Social Emergent Semantics for Personal Data Management

Social Emergent Semanticsfor Personal Data Management

Cristian Vasquez ( cvasquez[at]vub.ac.be )

Semantics Technology and Applications Research Lab

Vrije Universiteit Brussel

Agenda:

● Motivation● Personal Data management● Use case

● Shared Ontology Views● Blackboard anatomy● Experiment dynamics● Summary

Motivation

Use case

Let's suppose....

… that in a far away country... A bar that is frequently visited by

sailors...

And they exchange experiences...

Motivation

These sailors enjoy talking about:

Practical things:

● Geographical information● Journey advice● Weather● hazards...

Use case

● Histories about their trips● Gossip● Big sea monsters● Phantom ships● Mermaids...

And not so practical things:

Motivation

Use case

These sailors would like to share information such as

● Maps● Drawings● Travel logs etc

Which are useful to their community of sailors.

Motivation

Let's suppose that....

They count with:

Advanced technological devices,And they use them to record and

store movies,photographs, sound, geographical information etc.

On all their journeys.

Motivation

● Every sailor has its own way of organizing its information

● It's already difficult for them to find their own information... since the volume is huge.

● Data is not well structured

The problem:

These sailors would like to share information with other sailors.

Motivation

Measurements, (I.e: 'coordinates')

Current solutions:

To 'Attach' pieces of information (structured or not) to other pieces of information, in order to find and manage them. 'Metadata'

Written symbols (I.e: 'tags')

Models, (I.e: 'taxonomies')

Motivation

Sharing information is easier with the help of:● Structured meta-data● Artifacts that reflect our agreements (ontologies)

● But to come up with agreements, is already a difficult task.

Motivation

Mermaids appear in the folklore of many cultures including east, europe, china and india, they are usually considered dangerous, and are associated with floods storms, shiprecks and drownings. However in other folk traditions, they can be benevolent and can fall in love with humans

● Example:

● Tree of our sailors want to share the pictures and position of the mermaids that they have seen

Sharing information is easier with the help of:● Structured meta-data● Artifacts that reflect our agreements (ontologies)

● But to come up with agreements, is already a difficult task.

● Sailor 1 (Greek)● Sailor 2 (British isles)● Sailor 3 (Slavic)

Motivation

Sailor 1 (greek): - These creatures are called 'seirines'- They live in the sea- They are woman- They have beautiful and long hair.- They have enchanting voices

Motivation


Sailor 2 (british isles): - These creatures are called 'mermaids'- They live in the sea- They are woman- They can be giant- They don't have inmortal souls

Motivation



For sailor 1 & 2, is direct to share artifacts about woman that live in the sea...

Motivation



Sailor 3 (Slavic):- These creatures are called 'Rusalkas'- They live in the sea- They are woman- They do not have a fish-like tail- They are beautiful young women with long green hair

Motivation

Sailor 1 (greek): - These creatures are called 'seirines'- They live in the sea- They are woman- They have beautiful and long hair.- They have enchanting voices- They DO have a fish-like tail

Sailor 2 (british isles): - These creatures are called 'mermaids'- They live in the sea- They are woman- They can be giant- They don't have inmortal souls- They DO have a fish-like tail

Sailor 3 (Slavic):- These creatures are called 'Rusalkas'- They live in the sea- They are woman- They do not have a fish-like tail- They are beautiful young women with long green hair

Sailors learn gradually from the conceptualizations of others.....

Motivation

Example: How we can store, classify and annotate digital data about?

● Sailor 1 (Greek) 'seirines'● Sailor 2 (British isles) 'Mermaids' ● Sailor 3 (Slavic):'Rusalkas'

● To make agreements can be easier for some domains than for others.

● Example: can be easy for these sailors to agree about:● System of coordinates for the islands.● Weather conditions (distinct types of weather).● Price of a good.

In order to share it?

● But it may be difficult to come up with agreements about personal (custom) data.

Tail

Application

Application

Blackboard networks

● Users interact through multiple 'canvas' or 'blackboards', in order to build 'semantic bridges'● These networks are constructed incrementally, and organically.● Network objective: To build and represent local agreements, collaboratively.

is aPart of

Proposal:

seirines

Mermaid

State of art

● Essential components:

● Semantic desktop (I.e [1] Nepomuk Framework)● Personal Information Model (PIMO) a local

'ontology' to annotate our personal data.

[1] http://nepomuk.semanticdesktop.org/nepomuk/

http://nepomuk.semanticdesktop.org/nepomuk/

State of art

An ontology view is not just a portion of a complete ontology. Rather is a collection of concepts and relationships that allows a unique representation by some participants of a certain domain. In the same way as ontologies, ontology views may be described using metadata representation languages such as RDF, RDFs and OWL among others. They evolve using change operators that allow coherent ontology view mutations.

ServiceShared

Ontology

MermaidsOntology Variant

seirinesOntology Variant

Example of elicitation of local ontology

● How to elicit custom ontologies?

Sailor 1 Sailor 2

● Ontology views

Elizabeth Chang, Tharam S Dillon, and Ling Feng. Modeling Ontology

Views : An Abstract View Model for Semantic Web. Proceedings of

the First International IFIP/WG12.5Working Conference on Industrial

Applications for Semantic Web (IASW), pages 227–246, 2005.

Referent (observed subject)

ConceptualizationThought + Observer

Symbols

Ontology views in the Web:

We want to describe our referents, toBe used by computers

● Structured descriptions● Identified referents (observed subjects)

'seirines'

●These 3 components cannot be separated!

Ontology views in the Web + personal dataspaces

Sailor 2's personal

dataspace

Sailor 1's Perspective

Ontology ViewIe: rdf schema seirines

(terminology)Mermaids

(terminology)

Sailor 1(british) Sailor 2(greek)Shared

Entity URI

Research proposal: Web blackboards

Blackboards can be seen as extensions of a semantic wiki web page, where participants collaboratively describe a subject using distinct description mechanisms and formalisms. A participant is allowed to subscribe to multiple blackboards, contributing content in order to converge into acceptable conceptualizations. The blackboards collected by an user constitute a network what he can bind directly with his own Personal data (extending his Personal Information Model)

Ontology views in the Web + personal dataspaces.

How to manage them?


ConceptualizationThought + Observer A

ConceptualizationObserver B + Thought

RepresentationLayer

Observers

Web Blackboard

(Public space)

Blackboard'sMetadata

Symbols Symbols

Blackboard as a playground

● Multiple of observers● Multiple representation layers

Anatomy of a blackboard




Language(practical)

Measures(empirical)

Models(ontology)

NaturalLanguage

Controlled Vocabulary

RDF

Semantic layer

Empirical layer

Pragmatical layer

Observers

Web Blackboard

(Public space)


Observer B private space

Multi Layer Blackboard variant Example





Language(practical)

Measures(empirical)

Models(ontology)

NaturalLanguage


RDF

Semantic layer

Empirical layer

Pragmatical layer

Observers

Web Blackboard

(Public space)





Written symbols (I.e: 'tags')




Language(practical)

Measures(empirical)

Models(ontology)

NaturalLanguage


RDF

Semantic layer

Empirical layer

Pragmatical layer

Observers

Web Blackboard

(Public space)





Measurements, (I.e: 'coordinates')




Language(practical)

Measures(empirical)

Models(ontology)

NaturalLanguage


RDF

Semantic layer

Empirical layer

Pragmatical layer

Observers

Web Blackboard

(Public space)





Models, (I.e: 'taxonomies')




Language(practical)

Measures(empirical)

Models(ontology)

NaturalLanguage


RDF

Semantic layer

Empirical layer

Pragmatical layer

Observers

Web Blackboard

(Public space)







RepresentationLayer

Observers

Web Blackboard

(Public space)


Symbols


Is related to


RepresentationLayer

Observers

Web Blackboard

(Public space)


Blackboards as a network

● Relations to other blackboards (links)● Wiki paradigm variant

● Sailor 3 (Slavic):'Rusalkas'● Sailor 1 (Greek) 'seirines'● Sailor 2 (British isles) 'Mermaids'

'Without tail''With tail'


• Users can relate blackboards using relationships such as causality, location function etc. forming a network. Pattern analysis is used then to provide feedback to the communities, increasing their awareness. • During the interplay within a blackboard, there will be cases where some participants disagree with others regarding some representation. Thus agreement mechanisms can be used in order to reach convergence.

• If the distinct participant's views become irreconcilable, then the blackboard itself may diverge into distinct variants, intended to capture distinct semantics.

Blackboard networks

Tail

Application

Application

Blackboard networks

● user constructs a perspective via selecting distinct blackboard variants● are decentralized● are constructed incrementally in an organic way (emerging)

is aPart of


seirines

Mermaid

“Is-a” relationship

cycle

Application

Application

is a

is a

is a


Blackboard networks

● Since one user only have a partial view of the blackboard network, ● We need mechanisms to promote awareness

● One possibility is pattern recognition

“part of”Relationship

pattern

Application

Application

Part of

Part of

Part of


Blackboard networks

● Since one user only have a partial view of the blackboard network, ● We need mechanisms to promote awareness

● One possibility is pattern recognition

Application

Application

User context


Application: An user augments their own Personal Information Model Ontology (PIMO) by means of binding their own concepts to the subjects described within the blackboards

Application

Application

User context

LOD cloud


Application: An user links elements from Linked Open Data to their own view of blackboards, creating 'bridges' to query for example using local terminology.


Semantic layer

Empirical layer

Pragmatical layer

Observers

Web Blackboard

(Public space)


Blackboard dynamics


Semantic layer

Empirical layer

Pragmatical layer

Observers

Web Blackboard

(Public space)


V1 V2 V3 Snapshot

1

1

1 2

1 2 3 4

1

2

Delta based versioning

Blackboard dynamics


Semantic layer

Empirical layer

Pragmatical layer

Observers

Web Blackboard

(Public space)


V1 V2 V3 Snapshot

1

1

1 2

1 2 3 4

1

2

O O O O

M M M M

S S S S

E

P

1

E E E

P P P1 1 1

0 0

0

2

1

1

Snapshot based versioning

Blackboard dynamics

All the layers are versioned together forming a

snapshot that is identified as a whole (With an URI).


Semantic layer

Empirical layer

Pragmatical layer

Observers

Web Blackboard

(Public space)


V1 V2 V3 Snapshot

1

1

1 2

1 2 3 4

1

2

O O O O

M M M M

S S S S

E

P

1

E E E

P P P1 1 1

0 0

0

2

1

1


Blackboard dynamics

LocalBlackboard

clone

Sailor'sStaging

area

Sailor'sWorking space

Users interacts selecting some blackboards and pulling them to their local spaces, where they can augment or use the blackboards. if they make contributions then they have to push them through multiple stages.


Semantic layer

Empirical layer

Pragmatical layer

Observers

Web Blackboard

(Public space)


V1 V2 V3 Snapshot

1

1

1 2

1 2 3 4

1

2

O O O O

M M M M

S S S S

E

P

1

E E E

P P P1 1 1

0 0

0

2

1

1


Blackboard dynamics

LocalBlackboard

clone

Sailor'sStaging

area

Sailor'sWorking space

A draft space or playground with no constraints

Expect consistency &

some degree of agreement the

local community

Users interacts selecting some blackboards and pulling them to their local spaces, where they can augment or use the blackboards. if they make contributions then they have to push them through multiple stages.


Semantic layer

Empirical layer

Pragmatical layer

Observers

MermaidWeb Blackboard


Blackboard dynamics

● Managing inconsistency

● Sailor 1 (Greek) 'Seirines'● Sailor 2 (British isles) 'Mermaids'

Example:

● They live in the sea● They are woman


Semantic layer

Empirical layer

Pragmatical layer

Observers


VariantA

0

VariantB

0

Blackboard dynamics

- They DO have a fish-like tail

- They do NOT have a fish-like tail

● Sailor 1 (Greek) 'Seirines'● Sailor 2 (British isles) 'Mermaids' ● Sailor 3 (Slavic):'Rusalkas'



Example:


Semantic layer

Empirical layer

Pragmatical layer

Observers


VariantA

0

VariantB

0

Blackboard dynamics

Why divergence is useful?

● Irreconcilable world views● Practical reasons

● (I.e distinct degrees of complexity needed)


Sometimes we don't want global InteroperabilityOur scope is our community.



Example:


Semantic layer

Empirical layer

Pragmatical layer

Observers

RootWeb

Blackboard


VariantA

VariantA

VariantB

0

0 1

VariantB

0

Variants mutate independently

Blackboard dynamics


Semantic layer

Empirical layer

Pragmatical layer

Observers

RootWeb

Blackboard


VariantA

VariantA

VariantB

0 1

0 1

VariantA

VariantB

VariantB

1

0

Blackboard dynamics


Semantic layer

Empirical layer

Pragmatical layer

Observers

RootWeb

Blackboard


VariantA

VariantA

VariantA

VariantB

0 1 1

0 1

VariantA

VariantC

VariantB

VariantB

VariantB

1

0

1

0

Convergence example:

Blackboard dynamics

- MAY have a fish-like tail


●With computer aided support:

● I.E: Relationship pattern recognitionB2

B1

B3

B4

How can we support convergence?

● 'Seirines' & 'Mermaids' very similar to 'Rusalkas' → suggest MAY have a fish-like tail


Semantic layer

Empirical layer

Pragmatical layer

Observers

RootWeb

Blackboard


VariantA

VariantA

VariantA

VariantB

0

0 1 1

0 1

VariantA

VariantC

VariantB

VariantB

VariantB

1

0

1

Service layer Services

1

0

Services

Blackboard dynamics

Why versioning and convergence is useful?

● Its easier to construct and maintain services

Service layer example:

The experiment

The experiment

Nepomuk Framework to● Local metadata-extraction● PIMO management

The experiment


Semantic media Wiki + iMapping● Blackboard description interface

(This is under evaluation)

The experiment


Semantic media Wiki + iMapping● Blackboard description interface

(This is under evaluation)

JGIT● Dataspace versioning ●Convergence and divergence capability

The experiment

RDF as representation model

● Fundamental 'glue' to put all the pieces together ● Straightforward possibility to use the Web as publishing and distribution mechanism.

Summary

• This framework explores notions such as personal context and emergent semantics, making use of artifacts such as blackboards that can diverge and converge in order to support meaning evolution, in order to improve our personal data management capabilities.

• In this work we don't aim to distill global semantics. Instead we want our own semantics, taking as hypothesis that they are incrementally constructed by our close communities.

Questions?

C3

C1

C2

C5C4

Blackboard network traceability, Things to look at:

● Concept Emergence - Removal● Concept abstraction - Specialization● Semantic Distance ( Hops between concepts ) ● Concept resistance and speed of change.

C3

C1

C2

C5

B1

C4

B2

B4B3

Example: Proselytizing

Indicator that counts how concepts are propagated transversally through two branches

First prototype

Date post:	17-Dec-2014
Category:	Health & Medicine
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Social Emergent Semantics for Personal Data Management

Health & Medicine