Slides for my keynote address for the joint session of the SALAD workshop and DBPedia day at SEMANTiCS2017. The talk addresses the need for research into the opportunities and challenges for Linked Data in the context of ICT for Development. It shows current work on Kasadaka, Semantic Web in an SMS and sneakernets http://salad2017.linked.services/ http://semantics.cc

1. Semantic Technology for Development:
Semantic Web without the Web?
Victor de Boer
Vrije Universiteit Amsterdam
With input from: Anna Bon, Christophe Gueret, Stephane Boyera , Nana Baah Gyan, Chris van Aart, Max
Froumentin, Aman Grewal, Mary Allen, Amadou Tangara, Etienne Barnard, Hans Akkermans, Julie
Ferguson, Marije Visscher,…

2. Victor de Boer
Web & Media Group, CS, VU University Amsterdam
Netherlands Institute for Sound and Vision
Web Science
Cultural Heritage and Digital History
ICT for Development

4. CAUTION!
DIGITAL DIVIDE AHEAD

5. Internet users per continent
Source: International Telecommunication Union
(ITU)

6. Linked Data for Development?

7. Can the (Semantic) Web (be made to) mean
something for knowledge sharing even under
very constraining conditions?
No internet, no computer, no electricity
Multitude of languages, levels of literacy
http://worldwidesemanticweb.org

9. Example: Landportal
http://landportal.info and http://landportal.sbc4d.com

10. Published by FAO
Example: Agrovoc/Agris

11. http://linked-development.org/
Example: Institute for Development Studies’
Eldis database

12. ELDIS Link to GeoNames
IDS: document 0002 Country:”Gambia”
Geonames:Gambia
Region: Africa
population : 1593256
N 13° 30' 0'' W 15° 30' 0'

13. Links to DBPedia
IDS: document 0001 Theme:”Food Security”
DBPedia:”Food Security”
Analysis of approaches to
understanding and addressing food
security issues; examination of the
structural causes of food insecurity
and different policy responses
Theme:” Food aid emergencies ”
Person:”David Pimentel”
Organisation:”FAO”
“Voedselzekerheid”@NL

14. IATI Sector:”Higher Education”
Organisation : UN Habitat
Activity: Multi donor fund to support civil
society in democracy related issues
Aid received (USD)
Geonames:Gambia
Worldbank:Gambia
N 13° 30' 0'' W 15° 30' 0'
Example: Linked Data for International Aid
Transparency Initiative (IATI)
Kasper Brandt

15. http://iati2lod.appspot.com/
4. How does violent conflict in recipient countries affect aid activities?
5. How does aid spending as registered in the IATI standard compare to World
Bank indicators?

16. Hm..

17. Information sharing should be made
1. usable on small, affordable,
hardware deployed in various
connectivity contexts;
2. accessible to individuals with
varied cultural backgrounds /
literacy levels;
3. relevant and directly useful to
the target public they aim to
empower.
Infrastructure
Interface Relevancy

18. Infrastructure
RelevancyInterface

19. https://www.flickr.com/photos/worldbank/7826373720
Mobile phones

20. Community radio

21. Low-literacy
One of the grand challenges of ICT4D
Prevalent among the rural poor

22. Speech: The most
natural user interface
http://www.holisticlc.com/speech-therapy/

24. M-agro Use Case
Context: Regreening in Africa

25. Tominian region
Mali

27. Market Data store
GSM/Voice interface
Web Interface
Text-To-Speech / ASR
Radio users
Field operative
Users
Information / Communique Services

28. “Slot and Filler” Text-to-Speech
English:
Bambara:
15 liters of offered by Zakari Diarra
15_ba.wav L_ba.wav Of_ba.wav
Spoken Language
Elements Repository
honey

29. Talking to LOD
Web applications
<VoiceXML> to SPARQL
Voice browser
RadioMarché
Linked market data
‘Allo, Linked
Data?
DBpedia
GeoNames
Agrovoc

30. rm:offering0001
rm:shea_butter
rm:1000
rdfs:label
rdfs:label “Amande de Karité”@fr
“Shea Nuts”@en
speakle:voicelabel_ba
rm:audio_shea_nl.wav
rm:audio_shea_ba.wav
speakle:voicelabel_nl
rdfs:label
“1000”
speakle:voicelabel_ba
rm:audio_1000_nl.wav
rm:audio_1000_ba.wav
speakle:voicelabel_nl
rm:Mazankuy_Diarra
rm:kilo
rdfs:label “kilo”@en
speakle:voicelabel_ba
rm:audio_kilo_nl.wav
rm:audio_kilo_ba.wav
speakle:voicelabel_nl
rm:has_contact
Speakle voice labels

31. RadioMarché leads to new questions,
new actions, new knowledge
Honey sales doubled in
Tominian since RadioMarche;
Sheabutter sales lagged
behind, despite the same
approach in RadioMarche
Source: Sahel Eco Amadou Tangara internal
Evaluation report EVP – Radio Marché

32. Infrastructure
Interface Relevancy

33. Weather Service (Ghana, Mali, Burkina Faso)
DigiVet (N-Ghana)
Radio message service (Mali)
Poultry vaccination service (Mali)
Milk service (Mali
Seed market (Mali and Burkina Faso)

34. Veterinary service (N-Ghana)
Knowledge base and diagnosis system
CommonKADS
Information sharing across locations
DigiVet

35. Mr. Meteo

36. Mali Milk

37. Linked Data’s flexible data model fits the
``downscaling’’ aspect of CS4D well
Data is locally relevant…
…but might be shared across regions…
…across locally co-developed applications…
…and aggregated to country/global level

38. Interface Relevancy
Infrastructure

39. http://www.nytimes.com/2008/12/16/health/16incubators.html?pagewanted=1&_r=3&partner=permalink&exprod=permalink

40. Appropriate, sustainable hardware
affordable,
low-power-consuming,
repairable,
durable,
ownable,
Total Cost of Ownership (TCO)

41. Example: Efficient Knowledge sharing
with SemanticXO and ERS
Christophe Guéret

42. ENTITY REGISTRY SYSTEM (ERS)
• Fully decentralised Linked Data publication platform
• Works under any kind of connectivity context
• Tracks back individual edits back to their authors
• Simple and versatile
• Open Source https://github.com/ers-devs
• Low resource demanding
... and open for contributions so don't
hesitate to fork it!

43. Voice service: Multiple solutions
SIP over Ethernet
HTTP
Officeroute
running Asterisk
Orange Emerginov Platform
GSM dongle
DOWNSCALING
Cheap, small
hardware

44. Foroba Blon 2.0
Kasadaka

45. Low-resource knowledge sharing platform
Low-power, ubiquitous, cheap hardware
FLOSS components
Rapid prototyping + deployment of (knowledge-intensive) services
User Interfaces
Voice services
SMS-based
Visual
Wifi, 3g or GSM network
RDF Data store (Linked Data) allows for
flexible data integration across applications, deployments
Kasadaka (“talking box”)
Andre Baart

46. Machine to machine
information exchange
GSM network

47. SPARQL in an SMS
Enable (Semantic) Web data exchange
over GSM networks.
Practical differences HTTP and SMS:
SMS works with phone number, HTTP works with URLs.
SMS has a size restriction, HTTP practically has none.
SMS is one-way messaging, HTTP follows request-
response.
Basic M2M communication based on
SPARQL.
Onno Valkering

48. SPARQL in an SMS
Converters to translate SPARQL HTTP request to SMS
message (140 or 160 chars) and vice versa
CONSTRUCT, INSERT/DELETE DATA
Challenges
Blending synchronous and asynchronous messaging
SPARQL/ RDF compression
Unpredictable query result sizes

49. Compression for small datasets experiments
Strategies
Different serializations: RDF/XML, N-triples, Turtle, HDT1, EXI2
Compression (zip)
Assume shared vocabularies (top 20 from prefix.cc) and
remove redundant (inferenced) triples (RDFS reasoning)
Evaluated on real-world datasets LOD Laundromat3
232,822 small datasets (1-1,000 triples)
[1] Fernández et al. “Compact Representation of Large RDF Data Sets for publishing and exchange” (ISWC 2010)
[2] Käbisch et al. “Standardized and Efficient RDF Encoding for Constrained Embedded Networks” (ESWC2015)
[3] http://lodlaundromat.org/ and Rietveld et al. “LOD Lab: Experiments at LOD Scale “ (ISWC2015)

50. Triples
N-triples
+gzip
RDF/
XML
RDF/XML+
Gzip Turtle
Turtle+G
zip HDT
HDT+
Gzip EXI
EXI+
Gzip
Best +
vocabulary
-based
1-10 50.7 103.8 77 102 70.3 495.5 180.1 57.5 65.9 44.2
11-20 22.5 62 27.1 50.5 24.2 122.2 47 23.3 24.9 18.9
21-30 16.2 58.2 18.5 48.7 16.3 79.5 31.1 16.5 17.5 13.6
31-40 28.3 69.1 30.9 62.1 28.6 86.5 40.7 28.2 29.1 23.5
41-50 9.8 51.2 10.2 42.3 8.6 38.1 14.8 9.3 9.7 8
51-60 17.2 59.2 17.5 50.1 15.9 50.5 22.8 15.8 16.3 8.7
61-70 11.8 58.5 12.4 42.4 10 43 17.7 11.1 11.6 6
71-80 8.8 54.8 8.5 40.9 7 31.6 11.2 7.5 7.8 6.4
81-90 6.7 52 6.3 40.6 5.1 25.4 9.1 5.8 6 4.4
91-100 8.1 54.9 7.6 40.4 6.2 26.9 9.7 6.8 7 5.7
101-200 8.8 62 8.3 39.2 6.7 24.7 10.1 7.6 7.9 5.7
201-300 4.8 50.8 3.6 39 2.8 13.4 4 3.6 3.6 2.7
301-400 4.8 51.5 3.3 37.7 2.5 11.4 3.3 3 3.1 2.5
401-500 4.4 51.5 2.9 37.4 2.2 10.4 2.7 2.6 2.7 2.2
501-600 5 53.8 3.4 38.7 2.5 8.9 3 2.9 3 2.4
601-700 4.1 51 2.5 35.9 1.7 8.5 2.2 2.3 2.4 1.7
701-800 4.5 51.1 2.7 36.2 1.9 8.1 2.1 2.4 2.4 1.9
801-900 4.4 51.1 2.6 36.4 1.8 7.9 1.9 2.3 2.3 1.8
901-1000 4.1 50.9 2.4 36.5 1.7 7.7 1.7 2.1 2.1 1.7
801-900 4.4 51.1 2.6 36.4 1.8 7.9 1.9 2.3 2.3 1.8
901-1000 4.1 50.9 2.4 36.5 1.7 7.7 1.7 2.1 2.1 1.7
For very small datasets (<40 triples), n-triples + gzip works best
For larger datasets Turtle+gzip compresses best
Removing redundancies using shared vocabularies adds additional compression

51. Compression experiments results
Number
of SMSes
Avg.
number
of triples
1 0
2 3
3 8
4 16
5 24
6 84
7 98
8 126
9 189
10 301
For very small datasets (<40 triples), n-triples + gzip works best
For larger datasets Turtle+gzip compresses best
Removing redundancies using shared vocabularies adds additional compression
0
10
20
30
40
50
60
70
80
Compression(percentagewrtn-triples)
Size of dataset in triples (binned)
N-triples +gzip
Turtle+Gzip
Best + vocabulary-based

52. Evaluation: 4 scenarios in 2 cases
Digivet and RadioMarche applications
Four scenarios / SPARQL queries in total
Results

53. Linked Data over
Sneakernet / Driveby wifi
Master project Fahad Ali

55. Semantic Web over SMS is feasible
using data compression + semantic background knowledge
economically feasible for ICT4D services for small datasets
Semantic Web without the Web is possible
cf. IOT
Radio Data System (RDS), LoRa,..

56. Dev. countries can leapfrog directly into the
information age,
jumping many phases of immature technologies
Img: flickr/n3v3rv0id

57. From ICT4D to Development Informatics
Img: flickr/n3v3rv0id
KR4D, LD4D, HCI4D, …
Test Computer Science hypotheses in
domains/environments

58. Thank you
@victordeboer
v.de.boer@vu.nl
http://w4ra.org
http://worldwidesemanticweb.org

59. One more example

60. Aid accountability in Nepal
Marije Visscher
Social Scientist
Aske Robenhagen
Computer Scientist

61. Citizens
Community frontline
associates (CFAs)
Accountability Lab
Office
Donors, Journalists
Surveys, focus groups. Collected in forms.
Reports from the field, solutions.
Aggregated reports, accountability

62. Example data

63. Citizens
Community frontline
associates (CFAs)
Accountability Lab
Office
Donors, Journalists
Smartphone - Voice System - SMS
Report1
हसकल्पनाम्यामआ
न्जिननयरहरुद्वारागाब
geo:Kavra
Report2
Knowledge graph