What do PLI, MetOpera, ASCO, and PLOS have in common? Content management and content discovery needed major improvements. User were not getting the results they needed. The content production team including editorials, managing editorials – the whole team – could no longer cope with the volume and variety. Content quality was suffering. Brief discussions of each organization’s challenges set the stage for AI-based, human curated solutions. What worked, what didn’t, and the how and the why will be presented.

1. Semantic Search and Content Management
Case Studies in Successful Software Implementations
Marjorie M.K. Hlava
Founder/President
mhlava@accessinn.com

2. ü Content management and content discovery needed major
improvements.
ü User were not getting the results they needed.
ü The content production team including editorial, managing editors –
the whole team – could no longer cope with the volume and variety.
ü Content quality was suffering.
ü Brief discussions of each organization’s challenges set the stage for AI-
based, human curated solutions.
ü What worked, what didn’t, and the how and the why will be
presented.
What do PLI, Met Opera, ASCO, McGraw-Hill and PLOS have in
common?

3. Who are they?
üPracticing Law Institute
onon-profit continuing legal education organization
ochartered by the Regents of the University of the
State of New York.
oFounded in 1933
othe company organizes and provides CLE programs
around the world.
üHow to make their content discoverable
üHow to reuse and cross reference training materials
üTime crunches and staff overloaded already
üLot of content so need automation to make this happen

4. First Auto-tag the Content When Ingested

5. Keep Track of Every Record and the Level of Tagging

6. Everyone Gets a Login to the Metadata Platform
– Levels of Permission Differ

7. üTailored the taxonomy to the content
üAuto-tagging on backfile and forward flow production
üEnsure accuracy in tagging with QA program of spot
checking and automatic monitors
üEnriched data quality and coverage
üAllowed creation of new CLE offerings through content
reuse
üSignificantly improved search for both staff and
customers

8. Financed by programs
and donations
Database, DOS based
implemented in 1980’s
Performers use the
archives database as
their resume referral
system
Keep track of every
performer in every
scene of every opera
ever preformed at The
Met
Time critical data input
needed
Archives mostly staffed
by volunteers – make
database easy to use
Metropolitan Opera

9. Metropolitan Opera

10. Metropolitan Opera
üAdd images and videos
üConnect to costume database
üSupport the archives website
üAllow immediate entry of data
after each performance in season
üSubmit comments and
corrections online
üMake search fast, accurate, and
easy

11. American Society for
Clinical Oncology
üWeb search inconsistent results
üTaxonomy to support synonyms and variant
term usage
üInsure comprehensive search
üType ahead in search
üSort conference papers into tracks
üTag to user profiles for better matching of
talks to attendees
üCreate meta-titles for better search SEO
üUse in journal productions tracking
üHelp match peer reviewers to potential
papers

12. American Society for Clinical
Oncology
Metastatic 170,000,000
Stage 4 296,000,000
Invasive 92,000,000

13. Consistent Search Based on
Taxonomic Metadata
üSearch for
oInvasive breast cancer
oMetastatic breast cancer
oStage IV breast cancer
üGet a single search set result
whatever term set you enter
Funding – 2
Conferences -3862
Journals 2993

14. Well formed vocabulary control Rich Synonymy and cross references Deep automatic tagging
Taxonomy – Tagging – Data Enriched

15. - Content Analysis
- Term Identification
- Multiple Taxonomies
with Weighted Terms
- Enriched Content (XML,
Video Transcript, Excel)
Automated
Tagging
- Taxonomy Development
- AI Training Set
- Classifying
- Metadata Enrichment

16. Lauren Sapira
Director, Sci/Tech Digital Products
“
“
Before we did this upgrade project, we heard
all kinds of complaints about our search.
Since we relaunched, there have been no
complaints at all!

17. Public Library of
Science
üTag every document on ingestion
üWebsite search platform
üThe search hierarchy is visible to 3
levels deep for enhanced search
üUsers can vote on keywords used to
train the system
üIndex to the most specific level
üEditors can view the entire hierarchy
üSearch is keyword driven based on
the taxonomy metadata
üTrack every usage of data by
searchers

18. Public Library of Science
üNeed to keep track of all incoming papers as
well as usage by customers
üWhat new journals should we create, and
which are not doing well?
üWho uses what on the web site
üWhat works, what does not
üIf we change something, what is the impact?

19. Show keywords
and allow users
to suggest
additions or
deletions

20. Automatic term suggestions for incoming documents

21. Automatic weighting of
taxonomy terms based on
source content

22. Summary
Content enrichment gives better data
Use subject metadata for better search
Don't launch at less than 85% accuracy
-- Precision and recall combined
Unstructured data is still structured
and can be tagged
Keep track of the data usage

23. About
ALBUQUERQUE
PHILADELPHIA
Cambridge
• Established in 1978
• Experts in semantic solutions
• Over 300 clients on 4 continents
• Over 2,000 project engagements
• 95% YOY client retention
• Data Harmony Suite
• Award Winning
• Patented Technology
• Semantic Services
• Managed Services
• Project Services

24. Clients
Publishing & Media
Education
Government
Non-profits &
Societies
Health/Pharma
Manufacturing
& Retail

25. Services Overview
We are experts in semantic solutions
ü Metadata Creation and Enhancement
ü AI Training Set Development
ü Custom Data Classification
ü Develop Controlled Vocabularies
ü Abstract and Index Documents
ü Automated Indexing
ü Capture and Convert Data
Not only do we build solutions that are compliant, in
many cases, we built or influence the data standard!

26. Semantic Environment
ü Concept Extraction
ü Term Recommendations
ü Sentiment Analysis
ü Visualization Tools
ü NLP Precision Monitoring
ANALYZE
ü Taxonomy Building
ü Thesaurus Building
ü AI Training-Set Building
MODEL
ü Abstracting
ü Classifying
ü Metadata Enrichment
ü Inline Tagging
ü Meta-Titles
ENRICH

27. Marjorie MK Hlava
Founder/President
mhlava@accessinn.com
(m): 505-975-5578
Change Search to Found
We are the intelligence and the technology behind world-class semantic solutions