This document proposes a framework that uses spatial coverage to discover geospatial semantics from volunteer geographic information (VGI) sources tagged with location data. The framework models tags using multiple facets including geographic features, classes, temporal data. It identifies tags and categorizes them using an algorithm based on whether tags are spatially covered in a photo's location. The framework aims to fill gaps in missing geographic characteristics for places with limited location data.

1. Centre Universitaire d’Informatique
Institute of Information Service Science
A Simple Tags Categoriza2on
Framework Using Spa2al Coverage
to Discover Geospa2al Seman2cs
Tardy, C., Moccozet, M., Falquet, G.
Workshop OD4LS - WWW2016 Companion
April 2016 Montréal, Canada

2. Filling the gaps
Tardy et al., University of Geneva 2

3. Filling the gaps
• Maps missing geographic features characteris@cs
• VGI sources with tags
• geographic and spa@al axis
• Non sta@s@cal approach
• Must work on places with small amount of data
Tardy et al., University of Geneva 3

4. Model
• Using tagged photos as source
• Mul@-facets model
• Algorithm by elimina@on
• Based on spa@al coverage
Tardy et al., University of Geneva 4

5. Spa@al Coverage
• A combina@on of seman2c and geographic
no@ons that describes precisely the spa2o-
temporal region shown in the picture.
• dis@nc@on between
– the geographic and spa@al content of a resource
– and its actual spa@al coverage.
Tardy et al., University of Geneva 5

6. Categories
Geographic
Features
Geographic
Class
Event
Temporal
Weather
Actor
Meta
Color
Tardy et al., University of Geneva 6

7. Algorithm
Geo
iden@fica@on
Tag in coverage
Yes No
Tag in category
No
Yes
Select
No
Select
Yes
Tardy et al., University of Geneva 7

8. Geo Iden@fica@on
Local Area &
Hierarchy mapping
Un@l match
Query GeoNames for each hierarchy
Neighbourhood
County
Region
Country
World
If match :
• Save geonames en@ty in database
• Associate with tag
• Save hierarchy level
Tardy et al., University of Geneva 8

9. Geo Iden@fica@on & coverage Example
https://flic.kr/p/m9ZBPB
In coverage :
à Théâtre Pitoëff
Tardy et al., University of Geneva 9

10. NonGeo Tag Iden@fica@on
1. Tag language detec@on using Wik@onary / WordNet
2. Tag disambigua@on:
1. Tags of same photo as set
2. Babelfy and BabelNet iden@fica@on grouped by language
3. If needed disambigua@on Geo – nonGeo comparing both
weights
Tardy et al., University of Geneva 10

11. NonGeo Tag Categorisa@on
• Using BabelNet categories extracted from
Wikipedia
• or by Hand
• Associate each tag to a category
Event
Temporal Weather
Actor
Meta
Color
Tardy et al., University of Geneva 11

12. Tag Categorisa@on Example
Actor
Event
Temporal
Color
Meta
The rest of the tags are :
• Ambiance
• Nightlife
• Music / Musique
• Concert / Gigs
• Shows
• Fes@val
Tardy et al., University of Geneva 12

13. Geo feature target
• Flickr photo loca@on info :
<loca2on la2tude="46.193959"
longitude="6.143385” accuracy="16" context="0"
place_id="EDcBbVFWWrj07WE” woeid="782861">
• OpenStreetMap
Tardy et al., University of Geneva 13

14. Missing / Wrong Characteris@cs
Tardy et al., University of Geneva 14

15. Finding characteris@cs
Tardy et al., University of Geneva 15
The rest of the tags are :
• Ambiance
• Nightlife
• Music / Musique
• Concert / Gigs
• Shows
• Fes@val
In coverage :
à Théâtre Pitoëff

16. Conclusion
• Works on geographic zones with low density of
resources
• Can be used as a pre-treatment to sta@s@cal
approach
• Enhancement of spa@al descrip@ons in geo-
services (Citygml, OpenStreetMap)
• Precise queries in search engines
Tardy et al., University of Geneva 16