Putting Geographic Information Ontologies to Work

The Case of Geospatial Science

Helen CouclelisGeography DepartmentUniversity of California

Santa BarbaraCalifornia, USA

GEOINFO 2013 – XIV Brazilian Symposium on Geoinformatics – November 2013

Is GIScience ‘working’ hard enough for us?

great theoretical work = great practical benefits?

Werner Kuhn VGI, trust, and clean wells in Africa

Kathleen Stewart & Christophe ClaramuntCall for papers: Spatio-temporal theories and models for environmental, urban and social sciences

Gilberto Câmara & teamgeoinformatics and … and… and…

and Helen Couclelis?

Early enthusiasmmodels! planning!spatial interaction, CA, ABM *

Mature doubtsuncertainty beyond dataforecasts and policy

Mature enthusiasmthe ‘big picture’ontology and representation in space and time

*EU’s FuturICT shortlisted project

Why ontology? What Ontology?...

from Plato to SUMO and DOLCE

World versus micro-worlds

at firstinteroperability

thencognition, language, structure, meaning, concepts, measurements, physical /non-physical entities, space, time, user, culture, reality, philosophy

recentlymicro-ontologiesmicrotheories and the Semantic Web

http://keet.wordpress.com/category/philosophy/ontology/

Note Gruber’s agent-centered definition:

An ontology is “a formal, explicit specification of a shared conceptualization” “… an ontology is a description … of the concepts and relationships that can exist for an agent or a community of agents.” Ontologies must “constrain the possible interpretations for the defined terms.”

ontologies are social artifacts

“The ultimate source of meaning is the physical world

and the agents who use signs to represent entities in the world

and their intentions concerning them”.

(Sowa)

Overview

(Introduction)Representation and the Big Picture in GISc

Ontologies of Geographic InformationA micro-ontology generating engine?

Geodesign: an applicationQuestions & Discussion

This is not a pipe The map is not the territory The model is not reality

Representation and the Big Picture

GIScience and the big-picture questions

frameworks, general theories, ontologies, base models

“The challenge of representing fields-objects in a computer environment” (Camara 2000)“Field-object integration through a common base model” (Kjenstad 2006)

“A general theory to bring many previous ideas under a single umbrella” (Goodchild et al. 2007)

“Need for a conceptually unifying data model” (Voudouris 2010)

Goodchild et al. 2007Kjenstad 2006

Two different paradigms in geospatial representation. Spatial-primitives centered (left) and concept-centered (right). Source: M. Kavouras and M. Kokla (2008) Theories of Geographic Concepts, p. 296.

Camara et al. 2000Gangemi & Mika 2003 Kuhn 2001

Couclelis 2010 Voudouris 2010

Camara

Kuhn

Gangemi

Kjenstad

Goodchild

Voudouris

Couclelis

activity X X

aggregation X X X

cognition X X

context X X X X X

data model X X X X

design X X

field/object X X X X X X

geo-unit X X X X

intentionality X X X X X

language X X

operations X X X

reification X X X X

semantics X X X

uncertainty X

user X X X X X

manycommonalitiesamongtheseauthors

The same concepts are categorized differently depending on the context

http://vissim.uwf.edu/VOTT/VOTT_desc.htm

‘Ontologies of geographic information’*

sense-perceptionsobservations

data informationknowledge

wisdom???

?

At every step, we ask:“what is the meaning of_?”

What gives information its meaning?How are data transformed into knowledge?Why model information and not directly the world?

*Couclelis 2010, IJGIS, December

What gives information its meaning?semantics on top of structure (syntax)

How are data transformed into knowledge?by being integrated into some coherent story

Why model information and not directly the world?Information entails a source and a decoder (agent)

Modeling information, not the world:three principles

Foregrounding the perspective of the user

Distinguishing a linked sequence layers of varying degrees of semantic richness

Selecting data through criteria resulting from the users’ purpose-oriented semantic choices

A representation is constructed in a particular way for a purpose

weather maps for

scientific studyschool text illustrationTV weather forecast

river models fornavigation companywater resource agencycross-border regulation

Purpose comes from the intentionality of the user

a GIScience representation (model) is constructed in response to some user need

The popular DOLCE ontology

My 2010 framework: the static version

The foundations information spacetime framework purpose

The key ingredients spacetime granules classes of properties GI Constructs (GICs)

The structureo representation levels

Most ontologies are represented as trees or semi-lattices

The foundations information spacetime framework purpose

The key ingredients spacetime granules classes of properties GI Constructs (GICs)

The structureo representation levelso lattice

This one is a lattice, with information and spacetime framework at one end, and intentionality at the other

The foundations information spacetime framework purpose

The key ingredients spacetime granules classes of properties GI Constructs (GICs)

The structure representation levelso lattice

Actually, it should be this way around

xt P = {p7} + {p6} +…+ {p1}

{p7} …. {p3} {p2} {p1}

p71, p72,… p7i …. p31, p32,… p2j p21, p22,… p2k p1

GICi

g1 1 1 … 0 …. 1 0 … 0 1 0 … 1 1

g2 1 1 … 1 …. 0 1 … 1 1 1 … 1 1

… …. …. …. …. ….

gm 1 1 0 …. 1 1 0 0 1 … 0 1

Geographic Information Constructs (GICs)topons, chronons, and codes across 7 property domains

The principle of semantic contraction

• [

Semantic resolution levels

Decoder capabilities

Representation

7 purpose intentionality

6 function instrumentality

5 complex objects association objects

4 simple objects categorization

3 patterns classification fields2 observables perception

1 space-time framework awareness

A road map of region X A map of roads in region X7 Purpose Facilitate vehicular travel

planning and navigationIdentify and mitigate barriers to wildlife movements

6 Function Represent possible routes from place A to place B

Represent the locations where wildlife corridors intersect with roads

5 Composite objects A road network A wildlife corridor network intersecting with a road network

4 Simple objects Places, freeways, arterials, collectors, intersections, ramps, roundabouts,…

Roads, wildlife corridor segments, underpasses, culverts, high-conflict intersections,…

3 Classes Fields of properties (corresponding to surface material, slope, network structure,…) aggregated in diverse geometrical patterns

Fields of properties (corresponding to incident frequency, barrier permeability, height, width…) aggregated in diverse geometrical patterns

2 Observables Hard, rough, green, brown, wet,…

Open, blocked, green, hard, kill, dry, wet…

1 Space-time Exist. “Task-relevant information exists here-now at such-and-such appropriate granularity”

“Task-relevant information exists here- now at such-and-such appropriate granularity”

A review and assessment of land-use change models: dynamics of space, time, and human choice By Agarwal, Chetan; Green, Glen M.; Grove, J. Morgan; Evans, Tom P.; Schweik, Charles M. (2002) Gen. Tech. Rep. NE-297. Newton Square, PA: U.S. Department of Agriculture, Forest Service, Northeastern Research Station.

A somewhat similar idea from more practical folks…

The framework, 3 years later…

A micro-ontology generating engine?...

Ontology >> language for model design

ontologies are models of modelsmicro-theories are models a model is a micro-theory

modeling is a languagea model is a statement about the worldlanguage has semantics, syntax and pragmatics

building a model is design-ingdesigned things reflect designer’s purpose purpose is supported by function

A structure emerges…

Model designer purpose perspective

Syntactics structure

Semantics meaning

Pragmatics context

contextSS

Syntax

mea

sure

men

ts

Semantics Pragmatics

cont

ext

Purpose

Patterns

Unpacking the ‘Ontologies’ framework

Data structures Interpretations Micro-ontologies

The temporal extension

• One additional key ingredient:

• R-event• For each level, a change in information

that significantly alters the structure of GICs at that level• ‘significant’ is relative to purpose!

• “Information: a difference that makes a difference” Gregory Bateson

• And the R-event types by level are…

R-events change the context of the situation described

• [

Semantic resolution levels

Decoder capabilities

R-event types

7 purpose intentionality change of purpose orobjectives

6 function instrumentality functionally relevant change

5 complex objects association addition, loss of partsdissolution

4 simple objects categorization Identity changebehavior change

3 patterns classification relevant field properties change

2 observables perception Gestalt change

1 space-time framework awareness s-t framework switch

Adding uncertainties and times

• [

Semantic resolution levels

Uncertainties(examples)

Times

7 purpose relating to purposeand purpose change

experiential

6 function as to function:fitness, repurposing

discrete-event

5 complex objects assignment parts-to objects; changing part-whole relations

multiple clocksvarious timescales

4 simple objects linguistic factors ‘lifespans’

3 patterns classification clock time

2 observables perception punctuated time

11 space-time framework awareness timeless

Some features of the framework

Guides construction of micro-ontologies (and possibly process models)

Integrates design & analysis through user perspective

Adds context-relevant notions of time, change and uncertainty

Is compatible with much other work in geographic information science

And now, something more applied!

Geodesigning from the inside out

My advisor used to say…

“there is nothing as practical as a good theory”

Searching for practical solutions by becoming more abstract

What next?...

Tentative, but a different way of looking at geospatial representationContinue connecting with literatureFormalize!Try deriving micro-ontologies foruse with the Semantic WebExperiment with environmental and other process models