1. Earth Sciences Sector Semantic Web …vers l’interopérabilité sur le Web Jean Brodeur Journée INNOVATION en Géomatique - 6e Édition Centre d’information topographique - Sherbrooke 8 novembre 2007

2. 2 Déroulement de la présentation Contexte Description Ontologie Technologies du W3C Conclusion

3. Earth Sciences Sector Web Sémantique Contexte

4. 4 Interoperability 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

5. 5 …between people Is based on –the communication process; –People knowledge and the commonness.

6. 6 … through the communication paradigm FactoryA … User’s request with his own concepts in memory (e.g. Factory, Mill, Plant, etc.) (Communication channel) “Factories within Kyoto?” FactoryA … 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) Provider User Administrative area (Kyoto) Building (factory) (Communication channel) FactoryA <tuple CrsName="urn:EPSG::21418"> 1259753 18503245 … Interoperability = correspondence of received data with the initial request. = T|S||S| -FactoryA-EPSG:21418 -1259753, 18503245 -Factory-Kyoto -Factory -Kyoto 4. Request decoding (message recognition) 3. Request reception -Building (factory)-Factory -Administrative -Kyoto area (Kyoto) |S||S|= T Request recognition from database’s geographic concepts then search of corresponding geographic information. Recognition = f ({C 1,...,C n }, Cr)

7. 7 Heterogeneity 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)

8. 8 Current 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

9. Earth Sciences Sector Web Sémantique Une description

10. 10 Semantic Web An idea introduced by T. 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.

11. 11 Semantic 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 with mixed vocabularies XML and single domain vocabularies Ontologies and rules

12. 12 Data, information, and knowledge pyramid from semanticweb.org

13. 13 Semantic 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)

14. 14 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

15. 15 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

16. 16 Web 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

17. 17 Geospatial 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 ontology  Geospatial 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

18. Earth Sciences Sector Web Sémantique Ontologie

19. 19 Ontology 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?

20. 20 Ontology 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

21. 21 Semantics 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

22. 22 Ontology Philosophy Artificial intelligence

23. 23 Ontology… 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

24. 24 Ontology… 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)

25. 25 Ontology… 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

26. 26 Ontology… an example Common conceptualization Living structure –Static –Volatile Explicit commitment to shared meaning among an interested community Can be re-used and extended

27. 27 Ontology Spectrum Daconta, M. et al., 2003. The semantic web, Wiley.

28. 28 Multiple 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, …)

29. 29 Role of ontology Knowledge base that supports interpretation, reasoning, and inference –Description logic: river/streamwatercourse –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 –…

30. 30 Reasoning 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 concept  extrinsic 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 –…

31. 31 Subsumption relations

32. 32 GeoSemantic Proximity Intrinsic Properties (C K °) Extrinsic properties (  C K ) CKCK

33. 33 Geosemantic Proximity 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 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

34. 34 Context 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

35. 35 Interoperability, Semantics, and Ontologies FactoryA … (Communication channel) “Factories within Kyoto?” FactoryA … R R’’’’ R’’ R’’’ R’ Provider User (Communication channel) FactoryA <tuple CrsName="urn:EPSG::21418"> 1259753 18503245 … -Factory-Kyoto -Factory -Kyoto Ontologies

36. Earth Sciences Sector Web Sémantique Technologies du W3C

37. 37 W3C 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/

38. 38 RDF 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 Example:

39. 39 RDF-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

40. 40 RDF-S, an example CI_Address CitationAndResponsibleParty + addressAdministrativeArea + addressCity

41. 41 OWL 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

42. 42 OWL, an example CI_Address CitationAndResponsibleParty + addressAdministrativeArea + addressCity

43. 43 Tools 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

44. Earth Sciences Sector Web Sémantique Conclusion

45. 45 Conclusion 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

46. 46 Conclusion 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) –…

47. Earth Sciences Sector Questions Merci