This presentation was provided by Marydee Ojala of Information Today during the NISO event "The Impact of the Interface: Traditional and Non Traditional Content," held on November 20, 2019.

1. Sophisticated Searcher
Marydee Ojala
Editor-in-Chief, Online Searcher

2. About me
• Editor-in-Chief, Online Searcher
• Successor title to ONLINE, which merged with Searcher in 2013, published by
Information Today, Inc. (infotoday.com/onlinesearcher)
• Write Dollar Sign column about business resources
• Conference Program Director
• Internet Librarian International (internet-librarian.com)
• Enterprise Search & Discovery Summit (enterprisesearchanddiscovery.com)
• Data Summit (dbta.com/datasummit)
• Contributor to WebSearch University, Computers in Libraries,
Internet Librarian, other library/technology conferences

3. Sophisticated user
• A sophisticated user of what?
• Library services
• Library collections
• Digital information
• Sophisticated searcher
• Super searcher
• How much sophistication will be needed going forward?

4. What makes a super searcher?
• Lessons from Super Searcher books
• Joy of the chase
• Intrigued by intricacies of search strategizing
• Understands information architecture
• Willing to try almost anything to get to the answer
• Enjoys finding needles in haystacks
• Tenacious and curious
• Likes language

5. Brief history of search
• Designed for info pros, intermediary searchers
• Extreme Boolean
• Structured data
• Textual
• Expensive
• Some of search legacy still exists

6. Search today from users’ perspective
• Search = Google
• Google Scholar can replace library subscription databases
• Research requires more than Google
• Googley expectations
• Simplify, simplify, simplify
• TL;DR
• Google does not do Boolean

7. Search strategies
• Sophisticated searchers still use Boolean to good effect in searching
bibliographic databases
• Strategies depend on intent
• A few good articles for an undergraduate paper
• A business decision for an MBA student
• A search to determine patentability
• A systematic review
• Examples from business, patent, medical

8. Business
• ((pet OR dog OR cat OR fish) ADJ food) AND ((market ADJ3 (share OR
size OR trends)) AND (us OR united states OR canada OR britain OR uk
OR united kingdom)
• Factiva syntax
• ((pet OR dog OR cat OR fish) PRE/2 food) AND (market NEAR/3 (share
OR size OR penetration)) AND (us OR united states OR uk OR united
kingdom OR Britain)
• ProQuest syntax

9. Patent
• Patbase search (courtesy Tom Wolff, “Mistakes Happen: A
Patentability Case Study”, Online Searcher, Nov/Dec 2019) for trigger-
activated animal nail clippers
• tac=((trigger* or activat* or actuat* or releas*) w5 (clipper* or
trimmer* or cutter* or nipper*))
• tac=((nail* or claw*) w5 (clip* or trim* or cut* or nip*))
• sc=A45D29/02 – CPC/IPC patent class on Nail clippers or cutters
• uc=30/28 – US patent class on Manicure… nippers

10. Medical
• Metformin AND (“adverse drug reaction” OR “drug overdose” OR
“drug misuse” OR “drug abuse” OR “substance abuse” OR pregnancy
OR “drug efficacy” OR “drug withdrawal” OR “drug tolerance” OR
“medication error” OR death OR “drug interaction” OR
carcinogenicity OR “off label drug use” OR “occupational exposure”
OR toxicity OR intoxication OR “drug contraindication” OR “congenital
disorder” OR “drug treatment failure” OR lactation OR “case report”
OR “environmental exposure” OR “treatment
contraindication”)
• PubMed syntax

11. Unsophisticated searcher
• Pet food
• Clippers
• Metaformin
• Assumption is that Google (or another web search engine) will intuit
what the searcher wants to know
• Single interface to all knowledge

12. Web search is different
• Boolean doesn’t really work
• Long search queries with Boolean logic, nested terms, bound phrases
don’t work
• The NOT command is problematic
• Proximity operators are close to non-existent except for exact phrase
• Relevancy is determined by machine learning
• The interface is increasingly voice
• This sets expectations for library database platforms

13. Who is searching
• Everybody
• Do they want to learn how to search?
• No, they just want to search
• And they all think they are expert, sophisticated searchers
• Browsing versus searching
• Web search is grounded in shopping, transactional, quick answers

14. Finding a flight

15. Fact-based query
• What was the name of Hopalong Cassidy’s horse?

16. What students want
• Fast response time, convenience
• Relevant results
• Complete answers
• Unshelved.com – July 29, 2010 – Complicated question
• Visualization – show me
• Analytics – explain what it means

17. Disambiguating
• Civil War
• Whose Civil War?
• What countries?
• When?
• Did they even call it a Civil War?
• Could new technology answer these questions without a librarian
doing a reference interview?
• Geolocation, knowing what in which courses the student is enrolled
• Previous search history, personalization

18. Evolution of search technology
• From text to multimedia
• From command language to AI-driven
• Graph databases, Predictive analytics, Natural Language Processing, Semantic
search
• Machine learning
• Thesauri, Metadata, Controlled vocabulary
• Big data
• What happens when everything is digitized and machines can read all of it

19. Multimedia
• Default for web search
• Art students want images
• Search by color, technique?
• Music students want sound
• Search by tune?
• Theater students want actual performances
• Search by stage, by costume?
• We’re not quite there yet

20. AI technologies
• Knowledge graphs – semantic technology, network of things we want
to describe and how they are related – used extensively by web
search engines
• Predictive analytics – text analytics – analyzes documents to
determine what actions to take, identify outliers
• Natural Language Processing – disambiguating language in full text
search
• Semantic search – context not just content
• Machine learning – Indicator of relevance based on prior search
behaviors

21. AI for thesauri, metadata, controlled
vocabulary

22. Big data
• Pattern matching in millions of documents
• Unstructured information
• Overwhelming amount of available information
• Legal contracts
• Digital humanities
• Predicting recidivism
• Possibility of bias

23. Search platforms
• Content dictates information architecture
• One search box to rule them all won’t happen
• Intent remains critical component
• What is intuitive to you may not be intuitive to me
• Relevancy is in the eye of the beholder
• Digital transformation is ongoing

24. Sophisticated user
• How much sophistication will be needed going forward?
• When will search strings become obsolete?
• Will search results be totally non-textual?
• Can computers take over research?
• Who checks for bias?
• Information professionals must be flexible and willing to unlearn
techniques they swore by in the past

25. Contact details
• Marydee Ojala
• Editor-in-Chief, Online Searcher (www.infotoday.com/onlinesearcher)
• Marydee@xmission.com