1. SCOTT BERNIER | Senior Vice President
OPTIMIZING DISCOVERY
ER&L, April 2016
SCOTT BERNIER | Senior Vice President

2. Discovery Optimization is a collaborative effort across discovery providers, content providers & libraries. The contributions from each party have a direct impact on the end value…and all goals are aligned.

3. Collection Usage: the new reality of library platforms & user behavior
Direct Usage of Specialized Resources Remains
Discovery Services are Growing
Adoption, Usage & Emphasis (focal point of library web sites)
As libraries we are focused on the user experience
As a community, we are embracing the new reality
Moving beyond simple participation…into optimization

4. 3 Major Areas for Optimization
CONTENT
1. Exposing ENTIRE COLLECTION
(Data/Records & Resources)
RESULT LIST
2. Surfacing THE BEST RESULTS (Relevance Ranking)
Facilitating ACCESS TO CONTENT (Linking)
LINKING

5. Optimizing Content Exposure
Partnerships across all content providers
All publications from every publisher
Streamline data delivery
Beyond basic metadata
Subject Indexing, Book chapter level detail
As a means for discovering library resources
Extending discovery value
Non-textual data, Faculty connections, Facilitate ILL

6. Optimizing Content Exposure Apps & Cloud Services

7. Anticipating User Intent
Highlighting Content
Library Interaction
Sampling of apps hosted for EDS
30+ EBSCO Discovery Field Engineers equals 100+ customer driven apps and plugins available

8. Curriculum Builder
Instructors stay within the Course or Learning Management System, and can easily add EDS content into their courses
Student reading lists in Moodle, Blackboard, Sakai, and more
all LTI-compliant CMS/LMS <

9. Item Request Clear full-text or request buttons are key MeSH Widget
Button Widget
PubMed Clinical Queries

10. Optimizing Results (Relevance)
RESULT LIST
Optimizing Results (Relevance)
With billions of potential results, there has never been a time when precision relevance ranking has been more important for libraries
Successful user experience is critical
Not just a single search of your collection, but a truly worthy academic research tool
A combination of factors enables optimal results for every search query…

11. Optimizing Results (Relevance)
RESULT LIST
Optimizing Results (Relevance)
Comprehensive coverage +
Detailed Metadata (including subject indexing) +
Additional Editorial Value +
Search Smarts (search technologies to maximize available information) =
Precision Ranking (best results for every query)

12. RESULT LIST
Optimizing Results (Relevance) Additional Editorial Value to further optimize Search Precision
Enhanced Subject Precision
EBSCO’s professional taxonomy team developed a knowledge base of topical concepts
Then mapped vocabulary terms from multiple thesauri to these concepts
When a user’s search term matches a known concept, the system uses appropriate mapped terms to precisely search against individual vocabularies

13. RESULT LIST
Optimizing Results (Relevance) Additional Editorial Value to further optimize Search Precision
Enhanced Subject Precision – WHY?
Because each vocabulary uses its own terminologies to account for essentially the same concept, we are able to normalize search such that users don’t have to know how each index might develop its taxonomy
Searchers use different search terms that are conceptually the same
Because EBSCO has unique approach to discovery, we can further maximize the value of these subject indexes for the benefit of the user
Ultimately, it’s about enhancing precision in search by augmenting unstructured keyword searches with structured subject index application

14. RESULT LIST
Optimizing Results (Relevance) Additional Editorial Value to further optimize Search Precision
Enhanced Subject Precision – Example:
EBSCO Concept Knowledge Base: “Work-related injuries”
Subject Index #1: “occupational injuries”
Subject Index #2: “occupational-related injuries”
Subject Index #3: “work-related injuries”
When the user’s keyword search matches the “Work-related injuries” Concept, EDS applies a subject heading expansion using the preferred subject heading in that target database, thus matching highly relevant content.
NOTE: This all occurs IN ADDITION TO our existing detailed relevance ranking

15. Optimizing Access to Full Text (Linking)
Greatest possible accuracy
A single click to the full text
Remove potential points of failure
Ensure common understanding of ideal scenarios (syntax, data, etc.)

16. Link optimization Link resolvers still flawed (20%+ fail)
An additional % end in user frustration
User Research shows that students learn (what not to do)
Direct links can be better, but all links are not created equally

17. Full Text Linking - Simplified
LINK RESOLVER
CROSS
REF
DISCOVERY SERVICE RESULTS
FULL TEXT CONTENT
DIRECT LINKS

18. Linking Basics (Syntax)
CONSTRUCTED OF MULTIPLE “BITS” OF DATA
ISSN
Volume
Issue
Page #
Open URL (standard)
Various forms of Proprietary Constructed URL (content provider “reacts well” to a specific link construction)
Durable URL (Root + Unique Identifier)
(Unique ID could leverage a DOI, for example.)

19. Linking Basics – What EBSCO is considering as a discovery provider
Gain an understanding of the link construction each content provider can readily accept
Which “Syntax” is OK/IDEAL for that content provider?
How Does Data from the Origin (Discovery Service) Support the Syntax?

20. What do we need to know to optimize links?
Syntax
Supported by Content Provider (Y/N)
Detail (Notes)
Open URL Y
Proprietary
Specific Construction
Unique ID
Data

21. Example: 3 Records for Same Article
Inspec
JSTOR
IEEE
(publisher)
ISSN
Vol
Issue
Page #
etc.
Slightly different value for a given index field from these providers
We receive data from IEEE
Ideal link Syntax for IEEE is known
Their preferred link construction
Ideal Syntax can’t be constructed
Generate ideal link for IEEE to deliver Fulltext
Accurate direct link
Link may not resolve
May drop the user at journal or issue level

22. Ideal links for any record…
Inspec
JSTOR
IEEE
(publisher)
Publisher data
Ideal syntax
IEEE Full Text
DIRECT LINK
Construct Ideal Link

23. More Linkouts to FULL TEXT
Direct custom links
SmartLinks+
Link Resolver

24. With EBSCO, there is more usage overall Direct Custom Links, Link Resolver & SmartLinks+
EBSCO generates more usage of publisher content than ever before through a combination of platforms - EBSCOhost and EBSCO Discovery Service

25. The link resolver is NOT A “SOURCE OF TRAFFIC”
It is simply a “bridge” between the article record in a given platform and the full text on the publisher sides
But, many times publishers can only see as far back as the link resolver
I.E. – can’t see the origin (platform) where the user made request for full text
Smartlinks plus
Specific article or record
PUBLISHERS
Direct custom link
EDS
Link Resolver
The origin of 40% of the traffic cannot be seen
The most heavily used link resolvers are provided by companies that also offer a discovery service – adding to the confusion

26. NISO’s Approved Proposal
Proposal was submitted to NISO to enable consistent visibility into where requests for full text originate
When finalized, link resolvers will have guidelines on how to pass data to content providers in consistent manner
Goal is to ensure that publishers (& Libraries) can accurately identify the levels of traffic from each platform (link resolvers are not platforms)
Proposal approved

27. SCOTT BERNIER | Senior Vice President
THANK YOU
SCOTT BERNIER | Senior Vice President
ext. 2278