LSWT2019 Talk by Simon Bin, AKSW/InfAI e.V.; Patrick Westphal, AKSW/InfAI e.V.

1. The QROWD Project
H2020 Project, December 2016 - December 2019
LSWT 2019
Simon Bin, Patrick Westphal, Claus Stadler, Gordian Dziwis (InfAI)
This project has received funding from the European
Union’s Horizon 2020 research and innovation
programme

2. Motivation
2

Information
services
{[]}
CSV
<?xml …>
…
“Data at rest”
“Data in motion”
Data
Integration
● Efficient data-driven
transportation and
mobility
● Reduce CO2 emissions
● Assess the quality of
infrastructure
● Enhance the quality of
life of citizens
Data Analytics
Inform
Optimize
2

3. The Consortium
3

- Business technology company
- Data generation and acquisition
- In-car location and navigation company
- Data and service provider
- Software architecture and Semantic
Web company
- Architecture design and integration of
QROWD platform
- Business and marketing consulting
company
- Dissemination, communication and
exploitation
- University of Southampton
- Human computation, crowdsourcing
and citizen science
- University of Trento
- Mobile app for citizen sensing/crowd
feedback
- Central storage infrastructure
- Research institute @Leipzig University
- Data transformation and integration
- Analytics
- Municipality of Trento
- Data provider
- End user role
3

4. The Model Region
4

City of Trento
● Capital of Trentino region (Italy)
● People: 120.000 (2017)
● Third largest city in the Alps;
second largest in the Tyrol
● Smartest mid-sized city in Italy (iCity Rate, 2017)
4

5. The Overall Data Flow 5
QROWD citizen
feedback component
{[]}
CSV
<?xml …>
…
…
Links
Represent-
atives
QROWD
Platform
…
???
Questions/answers
DBpedia,
LinkedgeoData, ...
LOD Cloud
QROWD data
conversion component
(SparqlIntegrate, ...)
QROWD analytics
components
(DL-Learner, ...)
QROWD linking and
fusion components
(LIMES, …)

6. Data Acquisition
6

● SparqlIntegrate
6
process.sparqlgenerate-workloads.script workloads.sparql emit.sparql
distributions
generates
references
adds
iterates workloads selects output triples
yields
references

7. 7

●
7
Linking and Fusion Use Case ‘Bike Rack’

8. Linking and Fusion Use Case ‘Bike Rack’
8

● Linking
on
geodata
8

9. Analytics Use Case ‘Modal Split’
9

9
● Task: Given commute information captured on citizens’
smartphones + (geospatial) background knowledge,
detect the citizens’ transportation mode
● Detected modes can be aggregated → ‘Modal split’
● Distinctive patterns based on implicit spatial relations
(near, starts near, ends near, runs along, passes, is within, crosses, …)
○ Commute ran along a line feature of type Railway→ transportation mode is probably
‘train’
○ Commute started and ended near POI of type BusStop → transportation mode is
probably ‘bus’
○ …

10. Analytics Use Case ‘Modal Split’ 10
QROWD citizen
feedback component
{[]}
CSV
<?xml …>
…
QROWD data
conversion component
(SparqlIntegrate, ...)
…
QROWD analytics components
(DL-Learner, ...)
- OWL Reasoning (via
off-the-shelf OWL
reasoner + optimizations)
- Spatial learning
extension
- Temporal learning
extension
QROWD linking and
fusion components
(LIMES, …)
Links
Represent-
atives
QROWD mobile app for citizen
sensing and feedback (i-Log)
QROWD
Platform
…GPS
data
???
…
Questions/answers
DBpedia,
LinkedgeoData, ...
LOD Cloud
Trento-specific
opendatasets

11. https://www.qrowd-project.eu
@QrowdProject
https://github.com/QROWD
Thank you!

12. Analytics Use Case ‘Modal Split’ 12
spatialReasoner.getSpatialIndividualsOnWhichRunsAlong(bikeRide)
> [ <http://linkedgeodata.org/triplify/way257254437>,
<http://linkedgeodata.org/triplify/way413052887>,
<http://linkedgeodata.org/triplify/way493758696>,
<http://linkedgeodata.org/triplify/way257254437> ]