A brief presentation on linked data at Elsevier, presented to the MIT CSAIL Industry Affiliates on 2012-01-28.

1. MIT COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE LABORATORY 1/16/2012

2. Who We Are
Global Global Global
community audience market
7,000 editors 15 million doctors,
= 70,000 editorial
board members
200,000 referees
+ nurses and health
professionals
10 million+
+ North
America
researchers in
500,000+ authors 4,500 institutes Europe
Asia-
5 million students Pacific
Science & Technology Health Sciences
Science Journals (online and print) Global Medical Research
Databases Global Clinical Reference
Books (online and print) Clinical Decision Support
Nursing & Health Professions
U.S. Pharma
EMEA & Latin America
APAC
MIT COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE LABORATORY 1/16/2012

3. Linked Data in Scientific Publishing
Linked data from
partners and the Web Better discovery
through semantic
search & navigation
Text
Entities, Better understanding
through analysis and
Scholarly concepts and visualization
content relationships
Tables
Images New knowledge
through aggregation
and synthesis
Scholarly
knowledge
organization
systems
MIT COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE LABORATORY 1/16/2012

4. From Research Publications to
Research Linked Data
Provenance
Entity record metadata
Relational
Metadata
Document
Asset
metadata
Relational Relational
Metadata metadata
Media object
Asset
Asset metadata
Metadata
MIT COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE LABORATORY 1/16/2012

5. Adopting Linked Data at Elsevier
• Embrace linked data principles while leveraging our existing
content production workflow and infrastructure
– Find the right balance between production/QA and online delivery
• Leverage partners for content enhancement and knowledge
organization
– Reuse Web-standard vocabularies, taxonomies, ontologies and entity
resources where possible
• Build out linked data design patterns for application
development
• Deliver benefits across the complementary use cases of
researcher and practitioner
MIT COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE LABORATORY 1/16/2012

6. Work to Date
• Standards
– RDF/XML for production/QA
• Infrastructure
– Linked Data Repository for
online delivery services
• Applications
– Clinical Key (in beta)
– EMMeT medical taxonomy
– Lancet, SciVerse linked data
mashups
MIT COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE LABORATORY 1/16/2012

7. Long Range Objectives
• Build out linked data services for smart content
– Expose linked data for our primary and secondary content
– Link out to emerging, authoritative ontologies and repositories
– Generate linked data for partners’ content
• Deliver linked data to support user-driven innovation
– Foster a developer ecosystem using our services as a platform
– Collaborate in building needed authoritative resources (e.g. ORCID)
• Establish linked data business models
– The Four Principles make what works for the Web of Content work for
the Web of Data without modification
MIT COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE LABORATORY 1/16/2012