Wikipedia is an open-source encyclopedia, built collaboratively by a large community of web editors. The success of Wikipedia as one of the most important sources of information available today still challenges existing models of content creation. Despite the fact that the term ‘curation’ is not commonly addressed by Wikipedia’s contributors, the task of digital curation is the central activity of Wikipedia editors, who have the responsibility for information quality standards.
Wikipedia, is already widely used as a collaborative environment inside organizations5.
The investigation of the collaboration dynamics behind Wikipedia highlights important features and good practices which can be applied to different organizations. Our analysis focuses on the curation perspective and covers two important dimensions: social organization and artifacts, tools & processes for cooperative work coordination. These are key enablers that support the creation of high quality information products in Wikipedia’s decentralized environment.
1. Digital Enterprise Research Institute www.deri.ie
Wikipedia (DBpedia):
Crowdsourced Data Curation
Edward Curry, Andre Freitas, Seán O'Riain
ed.curry@deri.org
http://www.deri.org/
http://www.EdwardCurry.org/
Copyright 2010 Digital Enterprise Research Institute. All rights reserved.
2. Speaker Profile
Digital Enterprise Research Institute www.deri.ie
Research Scientist at the Digital Enterprise Research
Institute (DERI)
Leading international web science research organization
Researching how web of data is changing way business
work and interact with information
Projects include studies of enterprise linked data, community-
based data curation, semantic data analytics, and semantic
search
Investigate utilization within the pharmaceutical, oil &
gas, financial, advertising, media, manufacturing, health
care, ICT, and automotive industries
Invited speaker at the 2010 MIT Sloan CIO Symposium
to an audience of more than 600 CIOs
3. Overview
Digital Enterprise Research Institute www.deri.ie
Curation Background
The Business Need for Curated Data
What is Data Curation?
Data Quality and Curation
How to Curate Data
Wikipedia (DBpedia) Case Study
Best Practices from Case Study Learning
4. The Business Need
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Knowledge workers need:
Access to the right information
Confidence in that information
Working incomplete
inaccurate, or wrong
information can have
disastrous consequences
5. The Problems with Data
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Flawed Data
Effects 25% of critical data in world‟s top companies
(Gartner)
Data Quality
Recent banking crisis (Economist Dec‟09)
Inaccurate figures made it difficult to manage operations
(investments exposure and risk)
– “asset are defined differently in different programs”
– “numbers did not always add up”
– “departments do not trust each other‟s figures”
– “figures … not worth the pixels they were made of”
6. What is Data Curation?
Digital Enterprise Research Institute www.deri.ie
Digital Curation
Selection, preservation, maintenance, collection, and
archiving of digital assets
Data Curation
Active management of data over its life-cycle
Data Curators
Ensure data is
trustworthy, discoverable, accessible, reusable, and fit for
use
– Museum cataloguers of the Internet age
7. What is Data Curation?
Digital Enterprise Research Institute www.deri.ie
Data Governance
Convergence of data quality, data
management, business process management, and
risk management
Data Curation is a complimentary activity
Part of overall data governance strategy for
organization
Data Curator = Data Steward ??
Overlapping terms between communities
8. Data Quality and Curation
Digital Enterprise Research Institute www.deri.ie
What is Data Quality?
Desirable characteristics for information resource
Described as a series of quality dimensions
– Discoverability, Accessibility, Timeliness, Completeness, Inte
rpretation, Accuracy, Consistency, Provenance & Reputation
Data curation can be used to improve these
quality dimensions
9. Data Quality and Curation
Digital Enterprise Research Institute www.deri.ie
Discoverability & Accessibility
Curate to streamline search by storing and classifying
in appropriate and consistent manner
Accuracy
Curate to ensure data correctly represents the “real-
world” values it models
Consistency
Curate to ensure data created and maintained using
standardized definitions, calculations, terms, and
identifiers
10. Data Quality and Curation
Digital Enterprise Research Institute www.deri.ie
Provenance & Reputation
Curate to track source of data and determine reputation
Curate to include the objectivity of the source/producer
– Is the information unbiased, unprejudiced, and impartial?
– Or does it come from a reputable but partisan source?
Other dimensions discussed in chapter
11. How to Curate Data
Digital Enterprise Research Institute www.deri.ie
Data Curation is a large field with sophisticated
techniques and processes
Section provides high-level overview on:
Should you curate data?
Types of Curation
Setting up a curation process
Additional detail and references available in book
chapter
12. Should You Curate Data?
Digital Enterprise Research Institute www.deri.ie
Curation can have multiple motivations
Improving accessibility, quality, consistency,…
Will the data benefit from curation?
Identify business case
Determine if potential return support investment
Not all enterprise data should be curated
Suits knowledge-centric data rather than transactional
operations data
13. Types of Data Curation
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Multiple approaches to curate data, no single
correct way
Who?
– Individual Curators
– Curation Departments
– Community-based Curation
How?
– Manual Curation
– (Semi-)Automated
– Sheer Curation
14. Types of Data Curation – Who?
Digital Enterprise Research Institute www.deri.ie
Individual Data Curators
Suitable for infrequently changing small quantity of
data
– (<1,000 records)
– Minimal curation effort (minutes per record)
15. Types of Data Curation – Who?
Digital Enterprise Research Institute www.deri.ie
Curation Departments
Curation experts working with subject matter experts
to curate data within formal process
– Can deal with large curation effort (000‟s of records)
Limitations
Scalability: Can struggle with large quantities of
dynamic data (>million records)
Availability: Post-hoc nature creates delay in curated
data availability
16. Types of Data Curation - Who?
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Community-Based Data Curation
Decentralized approach to data curation
Crowd-sourcing the curation process
– Leverages community of users to curate data
Wisdom of the community (crowd)
Can scale to millions of records
17. Types of Data Curation – How?
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Manual Curation
Curators directly manipulate data
Can tie users up with low-value add activities
(Sem-)Automated Curation
Algorithms can (semi-)automate curation activities
such as data cleansing, record duplication and
classification
Can be supervised or approved by human curators
18. Types of Data Curation – How?
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Sheer curation, or Curation at Source
Curation activities integrated in normal workflow of
those creating and managing data
Can be as simple as vetting or “rating” the results of a
curation algorithm
Results can be available immediately
Blended Approaches: Best of Both
Sheer curation + post hoc curation department
Allows immediate access to curated data
Ensures quality control with expert curation
19. Setting up a Curation Process
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5 Steps to setup a curation process:
1 - Identify what data you need to curate
2 - Identify who will curate the data
3 - Define the curation workflow
4 - Identity appropriate data-in & data-out formats
5 - Identify the artifacts, tools, and processes needed to
support the curation process
21. Wikipedia
Digital Enterprise Research Institute www.deri.ie
Open-source encyclopedia
Collaboratively built by large community
Challenges existing models of content creation
More than 19,000,000 articles
270+ languages, 3,200,000+ articles in English
More than 157,000 active contributors
Studies show accuracy and stylistic formality are
equivalent to resources developed in expert-
based closed communities
i.e. Columbia and Britannica encyclopedias
22. Wikipedia
Digital Enterprise Research Institute www.deri.ie
MediaWiki
Wiki platform behind Wikipedia
– Widespread and popular technology
Wikis can also support data curation
– Lowers entry barriers for collaborative data curation
Widely used inside organizations
Intellipedia covering 16 U.S. Intelligence agencies
Wiki Proteins, curated Protein data for knowledge
discovery and annotation
23. Wikipedia
Digital Enterprise Research Institute www.deri.ie
Decentralized environment supports creation of
high quality information with:
Social organization
Artifacts, tools & processes for cooperative work
coordination
Wikipedia collaboration dynamics highlight good
practices
24. Wikipedia – Social Organization
Digital Enterprise Research Institute www.deri.ie
Any user can edit its contents
Without prior registration
Does not lead to a chaotic scenario
In practice highly scalable approach for high quality
content creation on the Web
Relies on simple but highly effective way to
coordinate its curation process
Curation is activity of Wikipedia admins
Responsibility for information quality standards
25. Wikipedia – Social Organization
Digital Enterprise Research Institute www.deri.ie
Four main types of accounts:
Anonymous users
– Identified by their associated IP address
Registered users
– Users with an account in the Wikipedia website
Administrators/Editors
– Registered users with additional permissions in the system
– Access to curation tools
Bots
– Programs that perform repetitive tasks
26. Wikipedia – Social Organization
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27. Wikipedia – Social Organization
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Incentives
Improvement of one‟s reputation
Sense of efficacy
– Contributing effectively to a meaningful project
Over time focus of editors typically change
– From curators of a few articles in specific topics
– To more global curation perspective
– Enforcing quality assessment of Wikipedia as a whole
28. Wikipedia – Artifacts, Tools &
Processes
Digital Enterprise Research Institute www.deri.ie
Wiki Article Editor (Tool)
WYSIWYG or markup text editor
Talk Pages (Tool)
Public arena for discussions around Wikipedia resources
Watchlists (Tool)
Helps curators to actively monitor the integrity and quality of
resources they contribute
Permission Mechanisms (Tool)
Users with administrator status can perform critical actions such
as remove pages and grant administrative permissions to new
users
29. Wikipedia – Artifacts, Tools &
Processes
Digital Enterprise Research Institute www.deri.ie
Automated Edition (Tool)
Bots are automated or semi-automated tools that perform repetitive
tasks over content
Page History and Restore (Tool)
Historical trail of changes to a Wikipedia Resource
Guidelines, Policies & Templates (Artifact)
Defines curation guidelines for editors to assess article quality
Dispute Resolution (Process)
Dispute mechanism between editors over the article contents
Article
Edition, Deletion, Merging, Redirection, Transwiking, Archiv
al (Process)
Describe the curation actions over Wikipedia resources
30. Wikipedia - DBPedia
Digital Enterprise Research Institute www.deri.ie
DBPedia Knowledge base
Inherits massive volume of curated Wikipedia data
Built using information info box properties
Indirectly uses wiki as data curation platform
DBPedia provides direct access to data
3.4 million entities and 1 billion RDF triples
Comprehensive data infrastructure
– Concept URIs, definitions, and basic types
33. Overview
Digital Enterprise Research Institute www.deri.ie
Curation Background
The Business Need for Curated Data
What is Data Curation?
Data Quality and Curation
How to Curate Data
Wikipedia (DBpedia) Case Study
Best Practices from Case Study Learning
34. Best Practices from Case Study
Learning
Digital Enterprise Research Institute www.deri.ie
Social Best Practices
Participation
Engagement
Incentives
Community Governance Models
Technical Best Practices
Data Representation
Human- and AutomatedCuration
Track Provenance
35. Social Best Practices
Digital Enterprise Research Institute www.deri.ie
Participation
Stakeholders involvement for data producers and
consumers must occur early in project
– Provides insight into basic questions of what they want
to do, for whom, and what it will provide
White papers are effective means to present these
ideas, and solicit opinion from community
– Can be used to establish informal „social contract‟ for
community
36. Social Best Practices
Digital Enterprise Research Institute www.deri.ie
Engagement
Outreach activities essential for promotion and
feedback
Typical consumers-to-contributors ratios of less than
5%
Social communication and networking forums are
useful
– Majority of community may not communicate using
these media
– Communication by email still remains important
37. Social Best Practices
Digital Enterprise Research Institute www.deri.ie
Incentives
Sheer curation needs line of sight from data curating
activity, to tangible exploitation benefits
Lack of awareness of value proposition will slow
emergence of collaborative contributions
Recognizing contributing curators through a formal
feedback mechanism
– Reinforces contribution culture
– Directly increases output quality
38. Social Best Practices
Digital Enterprise Research Institute www.deri.ie
Community Governance Models
Effective governance structure is vital to ensure
success of community
Internal communities and consortium perform well
when they leverage traditional corporate and
democratic governance models
Open communities need to engage the community
within the governance process
– Follow less orthodox approaches using meritocratic
and autocratic principles
39. Technical Best Practices
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Data Representation
Must be robust and standardized to encourage
community usage and tools development
Support for legacy data formats and ability to
translate data forward to support new technology and
standards
Human & Automated Curation
Balancing will improve data quality
Automated curation should always defer to, and never
override, human curation edits
– Automate validating data deposition and entry
– Target community at focused curation tasks
40. Technical Best Practices
Digital Enterprise Research Institute www.deri.ie
Track Provenance
All curation activities should be recorded and
maintained as part data provenance effort
– Especially where human curators are involved
Users can have different perspectives of provenance
– A scientist may need to evaluate the fine grained
experiment description behind the data
– For a business analyst the ‟brand‟ of data provider can
be sufficient for determining quality
41. Conclusions
Digital Enterprise Research Institute www.deri.ie
Data curation can ensure the quality of data and
its fitness for use
Pre-competitive data can be shared without
conferring a commercial advantage
Pre-competitive data communities
Common curation tasks carried out once in public
domain
Reduces cost, increase quantity and quality
42. Acknowledgements
Digital Enterprise Research Institute www.deri.ie
Collaborators Andre Freitas & Seán O'Riain
Insight from Thought Leaders
Evan Sandhaus (Semantic Technologist), Rob Larson (Vice President Product
Development and Management), and Gregg Fenton (Director Emerging Platforms)
from the New York Times
Krista Thomas (Vice President, Marketing & Communications), Tom Tague
(OpenCalais initiative Lead) from Thomson Reuters
Antony Williams (VP of Strategic Development ) from ChemSpider
Helen Berman (Director), John Westbrook (Product Development) from the Protein
Data Bank
Nick Lynch (Architect with AstraZeneca) from the Pistoia Alliance.
The work presented has been funded by Science
Foundation Ireland under Grant No. SFI/08/CE/I1380 (Lion-
2).
43. Further Information
Digital Enterprise Research Institute www.deri.ie
The Role of Community-Driven
Data Curation for Enterprises
Edward Curry, Andre Freitas, & Seán O'Riain
In David Wood (ed.),
Linking Enterprise Data Springer, 2010.
Available Free at:
http://3roundstones.com/led_book/led-curry-et-al.html