- Web scale discovery services provide a single search box to search across a library's subscribed resources including journals, books, databases, and more. They index these resources upfront to provide fast search results compared to federated search which searches resources individually. - Key parameters for evaluating discovery services include coverage, relevance ranking methodology, metadata quality, search refinement options, value-added features, and customer support. Subject indexing can be improved through "platform blending" which leverages subject indexes from databases. - User studies have shown discovery services can improve search effectiveness for users compared to individual library databases or Google Scholar. Local support from the discovery service provider is important.

1. Nikesh Narayanan M.Tech, MLIS, M.Com
Discovery Service Engineer
EBSCO Information Services
nikeshn@gmail.com
Choice of Discovery
Evaluation of Web Scale Discovery Services

2. Agenda of the presentation
• Web Scale Discovery- What & Why ?
• Web Scale Discovery Vs Federated Searching
• Components of Discovery Service
• Evaluation parameters
• Live Demonstration

3. Important point #1
0% of users start their research on your
library’s website
(95% close to reality )

4. Important point #2
90% of users Users prefer Google as their first
search preference

5. Why users move away from Library ?
• library lacks a research tool that could fully
search all the subscribed resources of the
Library
• Library website usually present too many points of
entry; A variety of different databases make it
confusing to figure out where to go.
• Users wanted to start searching immediately from a
single entry point

6. – Books: Library OPAC (ILS
module)
– Articles from Individual e-
Journals
– Various e-Book collections
– Different e-journal publisher portal
– Aggregated : Full text and
Bibliographic Databases
– Databases that are not Subject
Indexes (WoS, Scopus etc.)
– Local Digital Collections (IRs)
Users’ Dilemma
Disjoint Sources of Information
Where to Start?
Why users move away from Library

7. Charleston 11-07
Where
should I
begin?

8.  Dissatisfied users
 Under utilized resources
 Loss of money time and efforts
 Unable to provide relevant information to users at right
time
 Inefficient mark on Library system ?
 Ultimate loss to institution & nation
Adverse consequences

9. Users Go to Google
But fail to get their relevant and authentic information
What is the solution ?

10. A Simple Vision
A single point entry to all the content and services offered by
the library…..
Search e-Journals, books, catalogue, IR’s etc.
….as well as providing an enjoyable search experience
Search

11. • Federated Search Software
• Single Search box
Web scale Discovery is beyond …..

12. Federated Search Software
Search:
Search Results
Real-time query and responses

13. Search:
Search Results
Pre-built harvesting and indexing
Meta
Search
Index
Local
Index
OPAC
Digital
Collections / IRs
Discovery Services

14. Discovery Service Federated Search Engines
Search is very fast as retrieval is done in pre-
harvested index
Slow (longer time for search completion) –
Federated searching is performing the meta
search on the fly from different resources
Standardized unified Index Many indexes: Individual indexes and different
database structures of various publishers makes
it difficult for metadata retrieval.
Robust Relevancy Ranking as retrieval is from
Unified index
Testing has proven that relevancy ranking is a
major issue in retrieval of quality data.
Metadata Enhancement is possible Metadata enhancement is not possible
Comprehensive results Shallow results -and eventually users will miss
much relevant content.
Performance quality is very high Many times important information from
relevant resources are missed out due to
connection error.
Subject filtering of result set is based on the
segmentation ( pre-defined controlled
vocabularies)
Subject filtering is through automatic clustering
and noise of non- standardized vocabularies
may mislead the users.
Discovery Vs Federated Search Engine

15. Main Components
Content (Knowledge Base)
Technology

16. Content (Knowledge Base)
– Journals
– Books
– Databases
– Aggregators content
– Open source Materials
– News papers, indexes etc.
– Library Catalogue
– Institutional Repositories
Base Index
Local Index

17. Technology
 Harvester (OAI/PMH etc.)
 Automated transfer routines, load tables
 Metadata mapping
 Platform blending
 Indexing technology
 De-duplication Algorithms
 Link Resolvers
 Relevancy Algorithms
 Interface technologies

18. Web Scale Discovery- Major Players

19. Open source alternatives
• Blacklight
• VuFind
• SOPAC (Social Opac)
• Scriblio
• Fac-Back-OPAC

20. All discovery services have these
Common Features
• Single Search Box
• Unified Index

21. Stakeholders Strive to Define Standards
for Web-Scale Discovery Systems
By Michael Kelley on October 11, 2012
clarifies various important factors
for implementing Discovery Services

22. All discovery services have these
Common Features
• Single Search Box
• Unified Index

23. Parameters for Discovery Evaluation
• Coverage
• Relevance ranking (stated parameters)
• Quality of metadata
• Subject index (Platform Blending)
• Search results , refinement & filtering
• Value added features
• Customer support (especially local support)

24. Parameters for Discovery Evaluation
• Coverage
• Relevance ranking (stated parameters)
• Quality of Metadata
• Subject index (Platform Blending)
• Search results , refinement & filtering
• Value added features
• Customer Support (Specially Local Support)

25. • Agreement with publishers ( Ask for the list)
• Should not depend on crawled downloads
• Coverage in central Index
• Full Text index- Some publishers provide
their full text content to Discovery Service
providers for indexing purpose.
Coverage- Knowledge Base

26. No Discovery Service is 100%
Complete

27. Make sure that Discovery service providers have
alternative integration options for non- covered
resources in the central index.
Some alternative options
•Integrated Federated connectors with Discovery
platform (results in same interface)
•Widgets

28. Federated connector along with discovery

29. Federated connector along with discovery

30. Federated connectors – top results

31. Widgets

32. widgets

33. Parameters for Discovery Evaluation
• Coverage
• Relevance ranking (stated parameters)
• Quality of Metadata
• Subject index (Platform Blending)
• Search results , refinement & filtering
• Value added features
• Customer Support (Specially Local Support)

34. User surveys show,
80% users expect the most relevant results on
the first 5 pages
“It is imperative, the most relevant & most valuable
articles should be on the first few pages”

35. Example- Relevance Ranking
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

36. Other Value Ranking if any ?
Example
Currency:
When relevance is equal,
the more recent journal
articles rank higher
Document Type:
In relation to the search terms,
document types may be
relegated to lower relevance
For example, when the word “book” or “review” is not searched,
there is a “bias” against book reviews
Length:
Articles of a more substantial
length have a heavier weighting

37. Parameters for Discovery Evaluation
Source – Library Journal (Oct / 12)
• Coverage
• Relevance ranking (stated parameters)
• Quality of Metadata
• Subject index (Platform Blending)
• Search results , refinement & filtering
• Value added features
• Customer Support (Specially Local Support)

38. Priority Order
of Article
Metadata
Better than
nothing
Pretty good
Best!
Even better when
contributed by a
leading subject
index database
subscription!
Even better with
searchable full
text!

39. Example

41. Parameters for Discovery Evaluation
• Coverage
• Relevance ranking (stated parameters)
• Quality of Metadata
• Subject index (Platform Blending)
• Search results , refinement & filtering
• Value added features
• Customer Support (Specially Local Support)

42. Subject Index providers do
not participate
in any Discovery Services

43. But can be made possible through
“Platform Blending” technology

44. How Does Platform Blending Work?
A fast, direct & smart line of communication between EDS and EBSCOhost
Fast = All resources loaded locally on EBSCO systems
Direct = A fully-enabled, dedicated open interaction between
EDS and high-end indexes on EBSCOhost
Smart = Technology developed to leverage relevancy ranking
from EDS, facet utilization & fully integrated results
Example

45. Discovery record with INSPEC Metadata

46. Parameters for Discovery Evaluation
• Coverage
• Relevance ranking (stated parameters)
• Quality of Metadata
• Subject index blending
• Search results , refinement & filtering
• Value added features
• Customer Support (Specially Local Support)

47. Evaluate the facets options
Users might be
interested to
know from
where all the
results are
coming from

48. Parameters for Discovery Evaluation
• Coverage
• Relevance ranking (stated parameters)
• Quality of Metadata
• Subject index (Platform Blending)
• Search results , refinement & filtering
• Value added features
• Customer Support (Specially Local Support)

49. Availability of Web 2.0 features

50. Availability of Catalogue Enhancements

51. Availability of Journal ranking

53. USER STUDIES

54. Published in
College & Research Libraries
“Paths of Discovery:
Comparing The Search Effectiveness
of EBSCO Discovery Service, Summon,
Google Scholar, and Conventional
Library Resources”
April 15, 2012
Read PDF Article

55. Availability of local support ?
• Local Implementation Specialist
• Discovery Service Engineer
• Specialist trainers

56. ?
Thanks!