1. Semantic enrichment of
Volunteered Geographic
Information using Linked Data:
a use case scenario for disaster
management
by Stanislav Ronzhin
Supervisor: Rob Lemmens
Professor: Menno-Jan Kraak
Reviewer: Marian de Vries

2. • Problem
• Linked Data at work
• Research Question & Objectives
• Use case
• Research steps
• Results
• Discussion
• Conclusion
Content

3. Problem -1
“…information is very directly about saving lives…”
“we found the data was very rich and could be
used for various other products that would aid in
the response"
Sir John Holmes,
The UN Emergency Relief Coordinator
“tweets were as accurate as official reports…;
they were also at least two weeks faster”
Chunara et al., 2012
Mr. Andrej Verity, an UNOCHA
veteran

4. Problem -2
From Minu Kumar LIMBU, 2012
Expert Survey: Knows vs. Uses

5. Problem -3
From Minu Kumar LIMBU, 2012
Why did the service not help?

6. Problem -4
“…lives were saved…”
Ushahidi Independent Evaluation
To sum up:
• Unstructured data
• Lack of interoperability
• Semantic heterogeneity
• Uncertainty & reliability
=
Problems of
crowdsourced
content

7. LD at work - 1
Linked Data to the rescue!

8. • Smart and elegant
• Graph Data Model
• Subject-Predicate-Object statements – LEGO!
LD at work -2
Resource Description Framework (RDF)
Relational DB Graph DB

9. LD at work - 3

10. LD at work -5

11. Linked Data:
• Structured data
• Integration between any
sources
• Formal semantics
• Giant Open data
repository
• Sophisticated
informational retrieval
LD at work -6
Problem:
• Unstructured data
• Lack of interoperability
• Semantic heterogeneity
• Uncertainty & reliability
• Keyword search

12. +
=
• Can we cross the bridge with a 12 ton fire truck?
• How to reach the closest operating hospital avoiding road
blockages and who is in charge at that place?
LD at work -7
Volunteered Geographic Information

13. To what extent can the Linked Open Data cloud help
to semantically enrich Volunteered Geographic
Information in order to better answer queries in the
context of crisis and disaster relief operations?
Research Question & Objectives
Objectives:
1. To integrate VGI into the LOD cloud
2. To evaluate methods for the construction of semantic queries
3. To evaluate the results

14. Use case-1
Chile earthquake, 2010
1. The 6th largest ever
2. Magnitude of 8.8
3. 80% of population
4. Tsunami
5. 525 casualties

15. Russia, 2010
Haiti, 2010
Chili, 2010
Christchurch, 2011
Libya, 2011
Australia, 2011
Kenya , 2007
South Africa, 2008
Louisiana, 2010
Serial INCIDENT TITLE INCIDENT DATE LOCATION DESCRIPTION CATEGORY LATITUDE LONGITUDE APPROVED VERIFIED
1636 200 need food
and water in
Laboule.
20/01/2010
10:46
Laboule, Port-
Au-Prince
AIDE POUR LA FONDATION
REGARS SISE A LABOULE
COORDONER PAR PETIT HOMME
STANEL NOUS AVONS ENVIRON
200 PERSONNES
~~~~~~~~~~~~~~~~~~~~~~~~~
We need help for the Regars
Foundation located in Laboule,
directed by Stanel Petit-Homme.
We have about 200 people who
need help.
~~~~~~~~~~~~~~~~~
Category: 4a. Health services
2b. Penurie d'eau |
Water shortage, 2a.
Penurie d'aliments |
Food Shortage, 3c.
Besoins en materiels et
medicaments | Medical
equipment and supply
needs,
18.49282 -72.3069 YES NO
Use case-2

16. Research steps -1
Step 1: to convert Ushahidi into RDF

17. Research steps-2
Step 1: to convert Ushahidi into RDF
Column name Value
Serial number 4349
Incident title SERVICIO DE SALUD CONCEPCIÓN
FUNCIONANDO
Incident date 3/4/2010 11:08:00 PM
Location Concepcion, Chile
Description Hospital Guillermo Grant Benavente ,
Hospital Traumatológico ,
Hospital de Lota ,
Hospital de Coronel
Category 4a. Servicios de Salud,
Latitude -36.8148
Longitude -73.0293
Approved YES
Verified NO
Thematic
Spatial
Spatial
Temporal

18. Research steps-3
Step 1: to convert Ushahidi into RDF
The MOAC
vocabulary
The Dublin Core
vocabulary
LinkedGeoData

19. Research steps-4
Step 1: to convert Ushahidi into RDF
http://linkedgeodata.org/triplify/node988381631
http://linkedgeodata.org/triplify/way126614190
http://linkedgeodata.org/triplify/way223990111
http://linkedgeodata.org/triplify/way124859821
"http://observedchange.com/moac/ns/
#HospitalOperating"
Thu Mar 04 23:08:00 CET 2010

20. Research steps -5
Step 2: to construct queries for a semantic enrichment

21. Research steps -6
Step 2: to construct queries for a semantic enrichment
LinkedGeoData – geometry and classification

22. Research steps -7
Step 2: to construct queries for a semantic enrichment
DBpedia – background information

23. Research steps -8
Step 3: to evaluate emerged data management techniques
Before After
Ushahidi class
Keyword search
MOAC
Machine readable
Multi criteria filtering
Literal date
Keyword search
Date
Temporal distance
Machine readable
One point per report
Keyword search
Georeferencing
Spatial querying
Interoperable geometry

24. Results-1
Extraction of geometries of blocked roads
# TITLE DATE LOCATION DESCRIPTION
CATEGO
RY
LAT/LO
NG
4730
Ruta 5 sur
esta cerrada/
Ruta 5 South
Closed
3/11/
2010
17:48
Rancagua,
Chile
Road N. 5 closed due to
the structural damaged
inflicted by the
aftershocks
1a.
Estructur
a
Colapsad
a,
-
34.034
2/
-
70.705
6

25. Results-2
Selection of officials of populated places where operating
hospitals are located

26. Prioritizing of the reports based on the density of
population.
Results-3

27. Conclusion
“Challenge is to provide an appropriate categorization
(with sufficient explanation) so that volunteers can
classify correctly from the beginning…” An UNOCHA veteran

28. Conclusion
• MOAC – domain knowledge - WHAT
• LGD – location – WHERE
• Spatial data access
• LOD is an integrated dataspace
• Approach can be applied to any VGI

29. Discussion
• Integration with LOD mitigates human factor
• Need for LD interface
• Remote SPARQL endpoint is a black box
• GeoSPARQL implementation in Virtuoso
• GeoSPARQL lacks KNN

30. Thank you!
Questions!?

31. Methodology – Software
• TripleGeo
• SILK Workbench
• RDF Online Validator
• Parliament
• iSPARQL
• Parliament
• Tabulator
• SIMILE Widgets

32. Background – Linked Data resources
• DBpedia – Wikipedia in RDF
• GeoNames – Gazetteer, 10 million toponyms
• LinkedGeoData (LGD) – OSM in RDF
• WordNet & GeoWordNet - lexical database + Geo extension
http://lod-cloud.net/versions/2014-08-30/lod-cloud_colored.png

33. Methodology – data conversion to RDF

34. Methodology – data conversion to RDF

35. Background – Ontologies and Vocabularies

36. URI – to name things, concepts
OWL – to express formal
semantics
SPARQL – query language
RDF – to wrap information
Hypothesis-4

37. Example of queries:
• Find me all the KFC restaurants located in less than 1 km from
the school(s) where Barack Obama had classes.
• Find me names of famous singers who lived in municipalities
along river Rhine and who used word “Rhine” in their songs.
LD in work -6

38. The MOAC vocab
Haitian
Earthquake 2010
Chilean Earthquake 2010
Category
number
English
name
Category
number
Spanish
English
translation
MOAC terms
Prefix MOAC:
<http://obser
vedchange.com/
moac/ns/#>
Terms
from other
vocabularie
s
1
Emergen
cy
1
Emergen
cia
Emergency
MOAC:Emergen
cy
5a
Collapse
d
structure
,
1a
Estructur
a
Colapsad
a
Collapsed
structure
MOAC:Collapse
dStructure
1b Incendio Fire MOAC:Fire
1c
Trapped
people
1c
Personas
atrapada
s
Trapped
people
MOAC:PeopleTr
apped
1e Tsunami Tsunami
http://onto
logi.es/Wor
dNet/data/T
sunami

39. Report as a graph

40. Report as a graph

41. Results-2
Selection of bridges located in 10-km proximity to
compromised bridges