Linked data may hold the potential to solve some classic serials dilemmas like latest vs. successive entry, or single vs. multiple records for print and online. How do these hopes mesh with the evolving current state of linked data projects in the commercial and library sector as well as with LC’s Bibframe initiative? The speakers will provide three different perspectives. An “early experimenter” and member of the Bibframe group modeling serials will discuss her experiences and thoughts on future directions. A publisher from a company that has reorganized some of its infrastructure and processes to facilitate linked data will share the goals and provide examples of the benefits of that project. Finally, the head of the U.S. ISSN Center will take an ISSN perspective as well as compare international work modeling serials according to FRBR-OO (object-oriented) with the Bibframe serials modeling effort. Audience input will be solicited in order to provide an exchange of ideas and viewpoints. (moderated by Laurie Kaplan) Michael Lauruhn Disruptive Technology Director, Elsevier Labs See accompanying presentation by Nancy Fallgren

1. Linked Data at Elsevier
Michael P Lauruhn
The Missing Link: The Evolving Current State of Linked Data for
Serials
NASIG 28th Annual Conference
June 7, 2013
@MikeLauruhn
@ElsevierLabs

2. 2
• Create greater online engagement with our
content and platform
• Create additional usage in journals and books,
through interactive use and downloads
• Semantically enrich content and increase
value of discovery services compared to the
similar content at other platforms
• As a publisher, add value and improve our
status as a partner in research
The challenge for publishers
Copyright © 2013 Elsevier, Inc. | All Rights Reserved

3. 3
Our Approach
• Expose asset and subject metadata as linked
data in Web pages to help discovery
• Use linked data principles while using our
current content production workflow
• Use of standard vocabularies, taxonomies,
ontologies and entity lists whenever possible
• Leverage partners for content enhancement and
knowledge organization
Copyright © 2013 Elsevier, Inc. | All Rights Reserved

4. Smart Content: Semantic Enhancements
for Scientific Publishing
Concepts:
Metadata,
Entities,
Relationships
Applied Smart Content
• Faceted search & browse
• Ontology-driven navigation
• Task-specific results
• Personalized/localized results
• Question answering
• Topic pages
• Social network maps
• Geolocation maps
• Data mashups
• Text mining reports
Multimedia
Text
Data
Elsevier
content
Related
Elsevier
content
and data
Linked data from
partners and the
Web
• Tag clouds
• Heatmaps
• Streamgraphs
• Scatterplots
• Time series
• Animations
Better discovery
Better understanding
Actionable, persuasive knowledge
Copyright © 2013 Elsevier, Inc. | All Rights Reserved

5. 5
What metadata can we collect & link
Asset Metadata
Bibliographic
Metadata
Entities
Relations
Citations
Copyright © 2013 Elsevier, Inc. | All Rights Reserved

6. 6
Entities Example: EMMeT
Core Semantic Types:
Diseases
Anatomy
Clinical Findings (includes symptoms)
Procedures
Drugs
Sources:
EMMeT
SNOMED CT
FMA (Foundational Model of Anatomy)
GO (Gene Ontology)
MeSH
NCIt (National Cancer Institute thesaurus)
Elsevier Merged Medical Taxonomy
Entities
Copyright © 2013 Elsevier, Inc. | All Rights Reserved

7. 7
<skosxl:literalForm xml:lang="en-US">Diabetes Mellitus</skosxl:literalForm>
<ebs:usageFlag rdf:resource="http://data.elsevier.com/EMMeT/Flags/MedicalName"/>
...
<skosxl:literalForm xml:lang="en-US">Diabetes</skosxl:literalForm>
<ebs:usageFlag rdf:resource="http://data.elsevier.com/EMMeT/Flags/ConsumerFriendlyName"/>
...
<skos:notation rdf:datatype="http://data.elsevier.com/vocabulary/EMMeT">177824</skos:notation>
<skos:notation rdf:datatype="http://dbpedia.org/resource/UMLS">C0011849</skos:notation>
...
<rdf:type rdf:resource="http://data.elsevier.com/EMMeT/SemTypes/DiseaseOrSyndrome" />
<skos:broader rdf:ID="Relation-34359" rdf:resource="http://data.elsevier.com/vocabulary/EMMeT/Concept/48543"/>
...
<skos:narrower rdf:ID="Relation-9812" rdf:resource="http://data.elsevier.com/vocabulary/EMMeT/34565"/>
...
<emsem:hasSymptom rdf:ID="Relation-99999"
rdf:resource="http://data.elsevier.com/vocabulary/EMMeT/Concept/53425"/>
…
Abnormal Sense of Taste
Abnormal metabolic state in diabetes mellitus
Disorders of endocrine system
EMMeT – Elsevier Merged Medical Taxonomy
Term example: Diabetes Mellitus
Relations
Entities
Copyright © 2013 Elsevier, Inc. | All Rights Reserved

8. 8
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 …
Linked Data Repository (LDR): Warehouse for
Smart Content Enhancements
Delirium treatment: An unmet challenge
Title
Drug
Clinical finding • Enhances extracted knowledge of Elsevier
assets by interlinking data with related sources
of medical and scientific content and data.
• Optimized for high-volume read-write for use
by end-user products.
• Provide service layer APIs for ease of
integration.
Disease
ATC: N06DA03
Drug: Rivastigmine
med:diseases Delirium
med:drugs Rivastigmine
Elsevier
Trial: NCT00623103
Intervention: Rivastigmine
Condition: Delirium
LinkedCT
Trial: NCT00623103
Serious Adverse events:
Atrial fibrillation
owl:same as
owl: same as
foaf:page
Copyright © 2013 Elsevier, Inc. | All Rights Reserved

9. 9
Some Examples
Copyright © 2013 Elsevier, Inc. | All Rights Reserved

10. 10
Linked Data Prototype for The Lancet
Special featureThe Lancet special issue: ―Stillbirths‖
(Vol 377; Number 9774; April 14, 2011)
Creation LDR-enabled interactive application
using:
• The Lancet content
• Datasets from The Lancet editorial staff
• Datasets from World Bank
• EMMeT subject tagging
• Geo locations & Map
The Lancet and World Bank datasets
loaded into the LDR as triples.
EMMeT tagging results of articles
loaded into LDR.
Copyright © 2013 Elsevier, Inc. | All Rights Reserved

11. 11
Trend Analysis Of Special Health Topics:
Stillbirths
http://ldr.elsevier.com/lancet
Countries color coded
by Lancet data on
stillbirth rate per 1000
births.
Bubbles represent
selected data from
World Bank
(GDP per Capita)
Narrow search by
identifying articles with
most relevant concepts
related to stillbirth.
Copyright © 2013 Elsevier, Inc. | All Rights Reserved

12. 12
http://ldr.elsevier.com/ukgov/map.html
Regions color coded
based on avg. cost of
Tamoxifen treatment
per 1000 people.
Bubbles represent
the cost of treating
side effects of Tamoxifen.
Example; North
Yorkshire
Additional cost for
Toremifene = £357k
If we assume an
adverse effect costs
£300 per patient.
Tamoxifen = £1.48m
Toremifene =
£103k
Savings Potential
>£1m by prescribing
a seemingly more
expensive drug
Healthcare Analytics: Breast Cancer Treatment in the UK
Data from
data.gov.uk,
Elsevier articles,
& Elsevier
Pharmapendium
Copyright © 2013 Elsevier, Inc. | All Rights Reserved

13. 13
Neuroscience Research: Improved Access To Data and
Better Tools
CHALLENGE
Neuroscience is a highly interdisciplinary field requiring analysis on vast
amounts of content from many different sources:
• Foundational information is hard to find
• Need to identify articles with specific methods or related experiments
• Analyze anatomy, connectivity, and gene expression data from different sources
• Sources use different ontologies
`
Unpaired midbrain region situated in the
ventromedial portion of the reticular
formation. The VTA is medial to the
substantia nigra and ventral to the red
nucleus, and extends caudally …..
Anatomy Connectivity Expression
Copyright © 2013 Elsevier, Inc. | All Rights Reserved

14. 14
• Increasing acquisition of data and text analytics
capabilities
• Shifting dependence from partners to in-house
resources for metadata creation and data
modeling
• Innovation in new knowledge organization
systems
– taxonomy for discovery
– ontology for understanding and integration
• Emergence of shared infrastructure based on
linked data principles
Trends within Elsevier today
Copyright © 2013 Elsevier, Inc. | All Rights Reserved

15. Thank you
Michael Lauruhn
m.lauruhn@elsevier.com
@MikeLauruhn
@ElsevierLabs
Copyright © 2013 Elsevier, Inc. | All Rights Reserved