User Search Terms and Controlled Subject Vocabularies in an Institutional Repository

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rshanrath

Presented Erik Radio at Open Repositories 2015, June 10, 2015

Technologie

User Search Terms and Controlled
Subject Vocabularies in an
Institutional Repository
Scott Hanrath | shanrath@ku.edu |@rshanrath
Erik Radio | radio@ku.edu
University of Kansas Libraries

User Search Queries
User Queries  FAST
User Queries  Item Metadata

User Search Queries
Google Analytics Segment:
• Landing Page = Item simple record page
• Next interaction = “BitStream Click” Event
• Keyword from Search Source is set

User Search Queries
• 4 month period, January - April 2015:
• 2,209 Query – Item combinations
• ~ 15% of the total BitStream Click Events
recorded

Reconciling Queries Against FAST
• Open Refine
• Ted Lawless’ refine reconciliation service
using the assignFast API:
https://github.com/lawlesst/fast-reconcile
http://www.oclc.org/developer/develop/web-
services/fast-api/assign-fast.en.html

Reconciling Queries Against FAST
baba yaga 

Image: http://commons.wikimedia.org/wiki/File:Bilibin._Baba_Yaga.jpg

# of Queries Auto-Reconciled Against
FAST
46 of 2209
(2%)

Manually-split Queries
• Sample of 300 queries.
• 97 (32%) were split.
 cognitive disability
 internet
cognitive disability and
internet

# of Manually-split Queries Auto-
Reconciled Against FAST
84 of 300
(28%)

Other Ways to Classify the Sampled
Queries
• Known Item Searches
• Google Scholar Related Item Searches
• Query similarity to Item Metadata

Google Scholar Related-Item Searches
(related:[some-id]:scholar.google.com/)
10 of 300
(3%)

Query Similarity to Item Metadata
• Fuzzy string matching: Levenshtein Distance
between query and item metadata values for
Title and Subjects
• Normalize score by string length, 0 to 100
scale (100 = same string)
• For multi-valued metadata fields, choose best
match with query

Japan
Mt. Fuji
Tourism
Environmental
Protection
[… more]
Query Similarity to Item Metadata
Query
mt fuji tourism
Subjects
Split Query
mt fuji
tourism
Best Score
46
Best Score
100

Query Similarity to Item Metadata
Field N Not Very Good
(< 75)
Pretty Good
(75 to 95)
Very Good
(95 to 100)
Title 298 91.6% 3.0% 5.4%
Subjects 197 95% 1.5% 2.5%
Subjects,
Split
Queries
197 87.8% 3.0% 9.1%

?Scott Hanrath | shanrath@ku.edu |@rshanrath
Erik Radio | radio@ku.edu
University of Kansas Libraries

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User Search Terms and Controlled Subject Vocabularies in an Institutional Repository

1. User Search Terms and Controlled Subject Vocabularies in an Institutional Repository Scott Hanrath | shanrath@ku.edu |@rshanrath Erik Radio | radio@ku.edu University of Kansas Libraries

2. What is this all about?

3. KU ScholarWorks and FAST

4. User Search Queries User Queries  FAST User Queries  Item Metadata

5. User Search Queries Google Analytics Segment: • Landing Page = Item simple record page • Next interaction = “BitStream Click” Event • Keyword from Search Source is set

7. User Search Queries • 4 month period, January - April 2015: • 2,209 Query – Item combinations • ~ 15% of the total BitStream Click Events recorded

8. Reconciling Queries Against FAST • Open Refine • Ted Lawless’ refine reconciliation service using the assignFast API: https://github.com/lawlesst/fast-reconcile http://www.oclc.org/developer/develop/web- services/fast-api/assign-fast.en.html

9. Reconciling Queries Against FAST baba yaga 

10. Image: http://commons.wikimedia.org/wiki/File:Bilibin._Baba_Yaga.jpg

11. # of Queries Auto-Reconciled Against FAST 46 of 2209 (2%)

12. Manually-split Queries • Sample of 300 queries. • 97 (32%) were split.  cognitive disability  internet cognitive disability and internet

13. # of Manually-split Queries Auto- Reconciled Against FAST 84 of 300 (28%)

14. Other Ways to Classify the Sampled Queries • Known Item Searches • Google Scholar Related Item Searches • Query similarity to Item Metadata

15. Known Item Searches 52 of 300 (17%)

16. Google Scholar Related-Item Searches (related:[some-id]:scholar.google.com/) 10 of 300 (3%)

17. Query Similarity to Item Metadata • Fuzzy string matching: Levenshtein Distance between query and item metadata values for Title and Subjects • Normalize score by string length, 0 to 100 scale (100 = same string) • For multi-valued metadata fields, choose best match with query

18. Japan Mt. Fuji Tourism Environmental Protection [… more] Query Similarity to Item Metadata Query mt fuji tourism Subjects Split Query mt fuji tourism Best Score 46 Best Score 100

19. Query Similarity to Item Metadata Field N Not Very Good (< 75) Pretty Good (75 to 95) Very Good (95 to 100) Title 298 91.6% 3.0% 5.4% Subjects 197 95% 1.5% 2.5% Subjects, Split Queries 197 87.8% 3.0% 9.1%

20. Summary and Next Steps

21. ?Scott Hanrath | shanrath@ku.edu |@rshanrath Erik Radio | radio@ku.edu University of Kansas Libraries

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