: designing, developing, storing, registering, discovering, browsing, maintaining and querying

– Canonical form for geospatial data queries

– Matching concepts to ontologies

– Ontology integration

Earth Sciences Sector

Web SémantiqueWeb Sémantique

OntologieOntologie

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OntologyOntology

• What is an ontology? – Taxonomy? XML schema?

– Thesaurus? Conceptual model?

– UML, RDF/S, OWL? Description logic?

– Logical theory?

• What is the purpose or role of an ontology?

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OntologyOntology

• A foundation for the success of the Semantic Web

• Meaning of data in a format that machine can understand

• Data derived its semantics from ontology

• To support integration of heterogeneous data across communities

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SemanticsSemantics

Concept

• Thoughts that give meaning to signs and phenomena;

referent signifier

signified

• Links between signs and real world phenomena.

(Frege, Peirce, Ogden & Richards, Eco)

Phenomenon

Colosseum,Rome,N41°53'25" Latitude E12°29'32" Longitude

Sign

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OntologyOntology

• Philosophy

• Artificial intelligence

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Ontology… A philosophical accountOntology… A philosophical account

• Study or science of being (or existence)

• Description of the world in itself

• Type of entities, properties, categories, and relationships that compose the reality

• Philosophy consider that there is only one ontology

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Ontology… An artificial intelligence accountOntology… An artificial intelligence account

• “An explicit specification of a conceptualisation” (Gruber 1993)

• “A logical theory accounting for the intended meaning of a vocabulary” (Guarino 1995)

• A layer enabling the definition of concepts of reality

• Meaning of a subject area or an area of knowledge

• A formal representation of phenomena with an underlying vocabulary including definitions and axioms that make the intended meaning explicit and describe phenomena and their interrelationships (Brodeur 2003)

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Ontology… An artificial intelligence accountOntology… An artificial intelligence account

• Represented by classes, relations, properties, attributes, and values

• AI considers that reality may be abstracted differently depending on the context from which “things” are perceived

• AI recognizes that multiple ontologies about the same part of reality may exist

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Ontology… an exampleOntology… an example

• Common conceptualization

• Living structure– Static

– Volatile

• Explicit commitment to shared meaning among an interested community

• Can be re-used and extended

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OntologyOntology Spectrum Spectrum

Daconta, M. et al., 2003. The semantic web, Wiley.

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Multiple ontology levelsMultiple ontology levels

• Global or top-level ontology:

general concepts independent

of a specific domain (e.g. space,

time, …)

• Domain ontology: concepts

specific to a domain (e.g.

transportation, geology, land

cover, …)

• Application ontology: concepts

that are specialised in a given

context and use (e.g. parcel

delivery, ambulance

dispatching, rescue, …)

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Role of ontologyRole of ontology

• Knowledge base that supports interpretation, reasoning, and inference

–Description logic: river/stream watercourse

–Notion of similarity/proximity: the concept watercourse contains the concept river/stream

–Joe is passenger of Train 1234; Train 1234 goes to Rome; Joe goes to Rome

–…

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Reasoning and inferenceReasoning and inference

• Possible through the relation that exist between concepts– Subsumption: isA, isSuperclassOf

– Meronymy: part of

– GeoSemantic Proximity: Based on a 4 intersection matrix between intrinsic and extrinsic properties of two concepts.

intrinsic properties provide the literal meaning of the conceptextrinsic properties provide meaning through the influence that other concepts

have on a concept (e.g. behaviours and relationships)

– Matching distance: a distance between concepts in a graph

– …

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Subsumption relationsSubsumption relations

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GeoSemantic ProximityGeoSemantic Proximity

Intrinsic Properties

(CK°)

Extrinsic properties

(CK)

CK CLCL

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Geosemantic ProximityGeosemantic ProximityCKCK

CLCL

Common extrinsic

properties

Common intrinsic

properties

No common intrinsic

properties

No common extrinsic

properties

The geosemantic proximity of Road with Street is then GsP_fftt ou contains

CK CL

Road vs. Street:• Street participates in a relationship with other types of

Road• Then, the intersection of extrinsic properties of Street

with intrinsic properties of Road is not empty

Road vs. Street:• Street corresponds to a value of the attribute

classification of Road• Both have the same geometry• Then, the intersection of intrinsic properties of

Road and Street is not empty

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ContextContext

• Provides concepts with real-world semantics• About how phenomena are perceived and abstracted

resulting in various classes, properties (thematic, spatial, temporal), and relationships

• About how data is captured in databases including constraints such as on object dimension

• Provide details on:– Use: user ID, user profile, user location, type of uses– Data: source, geospatial entities, meaning, scale, date of validity, etc.– Association: relationships (spatial, semantic, etc.)– Procedure: process steps to capture the data, query to get the data, etc.

• Metadata constitutes a valuable source of contextual details

• Can be captured by the way of intrinsic and extrinsic properties

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Interoperability, Semantics, and OntologiesInteroperability, Semantics, and Ontologies

<Factory> <name>FactoryA</name> …

(Communication channel)(Communication channel)

““Factories Factories withinwithin

Kyoto?”Kyoto?”

<Factory> <name>FactoryA</name> …

R

R’’’’

R’’

R’’’R’ProviderProviderUserUser

(Communication channel)(Communication channel)

<Factory> <name>FactoryA</name> <location> <GPL_CoordinateTuple>   <tuple CrsName="urn:EPSG::21418"> 1259753 18503245 …

-Factory-Factory-Kyoto-Kyoto

--FactoryFactory-Kyoto-Kyoto

Ontologies

Earth Sciences Sector

Web SémantiqueWeb Sémantique

Technologies du W3CTechnologies du W3C

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W3C TechnologiesW3C Technologies

• Resource Description Framework (RDF)– http://www.w3.org/RDF/

• Resource Description Framework Schema (RDF-S)– http://www.w3.org/TR/rdf-schema/

• Web Ontology Language (OWL)– http://www.w3.org/2004/OWL/

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RDFRDF

• Is based on the triple: Subject - Predicate – Object

• Subject: the resource, the thing about which something is asserted

• Predicate: the relation that binds the subject to the object

• Object: either a literal value or a resource referred to the subject by the predicate

Subject

Object

Literal Value

Predicate

Predicate

Example:<rdf:Description rdf:about="#colosseum"> <ex:isLocatedIn> <rdf:Description rdf:about="#Rome"> </rdf:Description> </ex:isLocatedIn></rdf:Description>

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RDF-SRDF-S

• Based on RDF

• Set of standard RDF resources to create application/user

community specific RDF vocabularies

• Allows to create classes of data

• Class instances are then created in RDF

• Relations are introduces as property

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RDF-S, an exampleRDF-S, an exampleCI_Address

CitationAndResponsibleParty

+ addressAdministrativeArea+ addressCity

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OWLOWL

• Language for knowledge representation

• Initiated in November 2001

• Is an evolution of DAML+OIL– DAML: DARPA Agent Markup Language

– DARPA: Defence Advanced Research Projects Agency

– OIL: Ontology Inference Layer

• Three levels from low to high expressivity– Lite: intended mainly for the description of classification hierarchy with

attributes, cardinalities are limited to 0 or 1

– DL: stands for description logics, add knowledge representation that improves reasoning, allows much flexibility on cardinality restrictions

– Full: allows maximum expressiveness and the syntactic freedom of RDF. As such a class may be either a collection of individuals or an individual in itself

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OWL , an exampleOWL , an example

CI_Address

CitationAndResponsibleParty

+ addressAdministrativeArea+ addressCity

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ToolsTools

• Jena 2 Toolkit: – RDF/OWL API– http://jena.sourceforge.net/

• Protégé 2000 – Editor for ontology – http://protege.stanford.edu/

• Tools at Network Inference– http://www.networkinference.com/

• OilEd:– http://oiled.man.ac.uk/– Editor for ontologies– Mostly for DAML+OIL, exports OWL but not a current representation

• OWL Validator:– http://owl.bbn.com/validator/– Web-based or command-line utility– Performs basic validation of OWL file

• OWL Ontology Validator:– http://phoebus.cs.man.ac.uk:9999/OWL/Validator– a "species validator" that checks use of OWL Lite, OWL DL, and OWL Full constructs

• Euler:– http://www.agfa.com/w3c/euler/– an inference engine which has been used for a lot of the OWL Test Cases

• Chimaera:– http://www.ksl.stanford.edu/software/chimaera/– Ontology evolution environment (diagnostics, merging, light editing)– Mostly for DAML+OIL, being updated to export and inport current OWL

• Extensive list of tools,– http://www.w3.org/2001/sw/WebOnt/impls

Earth Sciences Sector

Web SémantiqueWeb Sémantique

ConclusionConclusion

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ConclusionConclusion

• Semantic Web from T. Burners-Lee perspective is:

• Data interoperability across applications and organizations (for IT)

• A set of interoperable standards for knowledge exchange • An architecture for interconnected communities and

vocabularies

• Importance of URIs and ontologies• One URI denotes one concept

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ConclusionConclusion

• Similitudes importantes entre le Web Sémantique et les travaux sur l’interopérabilité des données géographiques

• ISO/TC 211 amorce un réalignement de ses activités de normalisation dans le but de profiter des effets du Web Sémantique et par le fait même d’y contribuer

– Revue du modèle de référence (ISO19101)

– Description des modèles UML en OWL

– Mise à jour du langage de schéma conceptuel (ISO/TS19103)

– …

Earth Sciences Sector

QuestionsQuestions

MerciMerci