This document discusses Smart Content and linked data applications at Elsevier. It provides an overview of Elsevier's efforts to develop a linked data repository and infrastructure that connects Elsevier content to external vocabularies and data sources. Examples are given of current Smart Content applications, including linking clinical trial data to drugs and adverse events, linking neuroscience articles to related methods and Wikipedia definitions, and highlighting energy terms in articles. Considerations for planning Smart Content projects are also outlined, such as focusing on use cases, ensuring quality and reliability of resources, planning for ongoing maintenance, and thorough testing.

1. Smart Content applications at
Elsevier
NISO/NFAIS Virtual Conference:
Connecting the Library to the Wider World - Successful Applications of Linked Data
Michael Lauruhn
December 3, 2014

2. | 2
Introduction & Agenda
Smart Content & Linked Data at Elsevier
Background
Key Components of Smart Content
Current Examples
Project Planning Considerations

3. | 3
Introduction: Smart Content & Linked Data
Elsevier Content
Componentized text
Data
Multimedia
3rd Party Linked data
Web Open data
Vocabulary

4. | 4
Smart Content infrastructure in practice
Trial: NCT00623103
Serious Adverse events:
Atrial fibrillation
Elsevier
med:drugs Rivastigmine
Delirium treatment: An unmet challenge
Rivastigmine, a cholinesterase inhibitor, has been used to
treat delirium in elderly patients with stroke. 1 A biologically
plausible premise—that impaired cholinergic transmission
might either cause or worsen delirium—led to a
randomised, placebo-controlled, double-blind trial by
Maarten van Eijk and colleagues 2 in The Lancet in which
they added rivastigmine or placebo to usual treatment of
patients in intensive care. The trial was halted at 104
patients by the drug safety and monitoring board (DSMB)
because of increased mortality (12/54 in the rivastigmine
group, 4/50 in the placebo group; p=0·07) and a worse
outcome. The rivastigmine group …
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owl:same as
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5. | 5
Smart Content as Infrastructure
Product Development & Enhancement
• More accurate search results
• Faceted navigation
• Improved content discoverability
Content Analytics
• New insights and abilities to take inventory
about what we publish
• Identification of co-occurring terms
• Link to related external content & data
Personalization
• Individual content recommendations
• Targeted individual marketing
Editorial Productivity
• Flexible product types – new collections,
image banks, etc.
• Increased speed to market

6. Key Components
of Smart Content

7. | 7
Vocabulary Example: EMMeT
EMMeT
UMLS
SNOMED
ICD9
ICD10
MeSH
LOINC
Elsevier
Custom
Resources
Gold
Standard
(Drugs)
Multi-language taxonomy:
>1 million concepts
>3 million synonyms
Classes include:
Anatomy
Diseases
Drugs
Symptoms
Procedures
Sourced from several
standardized vocabularies

8. | 8
• Breast Disorders
• Cancer of the Thorax
• Mammary Neoplasms
• More….
Medical Name
Malignant Neoplasm of the Breast
Consumer Friendly Name
Breast Cancer
Synonyms
Malignant Tumor of Breast
Malignant Breast Neoplasm
Breast Ca
Codes
ICD9 – 174.9
MeSH – D001943
SNOMED-CT – 190121004
Semantic Type/Group
Neoplastic Process/Disease
• Breast Sarcoma
• Familial Breast Cancer
• Malignant lymphoma of the Breast
• Malignant Neoplasm of the breast outer
quadrant
• More…
Symptoms
Diagnostic
Procedures
Treatment
Procedures
Medications
Risk Factors
Prevention
Complications
Breast Lump, Nipple Retraction, …..
Mammography, Breast Biopsy, …..
Chemotherapy, Mastectomy, ….
Tamoxifen, Doxorubicin, …..
Family History, Genetics, Predisposition, ….
Screening, Preemptive Mastectomy, ….
Metastatic Cancer, ….
Semantic Relationships
4
2
1
3

9. | 9
Vocabulary Example: EMMeT
EMMeT
UMLS
SNOMED
ICD9
ICD10
MeSH
LOINC
Elsevier
Custom
Resources
Gold
Standard
(Drugs)
FrEMMeT
SpEMMeT

10. | 10
Linked Data Repository

11. | 11
Linked Data Repository
• Knowledgebase of semantic data
• Large scale integration of related
sources of medical and scientific
content and data
• High performance service layer
APIs for integration into end-user
products and internal platforms
Classic subject metadata
Editorial &
Author
Keywords
Full-text
Indexing
Linked Data Environment
Componentized text
Robust Data models
Entity extraction
Semantic Annotations

12. Current Examples

13. | 13
ClinicalKey search

14. | 14
SciVal Funders Vocabulary
• Support the FundRef initiative facilitated by CrossRef organization to
provide a standard way of reporting funding sources for published
scholarly research.
• SciVal Funding is an online solution that provides targeted
recommendations on grants, making it easier for researchers to
discover funding opportunities related to their area of research.

15. | 15
Similar Methods for Neuroscience
System to extract and index the Methods sections of articles from
100 Elsevier neuroscience journals
Built a comparison and recommendation system so readers can
find and evaluate articles with “Similar Methods” to the ones
presented in the current article

16. | 16
Similar Methods for Neuroscience
Search process targets factors:
what brain regions are being studied
what organism is being used
what methodologies are being employed
what disease model is the focus of the study

17. | 17
Leveraging Wikipedia for Neuroscience
• Pilot project that identifies concepts from a
Neuroscience topics vocabulary
• Provides Wikipedia definitions to add context
around the article’s significant concepts

18. Additional context for Energy terms
18
• A ‘dictionary app’ using the portions of the Encyclopedia of
Energy (1818 terms)
• Available for articles from Applied Energy and Energy
Conversion and Management; additional pilots planned.
Example: http://www.sciencedirect.com/science/article/pii/S0306261913001888
Terms from dictionary
are highlighted in article,
when the reader clicks
on the term the definition
from the dictionary will
be shown in the feature
(right hand pane)

19. Project Planning
Considerations

20. | 20
Get to a Use Case early
• Get stakeholders invested
• Think about what users currently do… and what they can do better
• Focus on Use Cases to stay centered and identify priorities with
decision making.
Particularly helpful when introducing a
new infrastructure to an organization

21. | 21
Quality & Reliable of Resources
• Integration with third party content, data models and vocabularies
requires a vetting process:
 Are they accurate?
 Are they trustworthy?
 Are they current?
 Are they sustainable?
Warning: Some of the more attractive
resources on the web are one off
projects are no longer maintained

22. | 22
Ongoing Maintenance
• As knowledge models and vocabularies grow, resources are needed
to keep them current
• Governance policy should account for sources for new concepts,
terminology and relations:
 New content types
 Search logs
 New trends & discoveries
These require resources (people’s
time) that need to be factored into the
total cost of ownership

23. | 23
Quality & Testing
• Applying semantic web technologies for applications is not an
exclusively IT solution:
 Sponsors, stakeholders and subject experts need to contribute and
shape the vocabularies and the application functionality
 The fine tuning for some of these applications can be surprisingly
manual
 It’s important to not get distracted by the outliers and corner cases
Installing and implementing these
technologies OOTB is getting
easier…Quality is where it gets hard

24. | 24
Quality & Testing
• Test sets are essential
 Real content
 Real use cases
 Scores that show accuracy and measure improvement
Our SME’s: Our Application:

25. | 25
Other lessons & observations
• Don’t forget to look at opportunities for internal applications
 Consider internal workflows
 Look for efficiency enhancements
 Look for discovery opportunities
• Start small
 Get some early proof of concepts that you can share with stakeholders
before tackling bigger challenges

26. Thank You
Michael Lauruhn
m.lauruhn@elsevier.com
@MikeLauruhn
@ElsevierLabs