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OGC Information Communities and Semantics Technical Committee Meeting - Ottawa

A Communication-based Description of Semantic Interoperability and Introduction to the Geosemantic Proximity Concept. OGC Information Communities and Semantics Technical Committee Meeting - Ottawa. Jean Brodeur. Tuesday, April 20, 2004. Presentation outline. Introduction Problem Objective

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OGC Information Communities and Semantics Technical Committee Meeting - Ottawa

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  1. A Communication-based Description of Semantic Interoperability andIntroduction to the Geosemantic Proximity Concept OGC Information Communities and SemanticsTechnical Committee Meeting - Ottawa Jean Brodeur Tuesday, April 20, 2004

  2. Presentation outline • Introduction • Problem • Objective • Interoperability of geospatial data • Geosemantic proximity • Prototype and experimentation • Discussion and conclusion

  3. Introduction • Multiplication of geospatial datasources and increased usage of geospatial information technologies • NTDB, VMap, DCF, BDTQ, OBM, Geographic Data BC; • Geospatial data and services are more and more accessible on the Web • CGDI, NSDI; • Today, users are turned to various geospatial data sources to fulfill their needs; • Interoperability of geospatial data and geoprocessing, proposed at the beginning of the nineties, constitutes a solution for the sharing, re-use, and integration of geospatial data(McKee and Buehler 1998; Sondheim, Gardels and Buehler 1999).

  4. Problem • Availability of multiple geospatial databases on the Web; • Each database or information community uses a specificvocabulary; • Databases are heterogeneous at syntactic, structural and semantic levels; • Many users benefit from more than one geospatial database to satisfy their needs; • Many problems such as the difficulty to locate geospatial data • Locating: search, identification, selection and extraction of geospatial data from external sources.

  5. Spatial pictogram descriptions: :0D ; :1D ; :2D ; ?:unknown geometry ; :multiple geometry ; :alternate geometry (see [Bédard, 1999 #231] and [Brodeur, 2000 #149] for more details). 1[Natural Resources Canada, 1996 #240]; 2[VMap, 1995 #117]; 3[BC Ministry of Environment Lands and Parks (Geographic Data BC), 1992 #121]; 4[OBM, 1996 #120]; 5[Québec, 2000 #123]; 6[New Brunswick, 2000 #243]. Problem How does someone assess if the result he/she gets from his/her request corresponds to the initial perception of the reality he/she had in mind when he/she sent that request?

  6. Objective In the scope of locating geospatial data that fulfill user’s needs, identify, define, and explain the elements of semantic, spatial, and temporal proximity that contribute to data interoperability.

  7. Interoperability • Distributeddatabase • Databaseheterogeneity • Semanticsimilarity • Semanticnetwork • Ontology • Cybernetics • Cognitive sciences • Reviewed existing work; • Described geospatial data interoperability; • Defined the geosemantic proximity concept; • Developed a prototype and experiment with existing data. • Reviewed existing work; • Described geospatial data interoperability; • Defined the geosemantic proximity concept; • Developed a prototype and experiment with existing data. • Reviewed existing work; • Described geospatial data interoperability; • Defined the geosemantic proximity concept; • Developed a prototype and experiment with existing data. • Reviewed existing work; • Described geospatial data interoperability; • Defined the geosemantic proximity concept; • Developed a prototype and experiment with existing data. A new conceptual framework, which takes semantics into consideration A new concept to assess semantic similarity Software agents in Java communicating in XML Actions carried out… • Reviewed existing work; • Described geospatial data interoperability; • Defined the geosemantic proximity concept; • Developed a prototype and experiment with existing data.

  8. The Open GIS Consortium Inc definesinteroperability as software components operating reciprocally (working with each other) to overcome tedious batch conversion tasks, import/export obstacles, and distributed resource access barriers imposed by heterogeneous processing environments and heterogeneous data.(McKee and Buehler, 1998 ; Sondheim, Gardels and Buehler 1999) Interoperability

  9. Ability of two or more systems or components to exchange information and to use the information that has been exchanged.(IEEE 1990) Interoperability

  10. Chair:1 back,1 seat,4 legs. Thoughts that give meaning to signs and phenomena; establish the link between signs and real world phenomena. signified Concept « Chair » Sign Phenomenon referent signifier What is semantics?

  11. Ontology • formal representation of a set of phenomena with an underlying vocabulary; • includes definitions and axioms that make the intended meaning explicit and describe phenomena and their interrelationships; • constitutes a knowledge base for reasoning; • semantic network, taxonomy, thesaurus, conceptual model, OWL file, etc.

  12. Recognition = f ({C1, ... ,Cn}, rC) (Communication channel) Request recognition from database’s geospatial concepts then search of corresponding geospatial data. 2. Request transmission 3. Request reception “(Lake or river) withinSherbrooke?” Waterbody Lake, RiverWatercourse River Sherbrooke Sherbrooke |S| = T -Lake -River -Sherbrooke -Lake -River -Sherbrooke rC = f (C) 4. Request decoding (message recognition) 1. Request encoding (message production) R Sherbrooke Waterbody User Provider 8. Data decoding (message recognition) Watercourse 5. Data encoding(message production) -Lac des Nations -Magog River -St-François River -Lac des Nations -Magog River -St-François River Interoperability = correspondence of received data with the initial request. -Lac des Nations-Magog River -St-François River 7. Data reception 6. Data transmission “Lac des Nations Magog River St-François River |S| = T (Communication channel) Conceptual Framework for Interoperability User’s request with his own concepts in memory (e.g. body of water, lake, bank, river, stream, etc.) R’’ R’’’ R’ R’’’’

  13. R R’’/R’’’’ R’/R’’’ Geosemantic ProximityFormalization of phenomena and their abstraction

  14. Intrinsic Properties (CK°) Extrinsic properties (CK) Geosemantic ProximitySymbolic Representation of geoConcept or geoConceptRep CK

  15. 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 Common extrinsic properties Common intrinsic properties • 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 • Hazard to air navigation vs. Bridge: • Hazard to air navigation participates in a relationship with Bridge and reciprocally • Then, the intersection of extrinsic properties of Hazard to air navigation with intrinsic properties of Bridge is not empty as well as the intersection of extrinsic properties of Bridge with intrinsic properties of Hazard to air navigation The geosemantic proximity of Road with Street is then GsP_fftt ou contains • Hazard to air navigation vs. Bridge: • The value bridge of the attribute type of Hazard to air navigation corresponds to Bridges of 60 metres or more in height, • Hazard to air navigation refers also to other types of phenomena (e.g. tower, chimney, tank, etc.) • Bridge refers to all bridges without considering their respective height • Both have a common geometry • Then, the intersection of intrinsic properties of Hazard to air navigation and Bridge is not empty The geosemantic proximity of Hazard to air navigation with Bridge is then GsP_fttt or overlap No common intrinsic properties No common extrinsic properties Geosemantic Proximity

  16. Ontology Perceptory Prototype

  17. Experimentation Road network and hydrographic network from : • National Topographic Data Base – Standards and Specifications of Canada (Natural Resources Canada, 1996); • User's Guide to Digital and Hardcopy property and Basemap Products of Prince Edward Island (P.E.I. Geomatics Information Centre); • Base de données topographiques du Québec 1:20 000 – Normes de production (Québec, 2000); • Ontario Digital Topographic Database – 1:10,000, 1:20,000– A Guide for User (OBM, 1996); • Digital Baseline Mapping at 1:20,000 of the province of British Columbia (BC Ministry of Environment Lands and Parks (Geographic Data BC), 1992).

  18. Ontologies

  19. Same ontology Distinct ontologies Experimentation – road network Observed success rate in the correct interpretation of a user agent’s request by a data provider agent – (road network)

  20. Same ontology Distinct ontologies Expérimentation –hydrographic network Observed success rate in the correct interpretation of a user agent’s request by a data provider agent – (hydrography)

  21. Discussion • Ontologies have been derived from geospatial data product specifications, not developed for such a usage. • Within the limits of the prototype and tests that have been conducted • Two agents that were using the same ontology have been able to interoperate each others without problem (i.e. 100% of the cases); • Two agents that were using distinct ontologies have been able to interoperate each others from 30% to 100% of the cases depending the ontologies involved. • To improve the level of interoperability of geospatial data • Develop ontologies with meaningful geoConcepts; • Content; • Relationships with other geoConcepts; • Make abstraction of the manner in which geoConcepts are stored in geospatial databases.

  22. Conclusion • New vision of interoperability of geospatial data based on • Communicationprocess between human beings; • Cognitivereasoning of human beings; • Users and providers (servers) of data : • Communicate between them using their own vocabulary (ontology); • Recognize (interpret) automatically the content of received messages; • Answer more precisely to requests received in a vocabulary that isn’t their own. • Results allow us to anticipate the development of geospatial data servers more intelligent, accessible on the Web, making possible semantic interoperability of geospatial data in future.

  23. Future work • Evaluation of the geosemantic proximity approach for its potential use in the Pathways project (SDKI) • Comparison of geosemantic proximity results with those of human beings • Inclusion of natural language analysis to process knowledge included in concepts’ definition • Addition of the analysis of difference by expanding the 4-intersection model to the 9-intersection model, which will take into consideration the exterior of geoConcepts and geoConceptReps • Development of a Web feature server prototype that is semantically interoperable

  24. Thanks Questions

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