Spatial Groundings for Meaningful Symbols

Spatial Groundings for Meaningful Symbols

Stefan Dietzea, Vlad Tanasescub

a Knowledge Media Institute, The Open University, Milton Keynes, UK, [email protected] Cultures of Legibility project, School of Arts, The University of Edinburgh, [email protected].

Meaning – Similarity - Interoperability

<owl:Class rdf:ID="Color">

<rdfs:subClassOf>

<owl:Class rdf:ID="PhysicalQuality"/>

</rdfs:subClassOf>

</owl:Class>

<Color rdf:ID="Lilac"/>

<owl:Class rdf:ID="Colour">

<rdfs:subClassOf>

<owl:Class rdf:ID="Quality"/>

</rdfs:subClassOf>

</owl:Class>

<Colour rdf:ID="Purple"/>

?

Symbol grounding problem:

Ontological symbols lack meaningful grounding in reality (or cognitive dimensions)

Stefan Dietze & Vlad Tanasescu - Spatial Groundings for Meaningful SymbolsWorkshop on Matching and Meaning, AISB, April 9th 2009, Edinburgh, UK



<rdfs:subClassOf>


</rdfs:subClassOf>

</owl:Class>



<rdfs:subClassOf>


</rdfs:subClassOf>

</owl:Class>



Ontological symbols lack meaningful grounding in reality (or cognitive dimensions) Reasoning about similarity between symbols difficult


?



<rdfs:subClassOf>


</rdfs:subClassOf>

</owl:Class>



<rdfs:subClassOf>


</rdfs:subClassOf>

</owl:Class>


linguistic similarity?

linguistic similarity?

structural similarity?


Ontological symbols lack meaningful grounding in reality (or cognitive dimensions) Reasoning about similarity between symbols difficult Ontology interoperability needs to be based on (a) manual mappings or (b) linguistic or structural similarities (costly and error-prone) Need for grounded symbols allowing for implicit similarity representations


Meaning – Similarity - Interoperability Two approaches to more meaningful symbols…

…based on grounding ontologies in:

(a) Conceptual Spaces (b) Processual Spacetime


Instance I1j Instance I1i

Concept C1x is-a

refined-as-cs

refined-as-member refined-as-member d1

d2 d3

is-a

Ontology O1

Conceptual Space CS1x

...<owl:Class rdf:ID="Color"><rdfs:subClassOf><owl:Class rdf:ID="PhysicalQuality"/></rdfs:subClassOf>

</owl:Class><Color rdf:ID="Lilac"/>...

01010010100… {211; 169; 127}

11100010001… {228; 197, 8}

10001110100… {237; 177; 73}

Observed real-world parameter (e.g. color)

Sensor-data based on measurements(e.g. HSL values)

Ontological Knowledge(e.g. OWL individual of particular color)

L

S H



01010010100… {211; 169; 127}

11100010001… {228; 197, 8}

10001110100… {237; 177; 73}

car

user1

user2

user3

wheelswheel

travel

vehicle

Grounding ontologies in Conceptual Spaces


Concept C1x is-a

refined-as-cs


d2 d3

is-a

Ontology O1



Enhancing ontologies through CS-based representations (through CS ontology)

Multiple CS (each representing one concept C in one ontology O),

Instances I of C represented as members M in CS.



Enhancing ontologies through CS-based representations (through CS ontology)

Multiple CS (each representing one concept C in one ontology O),

Instances I of C represented as members M in CS.

d2

Instance I2i Instance I1i

CS12x

Concept C1x

is-a refined-as-cs

refined-as-member refined-as-member

Concept C2x

refined-as-cs

d1

d3

Ontology O1 Ontology O2

is-a

Agent 1 Agent 2 Similarity-computation between instances across ontologies by means of spatial distances

Requires common agreement at concept (i.e. CS) level…

… common case due to (a) usage of upper-level ontologies or (b) usage of ontologies within (virtual) organisations.


Concept C1x is-a

refined-as-cs


d2 d3

is-a

Ontology O1




Several applications utilising CS-based groundings for:

Ontology interoperability





Semantic Web Service discovery / interoperability

SWS1:OU-youtube

O1:Purp O1:Env

SWS2:entertain-youtube

O2:Purp O2:Env

SWS3:open-video

O3:Purp O3:Env

SWS4: bbc-backstage

O4:Purp.

O4:Env

WS1:OU-youtube

WS2:entertain-youtube

WS3:open-video

WS4:bbc-backstage

SWS6:get-video-request

M61={v1, v2, v3} M62

={v1, v2}

SWS5: mobile-youtube

O5:Purp.

O5:Env

WS5:mobile-youtube

CS2 Environment SpaceCS1 Purpose Space





Semantic Web Service discovery / interoperability

Bridging between sensor data and symbolic ontologies

SWS1:OU-youtube

O1:Purp O1:Env

SWS2:entertain-youtube

O2:Purp O2:Env

SWS3:open-video

O3:Purp O3:Env

SWS4: bbc-backstage

O4:Purp.

O4:Env

WS1:OU-youtube

WS2:entertain-youtube

WS3:open-video

WS4:bbc-backstage

SWS6:get-video-request

M61={v1, v2, v3} M62

={v1, v2}

SWS5: mobile-youtube

O5:Purp.

O5:Env

WS5:mobile-youtube

CS2 Environment SpaceCS1 Purpose Space



01010010100… {211; 169; 127}

11100010001… {228; 197, 8}

10001110100… {237; 177; 73}

Observed real-world parameter (e.g. color)

Sensor-data based on measurements(e.g. HSL values)

Ontological Knowledge(e.g. OWL individual of particular color)

L

S H



01010010100… {211; 169; 127}

11100010001… {228; 197, 8}

10001110100… {237; 177; 73}

Grounding ontologies in Processual Spacetime I


car

user1

user2

user3

wheelswheel

travel

vehicle

Processual spacetime :

Extreme Tagging Systems (ETS) :

• Instances considered as Processes in space and time rather than Things.• Concepts as abstract process occurrences, i.e. probabilities that a process has a given set of characteristics.• Copresence or cooccurrence relations: indicator of a significant probability that entities occur in the same spacetime region. E.g. `university’ and `campus’.

• Entities are the result of discrimination (Harnad 1990), they are differences.• Collaborative tagging systems provide an appropriate model for descriptive discrimination.•Extreme Tagging Systems (ETS), or Tagging the Tags, provides an appropriate model for spatiotemporal process exploration. How?

Grounding ontologies in Processual Spacetime IIWe adopt an associative network model in which a limited number of mental relations can be used to describe the association processes:

• Co-occurrence: significant probability of co-presence of the two elements in a region of spacetime. E.g. ‘car’ and ‘wheels’.• Similarity: the concepts appear similar. E.g. ‘pub’ and ‘bar’.• Cause-effect: causal relation. E.g. ‘weapon’ and ‘death’• …


• Application: in the Cultures of Legibility project, interview data about places and itineraries is projected on an ETS for automatic inference


More during the poster session…

…or via:

[ http://people.kmi.open.ac.uk/dietze ]

[ http://archipelagos.eu (soon)]

http://people.kmi.open.ac.uk/dietze

http://archipelagos.eu/

Education

Spatial Groundings for Meaningful Symbols