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EBSCO Discovery Service

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Page 1: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

EBSCO Discovery Service

Page 2: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

Discovery Background

– Quickly– By small development teams– Using rudimentary relevance algorithms built around

searching article titles and words within the full text– Often as a side project for companies who invest

more technical resources into ILS

First generation discovery services were developed:

Page 3: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

EDS Background

– Years in the making– Extraordinary technical development resources pre-launch – By far the main technical focus of EBSCO post launch

(EBSCO is not diverted by ILS, instead choosing to partner with existing ILS vendors):

• 330 of 424 developers & software engineers working on EDS as of 2014

– Sophisticated relevance and value ranking algorithms refined through unparalleled end user testing

EDS is next generation technology:

Page 4: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

Discovery Service Adoption Worldwide 2014

EBSCO stands as the front-runner, with a long lead of 5,612 library subscribers to EDS.

OCLC reports 1,717 libraries with access to WorldCat Local, though a smaller number use it as their primary discovery interface.

Ex Libris has licensed Primo to 1,407 libraries.

ProQuest reports 673 libraries using Summon.

American Libraries April 15, 2014[Marshall Breeding]

Page 5: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

NISO Report“The Future of

Library Resource Discovery”

Marshall BreedingFebruary 18, 2015 http://americanlibrariesmagazine.org/2015/05/01/library-systems-report/

Discovery Service Adoption Worldwide 2015

EBSCO, 8,246, [68%]

PRIMO,1528 [13%]

Summon, 697, [6%]

Enterprise, 538, [4%]

Encore, 346, [3%]

Others, 767, [6%]

Page 6: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

What is Discovery? Three Interlinked Systems

EDSPublication

FinderFull-Text Finder

Page 7: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

Global Search Scope

Local Search Scope

Link to FullText

Think of these systems as

Page 8: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

EDS

Publisher Provided Full-Text(Springer, Wiley, Elsevier, etc.)

Partner Databases(Web of Science, SCOPUS, TEMA etc.)

Partner Databases on EBSCOhost(PsycINFO, INSPEC, etc.)

Library Catalog

Institutional Respository

EBSCO Discovery Service

Page 9: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

Focus on the user and all elsewill follow…

Google: 10 things we know to be truewww.google.com/about/company/philosophy

Page 10: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

Using EBSCO Discovery as the library home page

Page 11: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

Embedding EBSCO Discovery in the Library homepage

Page 12: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

Offering Guest access

Page 13: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

Enhancing the Search Box ...

Page 14: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

Results enhanced with Research Starters, Koha Catalogue, widgets

Page 15: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

Increasing visibility of your collections

Page 16: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

Focus in on a particular resource

Page 17: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

Incorporate the OPACe.g. Heritage

Page 18: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

Shared catalogues

Page 19: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

Showcase certain products

Page 20: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

Incorporating local collections

Page 21: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

Highlight ebooks

Page 22: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

No dead-ends

Page 23: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

Incorporate Inter-Library Loan

Page 24: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

Discovery Services: A White Paper For The Texas State Library & Archives Commission

By Arta Kabashi, Christine Peterson, And Tim Prather Amigos Library Services

August 2014

Page 25: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

Link to Executive Summary

Discovery Services: A White Paper For The Texas State Library & Archives Commission

By Arta Kabashi, Christine Peterson, And Tim Prather Amigos Library Services

August 2014

Page 26: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

EBSCO’sRelevance Ranking

The system has the following priorities and has

no bias toward content from any provider:

1) Match on subject headings from controlled vocabularies

2) Match on article titles

3) Match on author keywords

4) Match on keywords within abstracts

5) Match on keywords within full text

Exact Matches: Exact matches are favored over partial matches – considering also the field in which those words appear (abstract vs. full text vs. title, etc.)

Density: The number of times the word(s) appears relative to the size of

the document (more is better) – considering also the field in which those words appear (abstract vs. full text vs. title, etc.)

Page 27: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

Evaluating the Central Index

• Is the indexed content a good fit?– Inclusion of your journals titles and holdings– Inclusion of your subscribed databases

• Richness of metadata• Ability to incorporate desired content in the

future

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Athena Hoeppner

Page 28: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

Concept-based Search

• EDS includes content with high-quality subject indexing using different specialized vocabularies

• Unfortunately, user search terms and precise subject indexing terms do not always align

• Opportunity exists to better leverage subject indexing for keyword-based discovery in EDS

User Search Query

Subject Indexing in EDS

adhd

Attention-deficit hyperactivity disorder

Attention Deficit Disorder with Hyperactivity

global warming

Climate changeClimatic changes

fracking hydraulic fracturing

tumors

tumoursNeoplasms

Neoplasms, Cystic, Mucinous, and Serous

Page 29: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

EDS includesmapping of “see also” and “use for” terms in nearly 100 index thesauri• Academic Search• America: History & Life• Art Abstracts• ATLA Religion Database• Business Source• CAB Abstracts• CINAHL• GeoRef• HeinOnline• Historical Abstracts

• Inspec• MathSciNet• MEDLINE / PubMed• MLA International

Bibliography• PsycINFO• RILM Abstracts of

Music Literature• SocINDEX• SPORTDiscus

Page 30: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

Team, Full Support of Discovery• Discovery Solutions Coordinator• Software as a Service (SaaS) Operations• Catalog Specialists• Training Specialists• Discovery Service Engineer• Technical Support

Page 31: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

Support network>8,200 ‘developers’, ‘support staff’ for EDS

Page 32: EBSCO Discovery Service. Discovery Background –Quickly –By small development teams –Using rudimentary relevance algorithms built around searching article

[email protected]

@choneybourne

Thank You