florian a. twaroch institute for geoinformation and cartography, tu vienna...

Florian A. TwarochInstitute for Geoinformation and Cartography, TU Vienna

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Naive Semantic Interoperability

Florian A. Twaroch

Institute for Geoinformation & CartographyVienna University of Technology

Annual Scientific Meeting – GeoGERAS 2006Pernegg, Austria

03rd July 2006

Florian TwarochInstitute for Geoinformation and Cartography, TU Vienna

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MotivationMotivation BackgroundBackground HypothesisHypothesis MethodMethod ConclusionConclusion

Overview

● Problem Definition● Humans Conception of Space● Hypothesis● Review of the Literature● Sandbox Geography

– How to organize concepts?– How to change concepts?

● Conclusions● Outlook


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Motivation BackgroundBackgroundBackgroundBackground Hypothesis Method Conclusion

How to Build a Concept ?

• Experience

• Humans hold several concepts

• Concepts underlie change


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Semantic Interoperability

• … the transition of two mental models …

• Shared reality

• Objectivity (Frank & Mark 1996)


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Related Work

● Similarity Measures (Tversky 1977, Rodriguez & Egenhofer 2004)

● Formal Specifications of Interoperability with Image Schemata– Linguistic (Frank 1998, Frank & Raubal 1998)– „Toy Spaces“(Rodriguez & Egenhofer 1997)– „Real Space“(Rodriguez & Egenhofer 2000,

Rüetschi & Timpf 2005)(Gassner 1997)


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Questions

● How do we acquire image schemata about space?

● How can we overcome linguistic and meta-cognitive constraints ?


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Motivation Background HypothesisHypothesisHypothesisHypothesis Method Conclusion

Hypothesis

Spatial concepts (as needed for semantic interoperability) are pre-linguistic

concepts that can be described by a set of axioms.

Are pre-linguistic concepts universal ?


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Motivation Background Hypothesis MethodMethodMethodMethod Conclusion

Developmental Psychology

• Theory theory: “Infants learn about the world by forming and revising theories. Their conceptual development is theory formation and change, their semantic development is theory dependent.”

• Development is driven by three forces

– Innate knowledge– Powerful learning abilities– Scaffolding from others

(Meltzoff & Gopnik 2002)


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How to Build a Concept - Revisited


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Multi-Tiered Model (Frank 2000)

perceptions

cognitive objects

agents


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Multiple Processors – Multiple StrategiesUse Case: Location Coding

● Newcombe and Huttenlocher (2000)

Self-referencedExternally referenced

Simple, limited

Sensorimotor learning (egocentric learning, response learning)

Cue learning

Complex,

powerful

Dead reckoning (inertial navigation)

Place learning


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Further Modelling Aspects

● Bottom up vs. Top Down = Symbolic vs. grounded

● Deterministic vs. Stochastic

● Embodied vs. Disembodied

● Single agent vs. multi agent system

● Hierarchic vs. heterarchic


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Sandbox Geography

● YES– Classification of Empirical Models– Abstract Models of Empirical Studies– Algebraic Specifications providing set of axioms for spatial relations

between objects– A symbolic mechanism for conceptual change

● NO– Cognitive architecture like SOAR, ACT-R, etc.– Model of the infant, tool for psychology– Artificial intelligence


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Type Amount

Support


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Theories for the Support of Objects


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Sandbox Geography II

1. Expansion2. Contraction3. Revision / Combination4. Analogy

3.

1.

4.

2.

xx

(Sowa 2003)


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Reasoning Towards New Theories

TRUERule explains

event

TRUE AND FALSERule explains and

does not explain event

FALSERule does not explain event

TRUE OR FALSERule is not tested

(enough) with eventT/F = 50 %

T > 50 %

T < 50 %


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Observations and Expectations

1. T Rule gains weight (store observations to rule made before)

2. F Rule looses weight (store observations to rule made before)

3. FF Rule becomes invalid pool4. TT Rule is valid a gains weight5. TF Verified hypothesis is falsified

Neutral/Action?6. FT Falsified hypothesis is verified

Neutral/Action?7. TTF A chain were verification outweighs

falsification Rule is true8. FFT A chain were falsification outweighs

verification Rule is false


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Properties of a Agent Based Framework

● Infant does not give up immediately – certain resistance against theory change (cf. Kuhn 1976)

● Pool of falsified theories (cf. Siegler and Chen 2002)

● An axiom that proofs a theory to be wrong is a case of specialization / generalization Detection of the Non Euclidian Geometry


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Summary

● Formal Specifications for Naive Interoperability have to consider– Experience (History)– Multiple Strategies, i.e. multiple overlapping concepts– Incremental Knowledge Representation– Mechanism to convert Knowledge Representations

● A framework with algebraic specifications suits these needs when embedded in a heterarchy– Contraction– Expansion– Revision– Analogy


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Motivation Background Hypothesis Method ConclusionConclusionConclusionConclusion

Conclusion

● The big question remains how to connect the simple parts of object ontologies.

● We point out some crucial aspects of the modeling process.

● Agent based simulations help to get sound theories in developmental psychology, we need human subject testing on various aspects.

● Psychology serves as an input for new computational models in other disciplines.


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Motivation Background Hypothesis Method ConclusionConclusionConclusionConclusion

To be continued …To be continued …

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