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The Semantic Web in an SMS
Onno Valkering, Victor de Boer, Awa Gossa Lô, Romy Valkering, Stefan Schlobach
Vrije Universiteit Amsterdam
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
Web for Development challenge
http://worldwidesemanticweb.org
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
allows for easy development of information services relevant to local
communities in their preferred language
Kasadaka (“talking box”)
Data store
GSM/Voice interface
Web Interface
Text-To-Speech / ASR
Radio users
Field operative
Users
Information Services
Market Information System (Mali)
Veterinary service (N-Ghana)
Knowledge base and diagnosis system
(CommonKADS)
Information sharing across locations
Poultry vaccination service (Mali)
Seed market (Mali and Burkina Faso)
Seed quality
Current cases
Machine to machine
information integration
GSM network
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
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)
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
Evaluation: 4 scenarios in 2 cases
Digivet and RadioMarche applications
Four scenarios / SPARQL queries in total
Results
Conclusions
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
Knowledge engineering, knowledge sharing for all
Towards a Computer Science for Development (CS4D)
Thank you
github.com/onnovalkering/sparql-over-sms
github.com/abaart/KasaDaka
kasadaka.com
worldwidesemanticweb.org
w4ra.org
v.de.boer@vu.nl
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
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.

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Semantic Web in an SMS as presented at EKAW2016

  • 1. The Semantic Web in an SMS Onno Valkering, Victor de Boer, Awa Gossa Lô, Romy Valkering, Stefan Schlobach Vrije Universiteit Amsterdam
  • 2. 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 Web for Development challenge http://worldwidesemanticweb.org
  • 3. 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 allows for easy development of information services relevant to local communities in their preferred language Kasadaka (“talking box”)
  • 4. Data store GSM/Voice interface Web Interface Text-To-Speech / ASR Radio users Field operative Users Information Services
  • 5. Market Information System (Mali) Veterinary service (N-Ghana) Knowledge base and diagnosis system (CommonKADS) Information sharing across locations Poultry vaccination service (Mali) Seed market (Mali and Burkina Faso) Seed quality Current cases
  • 6. Machine to machine information integration GSM network
  • 7. 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
  • 8. 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)
  • 9. 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
  • 10. Evaluation: 4 scenarios in 2 cases Digivet and RadioMarche applications Four scenarios / SPARQL queries in total Results
  • 11. Conclusions 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 Knowledge engineering, knowledge sharing for all Towards a Computer Science for Development (CS4D)
  • 13. 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
  • 14. 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.

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