This document discusses enabling Semantic Web data exchange over SMS by translating SPARQL queries to SMS messages. It evaluated different RDF serialization and compression techniques for representing small Linked Data sets in SMS messages. Experiments showed n-triples with gzip works best for datasets under 40 triples, and Turtle with gzip compresses larger datasets best. Removing redundant triples through shared vocabularies provided additional compression. This approach allows knowledge sharing and basic machine-to-machine information integration using the GSM network where internet is not available.
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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”)
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
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)
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.