Paths towards the Sustainable Consumption of Semantic Data on the Web
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Paths towards the Sustainable Consumptionof Semantic Data on the Web
Aidan Hogan & Claudio Gutierrez
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Growth of the Linked Data Cloud
Oct. 2007
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Growth of the Linked Data Cloud
Oct. 2007 Nov. 2007
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Growth of the Linked Data Cloud
Oct. 2007 Nov. 2007Feb. 2008
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Growth of the Linked Data Cloud
Oct. 2007 Nov. 2007Feb. 2008Sep. 2008
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Growth of the Linked Data Cloud
Oct. 2007 Nov. 2007Feb. 2008Sep. 2008Mar. 2009
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Growth of the Linked Data Cloud
Oct. 2007 Nov. 2007Feb. 2008Sep. 2008Mar. 2009July 2009
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Growth of the Linked Data CloudOct 2007Oct. 2007Nov. 2007Feb. 2008Sep. 2008Mar. 2009July 2009Sept. 2010
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Growth of the Linked Data Cloud
Oct. 2007 Nov. 2007Feb. 2008Sep. 2008Mar. 2009July 2009Sept. 2010Sept. 2011
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Growth of the Linked Data Cloud
Oct. 2007 Nov. 2007Feb. 2008Sep. 2008Mar. 2009July 2009Sept. 2010Sept. 2011Sept. 2012
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Growth of the Linked Data Cloud
Oct. 2007 Nov. 2007Feb. 2008Sep. 2008Mar. 2009July 2009Sept. 2010Sept. 2011Sept. 2012Sept. 2013
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Growth of the Linked Data Cloud
Oct. 2007 Nov. 2007Feb. 2008Sep. 2008Mar. 2009July 2009Sept. 2010Sept. 2011Sept. 2012Sept. 2013June 2014
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Growth of the Linked Data Cloud
One new dataset in the past year
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In loving memory of
Linked Data
2007–2012
Survived by its research
community
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Access Methods
• Client has a request/query Q• Server has a dataset D• Client issues Q to server• Server computes and returns response Q(D)
D
HTTP
Q
Q(D)
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Access Methods
Q
QDD
DQ
• Multiple clients / multiple servers (blurred)• Remote, decentralised, uncoordinated• Web scale Anything else like this?
Q
D
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Access Methods (over the web)
“The Web is not a database”-- Alberto Mendelzon, slides from a presentation at the Workshop on Internet Data Management, Washington,
November 1998.
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Linked Data Access Methods
1. Dereferencing:– Look up a URI, get an RDF document
2. Dumps:– Get all data in an archive
3. SPARQL Queries:– Send a query, get the answers
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DEREFERENCING
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Dereferencing (what is it?)
Q = “http://dbpedia.org/resource/Columbia”Q(D) =
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Dereferencing (what’s wrong with it?)
• Responses vary from server to server– local triples where URI is subject (83%) vs.– local triples where URI is subject or object (55%)
[Hogan et al. 2012]
WELL-DEFINED: For a given Q and D, clients and servers agree on what Q(D) should be.
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Dereferencing (what’s wrong with it?)
• Very coarse:– Give me all capitals of South American
countries.• Dereference documents for all country URIs• See which ones are in South America, throw away rest• Throw away triples other than capitals
GRANULAR: The language for Q allows the client to be specific so as to avoid wasting bandwidth
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Dereferencing (what’s wrong with it?)
• No pagination:– Give me some information about Italy.• Load document with 100,000 triples• Throw away 99,900 triples the user won’t read
PAGINATION: A large response Q(D) can be split into chunks
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DUMPS
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Dumps (what are they?)
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Dumps (what’s wrong with them?)
• 15× compression for RDF achievable• But same weaknesses as for deref. still apply
[Fernández et al. 2012]
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Dumps (what’s wrong with them?)
• 15× compression for RDF achievable• But same weaknesses as for deref. still apply• Also, no standard access methods:– Various compressions and formats– Linked through generic HTML
[Fernández et al. 2012]
ACCESSIBLE: The protocol and formats are defined for automatic access by software agents
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SPARQL
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SPARQL (what is it?)
“What’s the name of the query language again … what’s that? Oh yes, SPARQL.”
-- C. Mohan, June 2014, Cartagena, AMW Summer School
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SPARQL (what is it?)Q =
Q(D) =
PREFIX dbo: <http://dbpedia.org/ontology/>...
SELECT ?capitalWHERE { ?s a dbo:Country ; dbp:capital ?c ; dcterms:subject category:Countries_in_South_America . ?c rdfs:label ?capital . FILTER (lang(?capital)="en")}
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SPARQL (to the rescue?)
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SPARQL (what’s wrong with it?)[Buil Aranda et al. 2012]
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SPARQL (what’s wrong with it?)[Buil Aranda et al. 2012]
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SPARQL (what’s wrong with it?)
• Simple Protocol and RDF Query Language• SPARQL evaluation: PSPACE-complete• SPARQL 1.1 even more complex
[Perez et al. 2009]
[Arenas et al. 2013]
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SPARQL (what’s wrong with it?)
COSTABLE: The cost of processing a query can be anticipated before actual processing.
CACHEABLE: Common requests can be cached and re-used. Queries can be anticipated.
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SPARQL (what’s wrong with it?)
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SPARQL (what’s wrong with it?)
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SPARQL (what’s wrong with it?)
• Simple Protocol And RDF Query Language
• Protocol always expects a perfect answer– No support for partial results, timeouts,
exception handling, pagination …
ROBUST: Access method can gracefully handle error cases and provide Quality of Service
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SPARQL (what’s wrong with it?)
• D is a black-box for the user
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SPARQL (what’s wrong with it?)
Q =
Q(D) =
PREFIX dbo: <http://dbpedia.org/ontology/>SELECT (COUNT(?c) as ?count)WHERE { ?c a dbo:Country .}
TRANSPARENT: The client can determine if a dataset D is relevant and the service sufficient.
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WRAP-UP
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Problem Categories
1. Standardised2. Bandwidth-efficient3. Server-processing-efficient4. Usable by client
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Conclusion
• Data access methods crucial– Access = Protocol + Query Language
• But current ones don’t work→ need something else (or multiple things?)– Open question: