Prepared by A/Prof Gary J. Hunter, November 2007 Slide 1

First International Workshop on Semantic and Conceptual Issues in Geographic Information

Systems (SeCoGIS 2007), Auckland, New Zealand

Conceptual Modelling, Semantics and

Interoperability for GIS: Progress and Prospects

Conceptual Modelling, Semantics and

Interoperability for GIS: Progress and Prospects

A/Prof. Gary HunterDepartment of Geomatics

University of Melbourne, Australia garyh@unimelb.edu.au

Prepared by A/Prof Gary J. Hunter, November 2007 Slide 2

Presentation OutlinePresentation Outline

•The past

•The present

•The future

•Conclusions

Prepared by A/Prof Gary J. Hunter, November 2007 Slide 2

Prepared by A/Prof Gary J. Hunter, November 2007 Slide 3Prepared by A/Prof Gary J. Hunter, November 2007 Slide 3

The Past: 1970s to mid-80s

The Past: 1970s to mid-80s

• Islands of self-contained information and DIY digital data, nothing else available

• Conceptual models had to be clear to get funding for implementation

• Data formats were incompatible e.g. IBM GFIS vs ESRI Arc/Info vs Intergraph IGDS/DMRS (standalone, turnkey systems)

• Inability to share data avoided many problems in the early days

• ‘Spatial’ in common use (SORSA mid-70s) but policy makers didn’t understand it.

Prepared by A/Prof Gary J. Hunter, November 2007 Slide 4Prepared by A/Prof Gary J. Hunter, November 2007 Slide 4

The Past (cont.)The Past (cont.)

• Early solutions: force agencies to adopt common software, hardware and base data

• Integration was about data ‘lining up’ features

• Winners and losers

• Problems started to occur when we tried to share data—even by carrying magnetic tapes and disk drives between sites

• We could connect by LANs, but these tended to be slow and intra-agency

Prepared by A/Prof Gary J. Hunter, November 2007 Slide 5Prepared by A/Prof Gary J. Hunter, November 2007 Slide 5

The Past (cont.)The Past (cont.)

• Began to observe that different people defined things differently e.g. street addresses—we thought the answer was standards (AS2482)

• Features could also be represented differently e.g. a road as a line, polygon or nothing at all

• Legal definitions caused problems e.g. roads, rivers, bridges could all be classed as ‘public highways’

• Bigger agencies again tried to force their ideas

• Now saw that one word could have multiple meanings, and one meaning could have multiple words.

Prepared by A/Prof Gary J. Hunter, November 2007 Slide 6Prepared by A/Prof Gary J. Hunter, November 2007 Slide 6

The Past (cont.)The Past (cont.)

• Also started to see that user-defined context and application was critical e.g. parcels, properties

• People were now ‘doing’ GIS rather than just digital mapping—attributes, queries, operations on data.

• Past remedies no longer worked

Prepared by A/Prof Gary J. Hunter, November 2007 Slide 7Prepared by A/Prof Gary J. Hunter, November 2007 Slide 7

The Present: late 1980s onwards

The Present: late 1980s onwards

• Work on SDTS being finalised

• MANs/WANs introduced

• Along came the web and everything changed

THE VISION

We should be able to search for, display, process and query spatial data in different formats, structures, databases, systems and locations seamlessly anytime, anywhere.

We should receive only the data suited to our context, and not receive data that doesn’t meet our needs.

Prepared by A/Prof Gary J. Hunter, November 2007 Slide 8Prepared by A/Prof Gary J. Hunter, November 2007 Slide 8

Conceptual Models

Semantics Interoperability

Data formats

Software products

Aggregation conflicts

Heterogeneity

System architectures

Multi-databases

Query processing

Data processing

Raster model

Vector model

Real World

Naming semantics

Cognitive semantics

Schema semantics

Query semantics

Level of abstraction

Standards

Specifications

Metadata semantics

Natural environment

Built environment

Geometric semantics

Feature semantics

The Present (cont.)The Present (cont.)

Resolution

Ontologies

Physical models

Logical models

Mediators

Prepared by A/Prof Gary J. Hunter, November 2007 Slide 9Prepared by A/Prof Gary J. Hunter, November 2007 Slide 9

Conceptual Models

• An abstract or theoretical construct that represents something, with a set of variables and a set of logical and quantitative relationships between them.

• Conceptual models are not always as complicated or confusing as the Stanford Watershed Model (at left)

The Present (cont.)The Present (cont.)

Prepared by A/Prof Gary J. Hunter, November 2007 Slide 10Prepared by A/Prof Gary J. Hunter, November 2007 Slide 10

Semantics: from the Greek sema, sign and semantikos, giving signs, significant

• the implied meaning of words, expressions, sentences, data, symbols or other representations.

“Suddenly, a heated exchange broke out between the King and the moat contractor.”(© Gary Larson, The Far Side)

The Present (cont.)The Present (cont.)

Prepared by A/Prof Gary J. Hunter, November 2007 Slide 11Prepared by A/Prof Gary J. Hunter, November 2007 Slide 11

Interoperability

• the ability of a system or product to work with other systems or products without special effort on the part of the customer.

The Present (cont.)The Present (cont.)

Prepared by A/Prof Gary J. Hunter, November 2007 Slide 12Prepared by A/Prof Gary J. Hunter, November 2007 Slide 12

• Semantic problems are growing as more people create data e.g. 700 forest definitions

• Even these change within an organisation with time and purpose e.g. land use classes

• Technical issues started to be overcome re: interoperability e.g. Open GIS Consortium

• Military alliances know the pain of semantic differences and vigorously pursue their own strict standards e.g. NATO

• Still often governed by the conceptual models provided in vendor’s products

The Present (cont.)The Present (cont.)

Prepared by A/Prof Gary J. Hunter, November 2007 Slide 13Prepared by A/Prof Gary J. Hunter, November 2007 Slide 13

• The work of Mark, Smith, Frank, Kuhn and others to raise the profile of spatial data semantics

• Describing/capturing semantics – can we define the complete set of characteristics for an object class? Usually not as there are exceptions, inclusions and exclusions (Kuhn, 1995)

• Meaning can be encapsulated by rules and placed in deductive databases (Kim, 1995)

• Ontology-driven GIS that permit users to learn about ‘embedded knowledge’ in a system (Fonseca, Egenhofer, Aouris & Camara, 2002)

• Ontology-based metadata (Schuurman & Leszczynski, 2006)

The Present (cont.)The Present (cont.)

Prepared by A/Prof Gary J. Hunter, November 2007 Slide 14Prepared by A/Prof Gary J. Hunter, November 2007 Slide 14

• Problem and product ontologies used for the revision of beach nourishments in The Netherlands (Van de Vlag, Vasseur, Stein & Jeansoulin, 2005).

• The wide range of ontological considerations in GIScience (Argawal, 2005)

• Geospatial ontologies for space, theme and time (Arpinar et al. 2006)

• Ontology based retrieval of spatial information (Lutz & Klein, 2006; Delboni et al, 2007)

• Ontology mediation for geospatial queries (Peachavanish & Karimi, 2007)

• Geospatial data retrieval and the semantic web (Wiegand & Garcia, 2007; (Fonseca & Rodriguez, 2007))

• Semantic geospatial data models (Mennis, 2003)

The Present (cont.)The Present (cont.)

Prepared by A/Prof Gary J. Hunter, November 2007 Slide 15Prepared by A/Prof Gary J. Hunter, November 2007 Slide 15

Hardware and operating systems

Spatial data formats/files

Spatial databases

Data model

Application semantics

Network protocols

The 6 levels of interoperability (adapted from Bishr, 1998)

Harder

Easier

Personal control

Non-personal control

The Present (cont.)The Present (cont.)

Prepared by A/Prof Gary J. Hunter, November 2007 Slide 16Prepared by A/Prof Gary J. Hunter, November 2007 Slide 16

Temporal interpolation of spatial objects

Temporal interpolation of spatial objects• Zhang and Hunter (2000) investigated how we might

model dynamically changing features that are born, modified or die.

Two observations of a dynamically changing region

• Issues of correspondence and changing attributes were (and still are) major conceptual obstacles.

• A technical impediment is trying to implement this within current data structures.

• Hunter, 1984, early temporal modelling efforts.

Prepared by A/Prof Gary J. Hunter, November 2007 Slide 17Prepared by A/Prof Gary J. Hunter, November 2007 Slide 17

Modelling spatial metadata as objects

Modelling spatial metadata as objects

• Shyllon and Hunter (in review) modelled metadata records as objects to facilitate searching of metadata portals on the basis of quality.

• Metadata categories are classified into top-level and bottom-level ontologies. Top-level ontologies are the meta-information, the quality-type and the quality-class. The bottom-level ontologies are the metadata objects with properties, states and behaviours. Uses ISO 19113-115 standards.

A set of the relations of metadata objects in Oracle PL/SQL 9.2i

The object table for the feature-based quality metadata

Prepared by A/Prof Gary J. Hunter, November 2007 Slide 18Prepared by A/Prof Gary J. Hunter, November 2007 Slide 18

Per-feature quality model Feature independent quality model Feature-hybrid quality model

Modelling spatial variation in data quality

Modelling spatial variation in data quality

• Sadiq and Duckham (2007) use three different models to transparently provide positional quality information.

• Uses a “Quality” button implemented in Oracle Spatial.

• Copes with sub-feature quality variation.

• Caters for alternative production methods of adding data quality information.

Prepared by A/Prof Gary J. Hunter, November 2007 Slide 19Prepared by A/Prof Gary J. Hunter, November 2007 Slide 19

The FutureThe Future

• Traditional raster and vector models often limit our imagination and applications e.g. harbour shipping movements

• Dutch cadastral system has been modelled in XML/GML—vendors asked to work with it

• Semantics apply not only to words and attributes, but also to feature classes, features types, queries and events

• Spatial semantics also need to be handled

• Need to better explain the problems and solutions to users and developers e.g. ontologies – give practical examples

Prepared by A/Prof Gary J. Hunter, November 2007 Slide 20Prepared by A/Prof Gary J. Hunter, November 2007 Slide 20

The Future (cont.)The Future (cont.)• “GIS interoperability [is] a dream for users

and a nightmare for system developers” (Laurini,

1998, p. 373)

• Are there times when we can’t stop ourselves and go beyond what system developers will ever implement? e.g. quest for an error button

• Vendors only want bite-size pieces e.g. TIGRIS

• What don’t we handle well: 3-D, dynamic objects, land/sea/air interface environments, geometric and classification fuzziness, sensor data

• Standards only work when all terms are agreed upon—o.k. for technical matters but not good for meaning (e.g. ISO 19138)

Prepared by A/Prof Gary J. Hunter, November 2007 Slide 21Prepared by A/Prof Gary J. Hunter, November 2007 Slide 21

• Sometimes the real world has to change to suit database technologies e.g. Australian street names cannot use apostrophes, so ‘Smith’s Road’ becomes ‘Smiths Road’

• Give people an expert’s opinion on semantic similarities and distinctions

• Let users take some of the responsibility

Complete transparent,

technical solutions embedded in GIS

Enforced standards

Expert opinion

and advice

Empower users to take the action and make the decisions

Solution Spectrum

The Future (cont.)The Future (cont.)

Prepared by A/Prof Gary J. Hunter, November 2007 Slide 22Prepared by A/Prof Gary J. Hunter, November 2007 Slide 22

• We’ve come a long way since the early days

• ‘Applications in search of software’ has now turned to ‘software in search of applications’, but we have come full circle—and original concepts need updating

• Caveat: are we really in the business of satisfying people’s dreams?

• Commercial imperatives may well decide ‘no’.

• But at the same time, people/vendors are looking for next-generation systems—so we are on the right track !

ConclusionsConclusions

Prepared by A/Prof Gary J. Hunter, November 2007 Slide 23Prepared by A/Prof Gary J. Hunter, November 2007 Slide 23

Thank you.

Questions?

Thank you.

Questions?

Prepared by A/Prof Gary J. Hunter, November 2007 Slide 24Prepared by A/Prof Gary J. Hunter, November 2007 Slide 24

ReferencesReferencesArgawal, P. Ontological considerations in GIScience”. International Journal of Geographical

Information Science, 19(5), pp. 501-536.

Arpinar, I.B., Sheth, A., Ramakrishnan, C., Usery, E.L., Azami, M. and Kwan, M-P, 2006, “Geospatial ontology development and semantic analytics”. Transactions in GIS, 10(4), pp. 551-576.

Bishr, Y., 1998, “Overcoming the semantic and other barriers to GIS interoperability”. International Journal of Geographical Information Science, 12(4), pp. 299-214.

Delboni, T.M., Borges, K.A.V., Laender, A.H.F. and Davis Jr., C.A., 2007, Transactions in GIS, 11(3), pp. 337-398-257.

Fonseca, T.F., Egenhofer, M.J., Agouris, P. and Camara, G., 2002, “Using ontologies for integrated GIS”. Transactions in GIS, 6(3), pp. 231-257.

Fonseca, T.F. and Rodriguez, A., 2007, “From geo-pragmatics to derivation ontologies: new directions for the geospatial semantic web”. Transactions in GIS, 11(3), pp. 313-316.

Kim, W, 1995, Modern Database Systems, The Object Model, Interoperability and Beyond. Reading, MA: Addison-Wesley)

Laurini, R., 1998, “Spatial multi-database topological continuity and indexing: a step towards seamless GIS data interoperability”. International Journal of Geographical Information Science, 12(4), pp. 373-402.

Lutz, M. and Klein, E. 2006, “Ontology-based retrieval of geographic information”. International Journal of Geographical Information Science, 20(3), pp. 261-272.

Mennis, J.L., 2003, “Derivation and implementation of a semantic Gis data model informed by principles of cognition”. Computers, Environment and Urban Systems, 27, pp. 455-479.

Prepared by A/Prof Gary J. Hunter, November 2007 Slide 25Prepared by A/Prof Gary J. Hunter, November 2007 Slide 25

ReferencesReferencesPeachavanish, R. and Karimi, H., 2007, “Ontological engineering for interpreting geospatial

queries”. Transactions in GIS, 11(1), pp. 115-130.

Sadiq, Z. and Duckham, M., 2007, “Storing and querying spatially varying data quality information using an integrated spatial DBMS”. Proceedings of the 2007 International Symposium on Spatial Data Quality (ISSDQ 2007), A. Stein ed., Enschede, The Netherlands, 8 pp. (Proceedings on CD)

Schuurman, N. and Leszczynski, A., 2006, “Ontology-based metadata”. Transactions in GIS, 10(5), pp. 709-726.

Shyllon. E.A. and Hunter, G.J., (in review), “Ontologies describing spatial data quality semantics: a web search structure for metadata portal catalogues”. Submitted to Geoinfomatica, 22 pp.

Van De Vlag, D., Vasseur, B., Stein, A. and Reansoulin, R., 2005, “An application of problem and product ontologies for the revision of beach nourishments”. International Journal of Geographical Information Science, 19(10), pp. 1057-1072.

Wiegand, N. and Garcia, C., 2007, A task-based ontology approach to automats geospatial data retrieval”. Transactions in GIS, 11(3), pp. 355-376.

Zhang, W. and Hunter, G.J., 2000, "Temporal Interpolation of Spatially Dynamic Objects". GeoInformatica, 4(4), pp. 403-18.