DBpedia Archive using Memento, Triple Pattern Fragments, and HDT

Herbert Van de Sompel & Miel Vander Sande
CNI Spring Meeting, San Antonio, TX, April 5 2016
Herbert Van de Sompel
@hvdsomp
Los Alamos National Laboratory
Acknowledgments: Lyudmila Balakireva, Harihar Shankar, Ruben Verborgh
Access to DBpedia Versions using
Memento and Triple Pattern Fragments
Miel Vander Sande
@Miel_vds
Ghent University

Outline
• Prelude: Memento and Linked Data
• First Generation DBpedia Archive
• Devising Affordable/Useful Linked Data Archives
• Intermezzo: Triple Pattern Fragments (TPF)
• Intermezzo: Binary RDF Representation (HDT)
• Devising Affordable/Useful Linked Data Archives
• Second Generation DBpedia Archive
• Try this At Home

Memento Framework

Memento LDOW 2010 Submission
Herbert Van de Sompel et al. (2010) An HTTP-Based Versioning Mechanism for Linked Data
http://arxiv.org/abs/1003.3661

Memento and Linked Data

Time-Series Analysis across DBpedia Versions
Data collected through “follow your nose” HTTP Navigation

First Generation DBpedia Archive: Storage

First Generation DBpedia Archive: Storage
Characteristics
upload software
custom
upload time
~ 24 hours per version
storage software
MongoDB
storage space
383 Gb for 10 versions
DBpedia versions
10 versions: 2.0 through 3.9
number of triples
~ 3 billion

First Generation DBpedia Archive: Subject-URI Access

http://dbpedia.mementodepot.org/memento/2009052/http://dbpedia.org/page/Oaxaca

Characteristics
TimeGate software
custom
access type
Subject URI & datetime
external integration
current DBpedia
clients
• all clients: direct access to
Memento Subject-URI
• Memento clients: datetime
negotiation with Subject-URI

DBpedia Archive @ LANL Since 2010
• Access based on Subject-URI (DBpedia Topic URI) only
• MongoDB storage
• A blob per Subject-URI per version
• Dynamically transformed to other RDF serializations
• No updates since version 3.9 (2013) of DBpedia as a result of
scalability problems
!!!
!!!

Affordable & Useful Linked Data Archives
• A Linked Data Archive consists of temporal snapshots of one or
more Linked Data sets, whereby each temporal snapshot reflects
the state of a Linked Data set at a specific moment or interval in
time.
• How to make Linked Data Archives accessible in a manner that is
• affordable/sustainable for the publisher
• useful for the consumer

Linked Data Archive: Characteristics
General Characteristics Publisher Consumer
Availability
Bandwidth
Cost
Functionality
Interface Expressiveness
LOD Integration
Memento Support
Cross Time/Data
Verdict:
• Publication perspective: $$$$
• Access perspective: ++++

Linked Data Publishing
• The typical ways of publishing Linked Data on the Web:
• Subject URI access
• Data dump
• SPARQL endpoint
Let’s consider these from the perspective of Linked Data Archives,
i.e. archival storage and access

Linked Data Archive with Subject-URI Access
• For each temporal snapshot of a Linked Data set, and for each
Subject in that snapshot, publish an RDF description (of the Subject)
at a URI that is specific per snapshot/subject

Linked Data Archive with Subject-URI Access: Characteristics
Availability rather high rather high
Bandwidth ~ description ~ description
Cost rather low rather high
Functionality
Interface Expressiveness rather low
LOD Integration yes
Memento Support possible
Cross Time/Data follow your nose
Verdict:

Linked Data Archive Using Dumps
• Renders each temporal snapshot of a Linked Data set as a data
dump that places all temporal dataset triples (as they were at a
specific moment in time) into one or more files

Linked Data Archive Using Dumps: Characteristics
Availability high high
Bandwidth high high
Cost low high
Functionality
Interface Expressiveness download dataset
LOD Integration no
Memento Support not possible
Cross Time/Data download various datasets
Verdict:

Linked Data Archive with SPARQL Endpoint(s)
• For each temporal snapshot of a Linked Data set, supports arbitrary
SPARQL queries.
• Different architectural set-ups possible; no standard approach

Linked Data Archive Using SPARQL Endpoint(s): Characteristics
Availability problematic problematic
Bandwidth ~ query ~ query
Cost high low
Functionality
Interface Expressiveness highly expressive
LOD Integration no
Memento Support hard
Cross Time/Data custom distributed queries
Verdict:

Linked Data Archive Type Publishing Consuming
Data Dump $$$$ ++++
SPARQL Endpoint(s) $$$$ ++++
Subject URI Access $$$$ ++++

Linked Data Fragments (Ghent U)
• Every Linked Data interface offers specific fragments of a Linked
Data set
• A fragment is described by
• Selector: what questions can I ask?
• Controls: how do I get more fragments?
• Metadata: helpful information for consumption?
• Each interface type comes with tradeoffs
• cf. the analysis thus far
http://linkeddatafragments.org
Verborgh, R. et al. (2014) Querying datsets on the web with high availability. ISWC 2014
http://ruben.verborgh.org/publications/verborgh_iswc_2014/

Triple Pattern Fragments (Ghent U)
• Triple Pattern Fragments is a new interface with a different set of
tradeoffs that are attractive from an archival perspective
http://www.hydra-cg.com/spec/latest/triple-pattern-fragments/

• Allows querying a Linked Data set according to
?Subject ?Predicate ?Object
patterns

Controls: Responses provide navigational help for clients
• Based on emerging Hydra vocabulary for self-describing
Hypermedia-Driven Web APIs
Metadata: dataset info, estimated count (to aid client applications)
http://www.hydra-cg.com/spec/latest/core/

Binary RDF Representation for Publication and Exchange (HDT)
http://www.w3.org/Submission/HDT/

• Header-Dictionary-Triple (HDT) is a compact, binary representation
of RDF datasets.

• Able to represent massive data sets
• Dictionary/Triples structure achieves
• rapid search for ?subject ?predicate ?object pattern
• high compression rates
• Header provides metadata about the dataset

HDT Linked Data Archive with TPF Support
• For each temporal snapshot of a Linked Data set, generate an HDT
serialization that provides access according to
?subject ?predicate ?object
patterns

Linked Data Archive with ?s?p?o Access: Characteristics
Availability high high
Bandwidth ~ query ~ query
Cost low medium
Functionality
Interface Expressiveness better than subject-URI only
LOD Integration yes
Memento Support possible
Cross Time/Data follow your nose
Verdict:

Linked Data Archive Type Publishing Consuming
Data Dump $$$$ ++++
SPARQL Endpoint(s) $$$$ ++++
Subject URI Access $$$$ ++++
HDT & TPF $$$$ ++++

Second Generation DBpedia Archive: Storage

Second Generation DBpedia Archive: Storage
Characteristics
upload software
HDT-CPP
upload time
~ 4 hours per version
storage software
HDT binary files
storage space
70 Gb for 12 versions
DBpedia versions
12 versions: 2.0 through 2015
number of triples
~ 5 billion

Second Generation DBpedia Archive: ?s?p?o Query-URI Access

http://fragments.mementodepot.org/dbpedia_3_8?subject=&predicate=http://dbpedia.org/ontology/b
irthPlace&object=http://dbpedia.org/resource/Ghent

?s?p?o Query-URI Access
TimeGate URI http://fragments.mementodepot.org/timegate/dbpedia?
subject={DBpediaURI}&predicate={DBpediaURI}&object={DBpediaURI}
http://fragments.mementodepot.org/timegate/dbpedia?
subject=&predicate=&object=http://dbpedia.org/resource/Ghent
TimeMap URI not supported
Memento URI http://fragments.mementodepot.org/{DBpediaVersion}?subject={DBpediaURI
}&predicate={DBpediaURI}&object={DBpediaURI}
http://fragments.mementodepot.org/dbpedia_3_0?
subject=&predicate=&object=http://dbpedia.org/resource/Ghent
Further info http://mementoweb.org/depot/native/fragments/
Try it with Memento for Chrome – http://bit.ly/memento-for-chrome

Second Generation DBpedia Archive: Subject-URI Access

Second Generation DBpedia Archive: Subject-URI Access
Subject-URI Access
TimeGate URI http://dbpedia.mementodepot.org/timegate/{DBpediaURI}
http://dbpedia.mementodepot.org/timegate/http://dbpedia.org/data/Ghent
TimeMap URI http://dbpedia.mementodepot.org/timemap/link/{DBpediaURI}
http://dbpedia.mementodepot.org/timemap/link/http://dbpedia.org/data/Ghent
Memento URI http://dbpedia.mementodepot.org/{yyyymmdd}/{DBpediaURI}
http://dbpedia.mementodepot.org/20080103/http://dbpedia.org/data/Ghent
Further info http://mementoweb.org/depot/native/dbpedia/
Try it with Memento for Chrome – http://bit.ly/memento-for-chrome

Second Generation DBpedia Archive: Access
Characteristics
TimeGate software
① node.js LDF server 2.0.0
② LDF js client
access type
① ?s?p?o Query-URI & datetime
② Subject-URI & datetime
external integration
① DBpedia LDF server
② current DBpedia
clients
• all clients: direct access to
Mementos of Subject-URI and
?s?p?o Query-URI
• Memento clients: datetime
negotiation with Subject-URI and
?s?p?o Query-URI
1
2

Building a Linked Data Archive
• Convert the archival data set(s) to HDT using HDT-CPP

HDT Software (C++)
https://github.com/rdfhdt/hdt-cpp
• input data requires cleaning
before processing, especially
regarding URI characters
• DBpedia data not clean
• DBpedia v3.5 was not
successfully processed
• No meaningful error
messages to help locate
problems
• memory intensive
• Kyoto Cabinet was used
to optimize storage
requirement and speed
during processing
• Java version exists but has
memory problems

• Download the Triple Fragment Server code

Linked Data Fragment Server (Node.js)
https://github.com/LinkedDataFragments/Server.js
• provides ?s?p?o access to
local and/or remote Linked
Data sets
• supports HDT, Turtle files, N-
Triple files, JSON-LD files,
SPARQL endpoints, in-
memory store, and
BlazeGraph Linked Data sets
• version 2.0.0 (released March
31 2016) has built-in Memento
support

• Create the JSON config file for Memento

Linked Data Fragment Server, Memento Configuration
https://github.com/LinkedDataFragments/Server.js/wiki/Configuring-Memento
• declare archival data set(s)
• add datetime ranges for the
archival data set(s)
• add a TimeGate
• list the archival data set(s) for
which the TimeGate should
support datetime negotiation

• Create the JSON config file for Memento
• Run the server

DBpedia Archive using Memento, Triple Pattern Fragments, and HDT

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