The document discusses the importance of linked data in scientific publishing, emphasizing its potential to enhance discoverability and engagement with content. It outlines the challenges publishers face in integrating linked data into their workflows while highlighting Elsevier's approach to embracing these principles for improved performance and collaboration. The presentation suggests that smart content structured through linked data can greatly benefit the future of publishing by creating better insights and utility for researchers and institutions.

1. Linked Data and
the Future of Scientific Publishing
Bradley P. Allen, Elsevier Labs
Presentation to NFAIS Webinar – “Linked Data: What It Is, What It
Does and The Future of Information Discovery”
2012-10-25

2. Scientific knowledge in a post-print world
“Our new knowledge does not consist of a
careful set of works that have passed through
a series of gates. … Our new knowledge is not
even a set of works. It is an infrastructure of
connection.”
David Weinberger. 2011. Too Big to Know: Rethinking Knowledge Now That the Facts Aren't
the Facts, Experts Are Everywhere, and the Smartest Person in the Room Is the Room, Basic
Books, New York, NY
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2

3. “Infrastructure of connection” = linked data
Type of data Content Inputs Linked Data Outputs Benefits
• XML • Asset metadata • Better discoverability
• Long-form free text • Citations • Better visualization and
understandability
• Short-form free text • Classifications
What the • Tables • Clusters
• Better integration for use
in information solutions
literature is • Images • Entities
about • Video • Relations
• Audio • Language models
• Probabilistic graphical
models
• Article views • Article-level metrics • Provides the researcher
• Search queries • Sentiment analysis insight about her career
How the • User behavior • Ranking and impact
• Provides institutions data
about their performance
literature is • Social media streams metrics
and impact
• User interest profiles
being used • Provides publishers data
for optimizing our
business
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3

4. Linked data as standards and best practices
“Linked data is just a term 1. Use URIs as names for
for how to publish data on things
the web while working 2. Use HTTP URIs so that
with the web. And the web people can look up those
is the best architecture we names
know for publishing
information in a hugely 3. When someone looks up
diverse and distributed a URI, provide useful
environment, in a gradual information, using the
and sustainable way.” standards
4. Include links to
Jeni Tennison. 2010. Why Linked Data for
data.gov.uk? other URIs, so that they
http://www.jenitennison.com/blog/node/
140 can discover more things
Tim Berners-Lee. 2006. Linked Data
http://www.w3.org/DesignIssues/LinkedData.html

5. Scientific publication as linked data
Linked data
Provenance
metadata
Entity record
Relational
Metadata
Document Asset
metadata
Acquire Relational Relational Deliver
Metadata metadata
Media object
Asset
Asset metadata
Metadata
Transform,
Enhance, Index, Analyze,
Compose
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6. Linked data is increasingly important in science
6

7. The challenge for publishers
• Create greater online engagement with our content
and platform
• Semantically enrich our content and enhance value of
discovery services compared to the same and similar
content at other platforms
• Drive additional usage (in journals and books, in
downloads and interactivity)
• Improve our ability to be a partner in research, and as
a publisher that adds value
• Improve our connection with the scientific community
through productive collaborations that improve
search and discovery for all researchers
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8. Elsevier’s approach to linked data
• Expose existing asset and subject metadata as linked
data in Web pages to aid discovery
• Embrace linked data principles while leveraging our
existing content production workflow and
infrastructure
• Leverage partners for content enhancement and
knowledge organization
• Reuse Web-standard vocabularies, taxonomies,
ontologies and entity resources where possible
• Collaborate in building needed authoritative resources
for identity resolution and metrics
• Deliver benefits across the complementary use cases
of researcher and practitioner
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9. Creating smart content by extracting & linking
Asset
Metadata
Usage Entities
Citations Relations
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10. Methods for extracting and linking content & data
• Very mature, but • Variable degrees of maturity, but huge • Language-driven,
hard to scale strides through machine learning research so challenging to
• Crowdsourcing is a and practical application on the consumer generalize and
possible solution, Internet scale
but quality control • Data-driven, so the more data the better • Crucial to realize
is a challenge • Models can be used to build applications, promise of ease of
can be a new type of publication integration
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11. Packaging linked data for content production
tag:satelliteWrapper +
XML Schema
rdf:RDF+namespaces
sat:Satellite
Concept schemes SKOS
Statement 1 Generator
Tags
Diabetes
Statement 2
Hypertension LDR
... RDF
Generator
Para1-Statement-1
Region Tags
Diabetes Example RDF Statements
... Tags from a taxonomy for a given document
Document sections relevant to a given concept
Para2-Statement-2
Document sections providing answers to a given question
Hypertension Learning objects compliant with a given state educational standard
Genes mentioned in a given document
Documents supporting or disputing conclusions of a given document
Concepts that are in the areas of expertise for a given author
...
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12. Infrastructure for storing and publishing linked data
Loader (REST)
Data Spaces
tes
Satelli
ation
Annot
es
Satellit
Asset
es
Satellit
Vocab
Data
Party
3rd
Pipeline
Coordination Pipeline Services (Hadoop EMR)
N-
RDF Ontology
JSON Reaso Interlin ValidatiSvcs
Quads
Transform
Extract ning king on
Discovery Services
Amazo MongoDB SIREN/ Virtuoso
n S3 SOLR Triplestor
e
Discovery
Atom Admin& Ontology SPARQL
A&E Service API Analytics
Feed Monotoring Service Endpoint
(REST)
Load Balance & Failover (Akamai GTM & Amazon ELB)
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13. Integrating content & data services with linked data
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14. Delivering linked data through multiple online services
Organization Main driver Example Benefits Linked data
S&T Journals Making the article more engaging and Article of the Understanding, Entities, Citations,
informative through visualization and linking Future Discovery Relations
Books Making the book more engaging and Brain Navigator Understanding, Entities
informative through visualization and linking Discovery
A&G Making the discovery of relevant content Lipids SciVerse Discovery, Entities, Asset
Research easier and more engaging App Integration Metadata
A&G Making data about the production and use SciVal Spotlight Understanding Entities, Citations,
Institutional of scientific content easier to understand Usage
Corporate Alternative Making the exploration of design Elsevier Biofuels Discovery Entities, Citations
Fuels alternatives easier
Bibliographical Automating the indexing of content for Embase Discovery Asset Metadata,
Databases traditional discovery channels Entities
Engineering & Making the discovery of technology trends Illumin8 Discovery Entities, Citations,
Technology and sources easier Relations
Pharma Biotech Rich integration of content and data in Target Insights Discovery, Entities, Citations
support of research and design workflows Understanding
HS CDS Delivering actionable information in the Order Sets Integration Entities, Relations
context of medical decision making
GCR Making the discovery of relevant medical Clinical Key Discovery Entities, Asset
content easier and contextual Metadata
NHP Making the delivery and organization of General Discovery, Entities, Asset
medical content easier to integrate with Education Integration Metadata,
educational workflows Platform Relations
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15. Challenges in implementing linked data
• Access to content and data • Production
– Usage data not integrated or – Manually intensive knowledge
engineering
leveraged
– Balancing production validation and
– Hard to stage content for modeling rapid iterative development
and analytics – Relation extraction needed but
capabilities are minimal at best
• Integration – Tools for syntactic rather than
– Adoption of standards across silos semantic validation
and legacy systems • Sharing
– Globalization/localization of – Culture and legacy
knowledge organization systems – Business model disincentives
– Named entity registries for identity – Identifier, URI and namespace
resolution for accreditation, governance
provenance and trust • Quality control
• Human resources – Lack of clean external data
– Gaps in linked data resources
– Scarcity of data scientists, language – Bugs in knowledge organization
engineers systems

16. Trends within Elsevier today
• Increasing acquisition of data and text analytics
capabilities
• Shifting dependence from partners to in-house
resources for content enhancement and
knowledge organization
• Innovation in new knowledge organization
systems (some through integration of existing
ones)
– Two main design emphases: taxonomy for discovery,
ontology for understanding and integration
• Emergence of shared smart content
infrastructure based on linked data principles
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17. Smart content is a bridge to the future of publishing
• Smart content allows publishers to create new
products and services through structuring
content for better discovery, insight and utility
– The value is in the structure, not the content
– Creating that structure is hard work
– The kind of hard work that publishers have
traditionally focused on
• Consumer Internet businesses are using text and
data mining to add structure to content today…
quickly and on the cheap
• Publishers, societies and libraries both large and
small can use the same techniques to follow suit
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18. Thank you
Bradley P. Allen
b.allen@elsevier.com
bradleypallen on twitter, github