1. UNIVERSITÉ LAVAL Geosemantic Proximity and Data Fusion 1 Laval University Centre for Research in Geomatics Geosemantics Proximity and Data Fusion Jean Brodeur 1,2 Brodeur@rncan.gc.ca 2 Natural Resources of Canada Geomatics Canada Centre for Topographic Information Geoffrey Edwards 1 geoffrey.edwards@geoide.ulacval.ca Bernard Moulin 1 bernard.moulin@ift.ulaval.ca Yvan Bédard 1 yvan.bedard@scg.ulaval.ca

2. Geosemantic Proximity and Data Fusion 2 Presentation 0utline Context Problem Proposed Approach Concluding Remarks

3. Geosemantic Proximity and Data Fusion 3 Context Heritage Base of Topographic Data in Canada –National Topographic Data Base NTDB 1:50 000 and 1:250 000; –Statistics Canada Street Network Files (SNF); Digital Boundary Files (DBF); Digital Cartographic Files (DCF); –Department of National Defense VMap0 (DCW) : 1:1 000 000; VMap1 : 1:250 000; –National Atlas of Canada (1:2 000 000 … 1:30 000 000); –Canadian Provinces (1:10 000 and 1:20 000).

4. Geosemantic Proximity and Data Fusion 4 Context Heterogeneity of geospatial data –semantics; –spatial; –temporal. Marsh qw, marsh/swamp e, swamp qw, marsh/fen q w, wetland e, bog, slough, muskeg Bridge –NTDB: part of a road or a railroad built on a raised structure…; –BCDBM: Structure …; –BCDBM: also Trestle that is similar to bridge; –NB: ponts and ponceaux.

5. Geosemantic Proximity and Data Fusion 5 Context Data Warehouse –integration of data from multiple sources in order to get a coherent whole; –at CTI, the NTDB is a data warehouse built from stereodigitized data; provincial data; digitized maps; –mapping of semantics and spatial descriptions results in correlation tables based on comparison of models; process realized by experienced people on the different models.

6. Geosemantic Proximity and Data Fusion 6 Context Existing technical solutions for interoperability of geospatial information do not resolve the problem of semantic interoperability.

7. Geosemantic Proximity and Data Fusion 7 Context Web –Opens the access to data; –People want more and more to use data merged from multiple sources which create synergy not available from the use of independent data holdings; –Barrier : heterogeneity; –Need for an automatic solution.

8. Geosemantic Proximity and Data Fusion 8 Context Standardization –OGC envisions complete integration of resources in geomatics (data and software); development is components-based with standardized interfaces. –ISO/TC 211 goals are a better understanding of geospatial data; to encrease availability, access, sharing and integration of geospatial data; to develop a world wide vision.

9. Geosemantic Proximity and Data Fusion 9 Context Canadian Geospatial Data Infrastructure –to increase access and use of geospatial data; –to provide a common national structure; –to promote collaboration between producers. Centre for Topographic Information –from NTDB to CanVect; –Canvect : decentralised data warehouse; –autonomy of partners; –re-use of geospatial data without duplication.

10. Geosemantic Proximity and Data Fusion 10 Problem How to integrate data coming from multiple sources in a coherent whole? How to interoperate data coming from multiple sources? How to facilitate dialog between –machines and machines? –users and machines? How to integrate and support data interoperability based on users vocabulaty ? How to locate object classes, attributes, geometric and temporal representations from multiple data sources to meet the needs of users ?

11. Geosemantic Proximity and Data Fusion 11 Some examples Concepts in mind Representations in data bases

12. Geosemantic Proximity and Data Fusion 12 Problem This problem exists also elsewhere, for instance: –at "ministère des ressouces naturelles (MRN) du Québec" with municipal data; –in 911 services with municipal data, data from BDTQ and NTDB; –in "communautés urbaines" and "municipalités régionales de comté" with municipal data; –in the general population accessing data from different Web sites.

13. Geosemantic Proximity and Data Fusion 13 Decoding: a simulation process that maps conceptual representations to A f concepts Recognition: a process that matches sensory inputs with previously learned and stored referents (called concepts) [Bédard 1986] Detection: a physiological task performed by sensory receptorsEncoding: a simulation process [Barsalou 1999] that transforms A u concepts into physical symbols (called conceptual representations) The problem to locate object classes…: a Communication Process Recognition Encoding Detection Decoding Encoding Decoding

14. Geosemantic Proximity and Data Fusion 14 Three levels of ontologies [Guarino 1998] –application widely used; application schema, data dictionaty, feature catalogue, repository, standards and specifications on data; Standards and Specifications of the NTDB (Canada), VMap Specifications, British Columbia Specifications and Guidelines for Geomatics, Ontario Digital Topographic Data Base - A Guide for Users, BD-Topo, BD-Carto, ATKIS, USGS-DLG; –domain A shared vocabulary in an information community; National Standards for the Exchange of Digital Topographic Data, Volume II, Topographic Codes and Dictionary of Topographic Features; –global High level; generic concepts; domain independant; WordNet, CYC, TermiumPlus. Ontologies Knowledge bases on concepts; Agent referents composed of experiences, believes, etc.; Specification of a conceptualization; Shared vocabulary.

15. Geosemantic Proximity and Data Fusion 15 Geosemantics Proximity Geosemantics proximity relates different conceptual representations of the same concept each other; It is seen as one function of the simulation process [Barsalou 1999] which is one part of the process to locate object classes (communication process); Context is recognized as the principal medium that conveys the Real World Semantics (RWS) [Ouksel and Sheth 1999]; Context is split in two components: –conceptual representation intrinsic signification, thing/situation to which it refers, invariable; –conceptual representation extrinsic signification, roles, relationships with others things, things that are suggested or evoked; spatial temporal descriptive

16. Geosemantic Proximity and Data Fusion 16 Context Being : C K :Context of K, C K º :intrinsic signification of K,  C K :extrinsic signification of K, We say : C K =  C K U C K º

17. Geosemantic Proximity and Data Fusion 17 Geosemantics Proximity K :=  Road, VMap  L :=  Trees, VMap  M := Représentation conceptuelle de plus haut niveau d’abstraction disjoint (K,L) := (  x | x  K  x  L)  (  y,z | (y  K  y  M)  (z  L  z  M)) K :=  Waterbody, BNDT  L :=  Watercourse, BNDT  M :=  Réseau hydrographique  touche (K,L) := (  x | x  K  x  L)  (  y,z | (y  K  y  M)  (z  L  z  M)) K :=  Road, BNDT  L :=  Street, BNDT  dedans (K,L) := (  x  L | x  K) contient (K,L) := (  x  K | x  L) K :=  Bridge, BNDT  L :=  Hazard to air navigation, BNDT  M :=  Structure haute  chevauche (K,L) := (  x | x  K  x  L)  (  y,z | (y  K  y  M)  (z  L  z  M)) K :=  Waterbody, BNDT  L :=  Lac, BDTQ  M :=  Réseau hydrographique  couvre (K,L) := (  x  L | x  K)  (  y,z | (y  K  y  M)  (z  L  z  M)) couvert par (K,L) := (  x  K | x  L)  (  y,z | (y  K  y  M)  (z  L  z  M)) K :=  Marsh/Swamp, VMap  L :=  Wetland, BNDT  M :=  Water saturated soil  égal (K,L) := (  x | x  K  x  L)  (  y,z | (y  K  y  M)  (z  L  z  M))

18. Geosemantic Proximity and Data Fusion 18 Concluding Remarks The problem of geospatial data integration is multi-faceted; One facet is to locate object classes, attributes, geometric and temporal representations from multiple data sources to meet the needs of users; Geosemantics proximity is an operation to find if a conceptual representation simulates an agent concept (the thing it refers to); It is based on context (with intrinsic and extrinsic components); It follows the 4-intersections model [Egenhofer 1993] to derive geosemantics predicates.