modelling collaborative semantics with a geographic recommender christoph schlieder

Modelling Collaborative Semantics with a Geographic Recommender

Christoph Schlieder

SeCoGIS WorkshopNovember 7, 2007, Auckland

Lehrstuhl für Angewandte Informatik in denKultur-, Geschichts- und Geowissenschaften

Otto-Friedrich-Universität Bamberg

Schlieder: Modelling Collaborative Semantics 08-2

Chair of Computing in the Cultural Sciences

Laboratory for Semantic Information Processing

A geographic place

Bamberg UNESCO world

heritage site




A different conceptualization

Bamberg beer capital of

Bavaria




Yet another conceptualization

da

ily.e

lsch

.eu

Bamberg ?




Conceptual modelling

Place concepts „Bamberg“, „Southern

Germany“, „Europe“, … Thematically and spatially

different conceptualizations

Issues Formal semantics of place

concepts Data about different

conceptualizations

Contributions Semantic analysis based on

multi-object (!) tagging User similarity data from a

geographic recommender




Tripost Recommender




Part 1Geo-information communities

Part 2Collaborative Semantics

Part 3Geographic Recommender




Geo-information communities

Information community Gould & Hecht (2001)

A Framework for Geospatial and Statistical InformationOGC white paper

An information community is a group of people who share a common geospatial feature data dictionary (including definitions of feature relationships) and a common metadata schema.

Gould & Hecht (2001)




Example

Cadastral Communities Data and process models 27 national cadastral

authorities in the EU 1 community designing the

Cadastral Reference Model

Ontological engineering One ontology per

information community Cadastral Reference ModelLemmen et al. (2003)





High quality 30 experts from cadastral

agencies, GIScience and Knowledge Engineering

Description logic-based modelling (OWL-DL)

High cost 4 years for understanding

and modelling property transaction processes

COST G9Modelling real property

transactions




Ontological engineering

Hess, Schlieder (2006)

Ontology-based Verification

of Core Model Conformity, CEUS

National cadastral data model+ intended correspondences

Core Cadastral Data Model+ conformity constraints

Conformity checker (OWL-DL)




Information communities

Traditional view Each information community

defines its ontology Number of communities

or ontologies << 100 Complex conceptualization

uses DL role restrictions

Semantic boundaries Ontologies come with crisp

semantic boundaries The Greek cadastral model

is not the Danish model Semantic Web technologies

are appropriate (OWL-DL)




Collaborative geodata acquisition

Social Web Communities of users who

collect geospatial data

Collaborative mapping GPS biking trail libraries

Morris et al. (2004), Matyas (2007)

Public domain street mapswww.openstreetmap.org

dense data for London

sparse data for Brussels

ww

w.o

pen

stre

etm

ap

.org

ww

w.o

pen

stre

etm

ap

.org




Collaborative metadata acquisition

Social tagging Categorization of geospatial

data by a community Keywords („tags“) describe

spatio-temporal coverage and content type

Folksonomies folk taxonomy

= tag vocabulary www.geograph.org.uk




Tagging as categorization task

tagged by data producerfarm track




Tag frequency

Example 422.895 images 2.784 categories (tags)

Power law frequency rank - 36% tags used once only

24% tags used 2-5 times Most frequent tag used

17.360 times

rank tag

1 Church

2 Farmland

3 Farm

…

2782 Windmill stump

2783 Luminous object in space (sun)

2784 Penstock

www.geograph.org.uk




Spatial coveragew

ww

.pan

ora

mio

.com

/pho

to/2

014

27

Neuschwanstein POI in Google maps




Folksonomies

Low cost Categorization by voluntary

contributors (non-experts)

Low quality No controlled vocabulary

house vs. housemanson vs. manor house

misclassifications

Misclassificationby a non-expert




The semantics of tags

User tagging Not just the contributor but

all users provide tags Conflicting tags (!)

Semantic analysis Ternary semantic relation

for user tagged data

Classical view

tagging(object, tag)

Gruber (2005)

tagging(object, tag, user)




Multi-object tagging

Collection of objects

place nametag

Semantic analysis

tagging({obj1,…,objN}, tag, user)




Bug or feature?

Quality problem (bug) Serious for folksonomies Even more serious if user

tagging is permitted Unmanageable for multi-

object user tagging?

Consequence Use folksonomies only as

the poor man‘s ontology




Bug or feature?

Data source (feature) Multi-object user tagging

informs us about different conceptualizations

Consequence Invert the task of finding a

tag for a multi-object Find n objects from a

collection of m >> n to illustrate a (place) concept




The semantics of multi-object tags

Hypothesis Selection is based on two

conflicting criteria Typicality: choose typical

instances of the concept Variablity: show the

variability of the concept

violation of the variability criterion




Empirical data

Bamberg Patchwork Postcards

0

10

1 3 5 7 9 11 13 15 17 19 21 23

rank

freq

uenc

y





Issues How can we describe the

semantics of place concepts? How do we obtain data about

different conceptualizations?

Selection task Selection seems based on two

conflictin criteria: typicality and variability

Multi-object tagging User tagging of multi-objects

informs about the place concepts of individual users

tagging({obj1,…,objN}, tag, user)




Recommender systems

Item-to-item similarity recommendationswww.amazon.com




Multi-object recommendation

Use case The user selects images

and captions for a patchwork postcard.

The system generates other patchwork postcards with appropriate captions

www.wiai.uni-bamberg.de/tripost

TriPost Webservice




Tags of a single user

Bamberg

Cardiff

Dublin

Antwerpen

Anna‘s multi-object tags




Feature similarity

Anna Bill Clio Don Emma Franz

Bamberg 1-3-6 2-4-5 1-4-6 2-3-4

Cardiff 2-3-5 1-4-6 2-4-6 1-4-5 1-2-3

Southern Germany

1-2-6 2-4-6 3-5-6 1-3-6 4-5-6 1-2-6

Southeast of England

2-4-5 2-5-6 1-3-5 1-2-3 2-5-6

sim(A,B) = |A∩B| / |A∪B|2/3 0.66




User-to-user similarity

Anna Bill Clio Don Emma Franz

Anna .66 .22 .66 .33 .66

Bill .66 .22 .33 .44 .77

Clio .22 .22 .55 .66 .42

Don .66 .33 .55 .33 .55

Emma .33 .44 .66 .33 .33

Franz .66 .77 .42 .55 .33




Spatial similarity

Spatial Partonomy Users visiting a similar

selection of places are considered similar

Example: Europe in 7 daysWhich Countries? Which Cities? Which Fotographs?

Printed patchwork postcard




Computing similarity

Measures Feature similarity

e.g. Tversky measure User-user similarity

e.g. averaged feature similarity

Central idea User-to-user similarity in the

selection task is interpreted as a measure for shared conceptualization

Information community The community of a user u

consists of the k users most similar to u.




Tag Communities

A

D F

B

C

E

C

AB

A

D F

B

C

E

E

FD

F 3-neighbors(C) C 3-neighbors(F)

Fuzzy semantic boundary2-, 3-, 4-community?




Conclusions

Issues Formal semantics of place concepts Data about different conceptualizations

Contributions Semantic analysis based on multi-object (!) tagging User similarity data from a geographic recommender

Consequences Tagging communities are different from information

communities




Conclusions

Folksonomies modeling of semantics before the emergence of information

communities before crisp semantic boundaries have been established

Semantic Web ontologies modeling of semantics after that phase they assume crisp semantic boundaries

modelling collaborative semantics with a geographic recommender christoph schlieder

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