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Supercharge
Your AI
Marjorie M.K. Hlava
Chief Scientist
Access Innovations, Inc.
mhlava@accessinn.com
www.accessinn.com
Marjorie M.K. Hlava
• Expert in taxonomies, metadata, their application and data science.
• Her groundbreaking work has earned her numerous awards and 2 patents
with 21 claims granted
• Margie standards work includes
• Dublin Core Z39.85,
• DOI Syntax Z39.84, ,
• CrEdit Z39.104,
• ThesaurusANSI/NISO Z39.19 Thesauri and other controlled vocabularies
• many others.
• Currently Convener of the ISO - 25964 the International Standard on
Controlled Vocabularies
• Founder, Chairman, Chief Scientist of Access Innovations, Inc.
”large language models will not only mirror but magnify any problems with the
data sets, problems that many organizations may not realize they have."
Amplifying hidden biases and gaps seems like a real danger
We have content we
want to “slice and
dice” to create new
derivative products
We need to sort 1,000s
of journal
article/conference
session submissions
We need web
site navigation
We have content
that people can’t
find
We need to find
peer reviewers
We need to
personalize
conference sessions
Departments have
different vocabularies,
don’t talk to each other,
data is siloed and work
is duplicated
And Now… AI…
Large Language
Models
ChatGPT?
What’s different now??
Size is here
Server Farms
Power is a Concern
• Data – well enriched is
the key to
• Ontologies
• Search excellence
• Knowledge maps
• Knowledge graphs
• Data is the LLM core asset
• Without the data the rest of the initiative is nothing
• It is the essential component the strategy
• Do enrichment metadata
• SUBJECT metadata
• Use taxonomies, ontologies, and other models.
• The large language models will not only mirror but magnify any
problems with the data sets, problems that many organizations may
not realize they have. (Gary Carlson - Factors)
Technology
• It is a tool, not the focus
• Might need shiny new piece of technology,
• the technology is generally in the chorus
• not a main character
• Too many companies lead with technology and
• do not spend the time understanding their users or aligning
their strategy.
• Any company that has 1000s of Sharepoint or Teams sites where
people still can’t find the information they need knows this.
• Most large corps have 5 search software systems
• On the shelf
• “does not work”
• Because the data was not enriched
Governance and modeling
• Taxonomy and data modeling
• essential component of this investment.
• Data must be
• well sourced,
• managed
• Consider for ethical and performance reasons
• Ignore data quality at your peril
• It is hard work –
• Does not fit two-week sprint
• Get executives to agree on strategy and structure model
• Without a coherent model, governance, data pipeline, and resourcing
there is no strategic value to an AI initiative
Gartner says: “By 2024, companies that use graphs
and semantic approaches for natural language
technology projects will have 75% less artificial
intelligence technical debt than those that do not"
How??
• Use existing standards, schemas and ontologies as starting points.
• Extract a list of key terms that need to be modeled using data
mining/entity extraction/data profiling tools.
• Add handcrafted rules, entity attributes and relationships from
business glossaries and data dictionaries.
• .” [1] Gartner report, “How to Build Knowledge Graphs That Enable AI Driven Enterprise Applications”, 27 September 2022, ID G00768041, Afraz Jaffri.
• Does the data need to be
structured for AI?
• No
• Takes in text images, sounds in
all formats
• Do I need a new platform to offer
my content in Ai?
• No
• How can I ensure searchers will not
get hallucinations and wrong
answers?
• Guide it – tag it
• Do I need to protect my content?
• Yes
• When does this happen in the
workflow?
• Early as you can
What’s the process?
• Need a controlled vocabulary
• Keywords, taxonomy terms, entity
identification
• Apply it to your data
• Automatically if possible
• Use the power of the LLM
• Keep your data separate
How it trains
• Learns from itself – the written
resources
• Longer it is used – more accurate it
becomes
• Learns from interactions
• Studies the grammar
• Analyze the sentence
• Order of words
• Possible meanings
• How they fit together
• THEN make a prediction
• Continuations one word at a time
– dependent clauses
• Looks human in response
Dump in the data to the AI vortex
No work needed
Everything will be fine….
Bludgeon your data
Bludgeon your data
Taxonomy Priority (Semantic) Enrichment
LLM’s Need a Little Help
• To be accurate
• To avoid hallucinations
• Enhance the data
• Tag it with controlled terminology
• Add synonyms
• Suggest structure
“AI” + GenAI
• Start with enriched content
(tagged)
• Tell (feed to) GenAI
• GenAI puts new rules in the
inference engine
• Search results get better
• Repeat, repeat, repeat
Sounds too easy --- okay here’s more detail
• Taxonomies
• Available Knowledge domains
• Links to
• Knowledge graphs
• Ontologies
• Why the tagging?
• Is it expensive / time consuming?
• What about protecting my content?
What Are The Steps To Implement
Knowledge Domains In Generative AI?
• Define the Taxonomy Structure
• Collect and Preprocess Data
• Tag your Data with Taxonomy concepts
• Train the Generative AI Model
• Incorporate Taxonomy into Model Inference:
• Evaluate and Iterate
• Deploy and Monitor
• Add the SME’s
• Collaboration between domain experts,
data scientists, and AI engineers is crucial
for the success
Why a taxonomy?
• Matches your content
• Scales with the content increases
• Extensive synonymy – use any of the word term options
• The concept is the unit of thought
• Disambiguation
• Mercury
• Lead
• Built in feedback loops to keep current with content
• Prevents hallucinations
• Misunderstandings of multiple word meanings (Nonsensical output)
• Happens when the model is not trained on your content (Factual contradiction)
• Query goes against the rules of the system (Prompt contradiction)
How Can Knowledge
Domains Help LLM?
• Understanding Input
• Content Organization
• Knowledge Representation
• Query Expansion
• Quality Control
• Content Guides
Knowledge Domain
• Refers to a specific area or field of
knowledge
• subject matter, concepts, theories,
methodologies, and practices.
• Cohesive and organized body of
knowledge with a scope and boundaries.
• Vary widely in size and complexity,
• Established disciplines or sub-disciplines,
• theories, methods, and research traditions.
• Frameworks within specific areas
• Scholars, researchers, practitioners, SME’s
• Thesaurus with a rule base
Knowledge Domains
• Taxonomies, thesauri, or authority files
• Pre-Built
• Knowledge Domains
• Full term records
• hierarchical, equivalence, and
associative relationships, as well as
scope notes where appropriate.
• Hierarchy alone
• NISO Z39.19 and ISO 25964 compliant
• Formats,
• 22 options, Excel/CSV, 6 flavors of
SKOS, HTML, XML, SSL, etc.
Applied Science
Art
Behavioral Science
Biological Science
Business
Chemical – MAI Chem™
Communications
Computer Science
COVID
Economics
Educational Curriculum
Geography
Health and Safety
Health Science
History
Information Science
Language Arts
Law
Linguistics
Literature and Drama
Mathematics
NewsThes
Nursing
Philosophy
Physical Education and Recreation
Physical Sciences
Political Science
Psychology
Religion
Science
Social Sciences
General Purpose Taxonomies
These products can be SKOS downloads
Astronomy
Clinical Drugs
DTIC – Defense Technical Information Center
Environment – GEMET
ERIC – Education Resource Information Center
JSTOR
NASA
National Agricultural Library
Occupational Safety and Health
PLOS
CPT – Current Procedural Terminology
HCPCS – Healthcare Common Procedure
Coding System
ICD11 – International Classification of
Diseases
Kew Medicinal Plant Names (MPNS)
MeSH – Medical Subject Headings
Suspect Cell Lines
Taxogene – the Human Geonome
These products are available
as SaaS
Available =
already built
• Government resources
• Most agencies
• May need formatting
• NASA, DTIC, DOE, NAL, EPA, NLM
etc
• Sign up for updates
• License-able
• Taxobank
• Access Innovations
• others
Why tag / index
at all?
• Disambiguation
• Search and retrieval is accurate
• Promote taxonomy term first
searching
• In the inverted index
search controlled terms
first
• Then go to full text if
needed
• Use in search response
consistency and integrity
• Recommendation engines
using tag sets not vectors
Why Auto Tagging?
• Fast
• Sub-second versus 70 seconds per tag
• Able to add more tags quickly in same sub second time
• More depth
• Always goes to the most specific level of tagging
• No misspellings
• Consistency
• No editorial drift – people tend to use same tags over and over
• Do not need as many subject experts
• Replicable results – no black box
Adding Knowledge bases
• Using your own data
• But not depositing into the big LLM’s
• Send the same query to your own content
• Use the same terms
• Answer will be consistent since it is on tagged actual text
• Keeps your data out of the LLM and secure
• Use the LLM to get a general answer
• Use your content to get the specific and reliable answer
• Combine the two to get a quick summary of the material
Do not need to XML structure, but do need to tag
Problems with Chat systems using LLM
• Flooding of the system
• Irrelevant responses
• Lack of answer precision
• Answer
• Fine tuning the system
• Continuous updates
• Identifying the key points of problems
• Handling multiple target points simultaneously
• More focused approach to handling queries
• How?
• Keywords from the taxonomy
• Applied as an incoming filter
• Added to content responses
• Constant additions based on logs
Supercharge your AI - SSP Industry Breakout Session 2024-v2_1.pdf
Query Parser
Grammer translation
ChatBox
User Query
Client data
LLM System
Algorithms
Training sets
Enriched Custom Data Set
Can Taxonomies
Supercharge
your AI?
• Guiding Decision-Making
• Enhancing Understanding
• Improving Consistency
• Facilitating Interpretability
• Supporting Compliance
YES!!
Supercharge your AI - SSP Industry Breakout Session 2024-v2_1.pdf
Product Descriptions
Knowledge
Domains
Semantic Fingerprinting
Meta-Titles
Content repository
XML Intranet System
Managed Services
Author Disambiguation
Ready to get
started?
• Marjorie M.K Hlava
• Chief Scientist
• Access Innovations, Inc.
• mhlava@accessinn.com
Booth 215
END
Supercharge your AI - SSP Industry Breakout Session 2024-v2_1.pdf
Features Include:
ü Taxonomy and Thesaurus Editor
ü Term Suggestions
ü Subject Classification
ü Entity Extraction
ü Concept Extraction
ü Metadata Enrichment
ü Sentiment Analysis
ü Abstracting and Indexing
ü Text Analyzer and Summarization
ü Inline Tagging for Enhanced Search
ü Semantic Fingerprinting
ü Linked Data Management
Data Harmony is our patented, award winning, Artificial Intelligence Suite that
leverages explainable AI for efficient, innovative and precise semantic discovery of
your new and emerging concepts to help you find the information you need when you
need it.
IMPROVING SEARCH RESULTS BY
OVER 90% AND INCREASING
CUSTOMER PRODUCTIVITY BY 7X
Thesaurus with term records
Supercharge your AI - SSP Industry Breakout Session 2024-v2_1.pdf
A Knowledge Graphs
Does a knowledge graph need a controlled
vocabulary?
Yes
• Consistency
• Interoperability
• Facilitates Search and Discovery
• Semantic Enrichment
• Domain Understanding
Radial graph
and
Hierarchical
display
Both are
taxonomy
displays
https://www.hedden-information.com/taxonomies-vs-ontologies/
Does an ontology need a controlled vocabulary?
Yes
• Build the term / concept records (Objects, Subjects)
• Define the relationships (some of the Predicates)
• Tag the content
• Flow the taxonomy to the ontology
• Add Axioms or Constraints
• Add more Predicates
• Launch the maps and graphs

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Supercharge your AI - SSP Industry Breakout Session 2024-v2_1.pdf

  • 1. Supercharge Your AI Marjorie M.K. Hlava Chief Scientist Access Innovations, Inc. mhlava@accessinn.com www.accessinn.com
  • 2. Marjorie M.K. Hlava • Expert in taxonomies, metadata, their application and data science. • Her groundbreaking work has earned her numerous awards and 2 patents with 21 claims granted • Margie standards work includes • Dublin Core Z39.85, • DOI Syntax Z39.84, , • CrEdit Z39.104, • ThesaurusANSI/NISO Z39.19 Thesauri and other controlled vocabularies • many others. • Currently Convener of the ISO - 25964 the International Standard on Controlled Vocabularies • Founder, Chairman, Chief Scientist of Access Innovations, Inc.
  • 3. ”large language models will not only mirror but magnify any problems with the data sets, problems that many organizations may not realize they have." Amplifying hidden biases and gaps seems like a real danger
  • 4. We have content we want to “slice and dice” to create new derivative products We need to sort 1,000s of journal article/conference session submissions We need web site navigation We have content that people can’t find We need to find peer reviewers We need to personalize conference sessions Departments have different vocabularies, don’t talk to each other, data is siloed and work is duplicated And Now… AI… Large Language Models ChatGPT?
  • 6. Size is here Server Farms Power is a Concern
  • 7. • Data – well enriched is the key to • Ontologies • Search excellence • Knowledge maps • Knowledge graphs
  • 8. • Data is the LLM core asset • Without the data the rest of the initiative is nothing • It is the essential component the strategy • Do enrichment metadata • SUBJECT metadata • Use taxonomies, ontologies, and other models. • The large language models will not only mirror but magnify any problems with the data sets, problems that many organizations may not realize they have. (Gary Carlson - Factors)
  • 9. Technology • It is a tool, not the focus • Might need shiny new piece of technology, • the technology is generally in the chorus • not a main character • Too many companies lead with technology and • do not spend the time understanding their users or aligning their strategy. • Any company that has 1000s of Sharepoint or Teams sites where people still can’t find the information they need knows this. • Most large corps have 5 search software systems • On the shelf • “does not work” • Because the data was not enriched
  • 10. Governance and modeling • Taxonomy and data modeling • essential component of this investment. • Data must be • well sourced, • managed • Consider for ethical and performance reasons • Ignore data quality at your peril • It is hard work – • Does not fit two-week sprint • Get executives to agree on strategy and structure model • Without a coherent model, governance, data pipeline, and resourcing there is no strategic value to an AI initiative
  • 11. Gartner says: “By 2024, companies that use graphs and semantic approaches for natural language technology projects will have 75% less artificial intelligence technical debt than those that do not" How?? • Use existing standards, schemas and ontologies as starting points. • Extract a list of key terms that need to be modeled using data mining/entity extraction/data profiling tools. • Add handcrafted rules, entity attributes and relationships from business glossaries and data dictionaries. • .” [1] Gartner report, “How to Build Knowledge Graphs That Enable AI Driven Enterprise Applications”, 27 September 2022, ID G00768041, Afraz Jaffri.
  • 12. • Does the data need to be structured for AI? • No • Takes in text images, sounds in all formats • Do I need a new platform to offer my content in Ai? • No • How can I ensure searchers will not get hallucinations and wrong answers? • Guide it – tag it • Do I need to protect my content? • Yes • When does this happen in the workflow? • Early as you can
  • 13. What’s the process? • Need a controlled vocabulary • Keywords, taxonomy terms, entity identification • Apply it to your data • Automatically if possible • Use the power of the LLM • Keep your data separate
  • 14. How it trains • Learns from itself – the written resources • Longer it is used – more accurate it becomes • Learns from interactions • Studies the grammar • Analyze the sentence • Order of words • Possible meanings • How they fit together • THEN make a prediction • Continuations one word at a time – dependent clauses • Looks human in response
  • 15. Dump in the data to the AI vortex No work needed Everything will be fine….
  • 18. LLM’s Need a Little Help • To be accurate • To avoid hallucinations • Enhance the data • Tag it with controlled terminology • Add synonyms • Suggest structure
  • 19. “AI” + GenAI • Start with enriched content (tagged) • Tell (feed to) GenAI • GenAI puts new rules in the inference engine • Search results get better • Repeat, repeat, repeat
  • 20. Sounds too easy --- okay here’s more detail • Taxonomies • Available Knowledge domains • Links to • Knowledge graphs • Ontologies • Why the tagging? • Is it expensive / time consuming? • What about protecting my content?
  • 21. What Are The Steps To Implement Knowledge Domains In Generative AI? • Define the Taxonomy Structure • Collect and Preprocess Data • Tag your Data with Taxonomy concepts • Train the Generative AI Model • Incorporate Taxonomy into Model Inference: • Evaluate and Iterate • Deploy and Monitor • Add the SME’s • Collaboration between domain experts, data scientists, and AI engineers is crucial for the success
  • 22. Why a taxonomy? • Matches your content • Scales with the content increases • Extensive synonymy – use any of the word term options • The concept is the unit of thought • Disambiguation • Mercury • Lead • Built in feedback loops to keep current with content • Prevents hallucinations • Misunderstandings of multiple word meanings (Nonsensical output) • Happens when the model is not trained on your content (Factual contradiction) • Query goes against the rules of the system (Prompt contradiction)
  • 23. How Can Knowledge Domains Help LLM? • Understanding Input • Content Organization • Knowledge Representation • Query Expansion • Quality Control • Content Guides
  • 24. Knowledge Domain • Refers to a specific area or field of knowledge • subject matter, concepts, theories, methodologies, and practices. • Cohesive and organized body of knowledge with a scope and boundaries. • Vary widely in size and complexity, • Established disciplines or sub-disciplines, • theories, methods, and research traditions. • Frameworks within specific areas • Scholars, researchers, practitioners, SME’s • Thesaurus with a rule base
  • 25. Knowledge Domains • Taxonomies, thesauri, or authority files • Pre-Built • Knowledge Domains • Full term records • hierarchical, equivalence, and associative relationships, as well as scope notes where appropriate. • Hierarchy alone • NISO Z39.19 and ISO 25964 compliant • Formats, • 22 options, Excel/CSV, 6 flavors of SKOS, HTML, XML, SSL, etc.
  • 26. Applied Science Art Behavioral Science Biological Science Business Chemical – MAI Chem™ Communications Computer Science COVID Economics Educational Curriculum Geography Health and Safety Health Science History Information Science Language Arts Law Linguistics Literature and Drama Mathematics NewsThes Nursing Philosophy Physical Education and Recreation Physical Sciences Political Science Psychology Religion Science Social Sciences General Purpose Taxonomies
  • 27. These products can be SKOS downloads Astronomy Clinical Drugs DTIC – Defense Technical Information Center Environment – GEMET ERIC – Education Resource Information Center JSTOR NASA National Agricultural Library Occupational Safety and Health PLOS
  • 28. CPT – Current Procedural Terminology HCPCS – Healthcare Common Procedure Coding System ICD11 – International Classification of Diseases Kew Medicinal Plant Names (MPNS) MeSH – Medical Subject Headings Suspect Cell Lines Taxogene – the Human Geonome These products are available as SaaS
  • 29. Available = already built • Government resources • Most agencies • May need formatting • NASA, DTIC, DOE, NAL, EPA, NLM etc • Sign up for updates • License-able • Taxobank • Access Innovations • others
  • 30. Why tag / index at all? • Disambiguation • Search and retrieval is accurate • Promote taxonomy term first searching • In the inverted index search controlled terms first • Then go to full text if needed • Use in search response consistency and integrity • Recommendation engines using tag sets not vectors
  • 31. Why Auto Tagging? • Fast • Sub-second versus 70 seconds per tag • Able to add more tags quickly in same sub second time • More depth • Always goes to the most specific level of tagging • No misspellings • Consistency • No editorial drift – people tend to use same tags over and over • Do not need as many subject experts • Replicable results – no black box
  • 32. Adding Knowledge bases • Using your own data • But not depositing into the big LLM’s • Send the same query to your own content • Use the same terms • Answer will be consistent since it is on tagged actual text • Keeps your data out of the LLM and secure • Use the LLM to get a general answer • Use your content to get the specific and reliable answer • Combine the two to get a quick summary of the material Do not need to XML structure, but do need to tag
  • 33. Problems with Chat systems using LLM • Flooding of the system • Irrelevant responses • Lack of answer precision • Answer • Fine tuning the system • Continuous updates • Identifying the key points of problems • Handling multiple target points simultaneously • More focused approach to handling queries • How? • Keywords from the taxonomy • Applied as an incoming filter • Added to content responses • Constant additions based on logs
  • 35. Query Parser Grammer translation ChatBox User Query Client data LLM System Algorithms Training sets Enriched Custom Data Set
  • 36. Can Taxonomies Supercharge your AI? • Guiding Decision-Making • Enhancing Understanding • Improving Consistency • Facilitating Interpretability • Supporting Compliance YES!!
  • 38. Product Descriptions Knowledge Domains Semantic Fingerprinting Meta-Titles Content repository XML Intranet System Managed Services Author Disambiguation
  • 39. Ready to get started? • Marjorie M.K Hlava • Chief Scientist • Access Innovations, Inc. • mhlava@accessinn.com Booth 215
  • 40. END
  • 42. Features Include: ü Taxonomy and Thesaurus Editor ü Term Suggestions ü Subject Classification ü Entity Extraction ü Concept Extraction ü Metadata Enrichment ü Sentiment Analysis ü Abstracting and Indexing ü Text Analyzer and Summarization ü Inline Tagging for Enhanced Search ü Semantic Fingerprinting ü Linked Data Management Data Harmony is our patented, award winning, Artificial Intelligence Suite that leverages explainable AI for efficient, innovative and precise semantic discovery of your new and emerging concepts to help you find the information you need when you need it. IMPROVING SEARCH RESULTS BY OVER 90% AND INCREASING CUSTOMER PRODUCTIVITY BY 7X
  • 46. Does a knowledge graph need a controlled vocabulary? Yes • Consistency • Interoperability • Facilitates Search and Discovery • Semantic Enrichment • Domain Understanding
  • 48. Does an ontology need a controlled vocabulary? Yes • Build the term / concept records (Objects, Subjects) • Define the relationships (some of the Predicates) • Tag the content • Flow the taxonomy to the ontology • Add Axioms or Constraints • Add more Predicates • Launch the maps and graphs