This document discusses the evolution of DBpedia from 2007 to 2014 and challenges in aligning it as Wikipedia changes. It introduces DBpedia+, a new framework using unit testing and feedback loops to adapt the data extraction as Wikipedia and its templates evolve. RDFUnit is presented as a way to test RDF data and link data tests to software tests. The goal is to provide additional feedback through reporting, statistics, cross-checking between Wikipedias, and machine learning to improve the extraction process as Wikipedia changes over time.

1. DBpedia (in) ALIGNED
From DBpedia to DBpedia+
Dimitris Kontokostas
AKSW Group, Leipzig University
DBpedia Association

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DBpedia @ 2007

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DBpedia @ 2008

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DBpedia @ 2009

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DBpedia @ 2010

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DBpedia @ 2011

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DBpedia @ 2014

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RDF Stats (2014 release)
3B facts (only 580M facts in English)
● DBpedia En: 4.58M Things / 4.22M typed
● 125 Localized versions: 38.3M Things
● 50M links to other datasets
Many more stats @:
dbpedia.org/Datasets2014/DatasetStatistics

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Dev Stats
DBpedia Information Extraction Framework
● Java/Scala based framework
○ Old PHP-based framework
● 5.1K Commits
● 52K lines of code (100K/1M AT)
● 71 total contributors
Many more stats @:
www.openhub.net/p/dbpedia

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Aligning Problem
Lot’s of code & a lot more data
● Wikipedia evolves over time
○ Infobox Templates change, merge, deleted
○ New formatting templates
○ Structural differences per language edition
● Code should adapt to all the changes
○ hard at this (data) scale

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Unit-testing to the rescue?
● Software & Data testing
● Straightforward for software (since 70’s)
● Preliminary for (RDF) data
○ RDFUnit, SPIN, OWL, PelletICV, ShEx,...
■ W3C Data Shapes WG
Data testing++
● Generation: manual, (Semi)automatic, ...
● Linking: data & software tests

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RDFUnit
http://rdfunit.aksw.org

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UT feedback loop
Data verification and feedback at different
data extraction stages
● Three main points of failure in DBpedia:
○ Code
○ Infobox mappings
○ Wikipedia (!!!)

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DBpedia+
Workflow

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Additional feedback
We are looking into:
● Reporting
● Statistics
● Inter-Wikipedia cross-checking
● ML techniques

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Thank you & Questions?
ALIGNED
Aligned, Quality-centric Software and Data
Engineering