Slides for the presentation at the MEPDaW workshop during ESWC 2017.

1. Ruben Taelman - @rubensworks
imec - Ghent University
Versioned Triple Pattern Fragments
A Low-cost Linked Data Interface Feature for Web Archives

2. Let’s go back in time...
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3. Keynote by Axel Polleres - On Archiving Linked and Open Data
30/05/2016 ~ 11:00AM - MEPDaW 2016
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4. Keynote by Axel Polleres - On Archiving Linked and Open Data
30/05/2016 ~ 11:00AM - MEPDaW 2016
Importance of dataset evolution,
preservation and time-based access
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5. Keynote by Axel Polleres - On Archiving Linked and Open Data
30/05/2016 ~ 11:00AM - MEPDaW 2016
Research challenges:
…
How can certain time-specific queries over
archives be answered?
...
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6. How can certain time-specific queries over
archives be answered at Web scale?
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7. Structure of this presentation
Requirements for a Web-scale RDF archive interface
Interface implementation
Demonstration of the interface
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8. How can certain time-specific queries over
archives be answered at Web scale?
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9. How can certain time-specific queries over
archives be answered at Web scale?
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10. Different ways to query RDF archives
Version Materialization (VM)
Query a single version
Delta Materialization (DM)
Query differences between 2 versions
Version Query (VQ)
Query all versions,
results are annotated with their versions
Ferná ndez 2016
“Which books were present at time X?”
“Which books were added and removed
between times X and Y?”
“At what times was book A present?”
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11. How can certain time-specific queries over
archives be answered at Web scale?
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12. How can certain time-specific queries over
archives be answered at Web scale?
VM, DM and VQ queries
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13. How can VM, DM and VQ queries
over archives be answered at Web scale?
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14. How can VM, DM and VQ queries
over archives be answered at Web scale?
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15. Web scale
Allowing a large amount of concurrent clients
to query published datasources on the Web
with a high availability.
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16. Triple Pattern Fragments (TPF) enable
SPARQL querying with a low publication cost
TPF interfaces enable triple pattern queries to Linked Datasets
Both human and machine-readable (hypermedia)
Clients evaluate SPARQL queries on their own,
using these interfaces
→ Lowers server load compared to SPARQL endpoints
Verborgh 2016
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17. How can VM, DM and VQ queries
over archives be answered at Web scale?
using TPF
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18. How can VM, DM and VQ queries
over archives be answered using TPF?
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19. Some work has been done already into making
RDF archives queryable using TPF
Query different dataset snapshots with Memento (VM)
Continuous queries based on time-annotations (VQ)
Vander Sande 2016
Taelman 2016
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20. No generic interface that supports all types
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21. No generic interface that supports all types
yet...
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22. VTPF
Versioned Triple Pattern Fragments
A versioning feature for Triple Pattern Fragments
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23. VTPF enables VM, DM and VQ triple pattern queries
Additional forms and hypermedia controls.
TPF
VTPF VM
VTPF DM
VTPF VQ
subject, predicate, object
subject, predicate, object,
version
subject, predicate, object,
versionStart, versionEnd
subject, predicate, object
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24. We introduce the “version” vocabulary
for VTPF hypermedia controls and results
Each version is seen as a different dataset
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25. http://versioned.linkeddatafragments.org/bear
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Demo time

26. 26
Human-readable Version Materialization
http://versioned.linkeddatafragments.org/bear?subject=http%3A%2F%2Fd.opencalais.com%2Fer%2Fcompany%2Fralg-tr1r%2Fa2472e23-94d8-38e1-9d31-f5d7dd336eaa&predicate=&object=&versionType=VersionMaterialized&version=&versionStart=&versionEnd=

27. 27
http://versioned.linkeddatafragments.org/bear?subject=http%3A%2F%2Fd.opencalais.com%2Fer%2Fcompany%2Fralg-tr1r%2Fa2472e23-94d8-38e1-9d31-f5d7dd336eaa&predicate=&object=&versionType=DeltaMaterialized&version=&versionStart=&versionEnd=
Human-readable Delta Materialization

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http://versioned.linkeddatafragments.org/bear?subject=http%3A%2F%2Fd.opencalais.com%2Fer%2Fcompany%2Fralg-tr1r%2Fa2472e23-94d8-38e1-9d31-f5d7dd336eaa&predicate=&object=&versionType=Version&version=&versionStart=&versionEnd=
Human-readable Version Query

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<http://versioned.linkeddatafragments.org/bear#dataset> hydra:search
_:patternVersionMaterialized.
_:patternVersionMaterialized hydra:template
"http://versioned.linkeddatafragments.org/bear?versionType= VersionMaterialize
d{&subject,predicate,object,version}";
hydra:variableRepresentation hydra:ExplicitRepresentation;
hydra:mapping _:subject, _:predicate, _:object.
_:subject hydra:variable " subject";
hydra:property rdf:subject.
_:predicate hydra:variable " predicate";
hydra:property rdf:predicate.
_:object hydra:variable " object";
hydra:property rdf:object.
_:patternVersionMaterialized hydra:mapping _:version.
_:version hydra:variable " version";
hydra:property ver:relatedVersion.
Machine-readable hypermedia controls

30. 30
<http://versioned.linkeddatafragments.org/bear?versionType=VersionMaterialized&version=8 > a
ver:Version.
Each version is a regular TPF entrypoint
http://client.linkeddatafragments.org/#datasources=http%3A%2F%2Fversioned.linkeddatafragments.org%2Fbear%3FversionType%3DVersionMaterialized%26version%3D8&query=CONSTRUCT%20WHERE%20%7B%0A%20%20%3Fs%20%3Fp%20%3Fo.%0A%
7D%0ALIMIT%20100
So you can query against all versions using a TPF-enabled client

31. But we’re not done yet!
We considered the interface for querying RDF archives
We still need
mapping to RDF archive query language(s)
can we come up with a standard?
appropriate back-end storage solutions
snapshot, change-based, time-based, hybrid?
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