taxonomies: hidden but critical tools marjorie m.k. hlava president access innovations, inc
TRANSCRIPT
Taxonomies:Hidden but Critical Tools
Marjorie M.K. Hlava
President
Access Innovations, Inc.
Industry in change
• Technology changes
• Evolving standards
• Mergers
• New buzzwords
• Hard to tell what is real
Popular Misconceptions• Computers can do it all
• No need to index
• No need for thesauri or subject headings
• Full text gives all we need
• Automatic full text
• User friendly search engines
• Search engines are indexes
• User profiles provide the right context
• Data filters give right answers
Some of it is true
• What can we use?
• Automatic - semi - classification
• Depends…..
• Size of collection
• Cost of the effort
What’s in??• Taxonomies
– thesauri– hierarchies - classification– categorization– browsing
• Wellformedness
• Bricks and mortar, i.e., profit
Options for Access/Control
• Keep track of the input– Thesaurus– Authority file
• Maximize the access– Search engine– Browse list
• Power of the word– McCain
What do we need?
• The basics...
• Authority file– People, places, things
• Taxonomy– Thesaurus* with authority file or document
instance
• “Automatic” Classification
Thesaurus Construction
• Parts of a whole
• Noun and noun phrases
• People, places, things
• Actions and reactions
• Concepts and processes
Term Records -Thesaurus - format
• Main Entries
• Top Terms - TT
• Broader Terms - BT
• Narrower Terms - NT
• Scope Notes - SN
• History - HI
• Date Term - added/changed - DA
Thesaurus - Format
• Related Terms - RT
• See - S
• See Also - SA
• Use - U
• Use For - UF
• “Wellformedness” = W3C
What are the parts?
• Natural Language Processing
• Term forms
• Term Relationships
• Term Associations
Natural Language Processing
• Morphological
• Lexical Analysis
• Syntactic
• Numerical
• Phraseological
• Semantic Analysis
• Pragmatic
Seven Major Parts of NLP
1. Morphological
– plural
– past tense to present
Seven Major Parts of NLP
2. Lexical Analysis– part of speech tagging
3. Syntactic analysis– non phrase id– proper name boundary
Seven Major Parts of NLP
4. Numeric concept boundary
5. Semantic analysis– Proper name concept categorization– Numeric concept categorization– Semantic relation extraction
6. Phraseological - discourse analysis – Text structure identification
Seven Major Parts of NLP
7. Pragmatic analysis– Cause and effect relationships– Nurse and nursing– Common sense reasoning (buy possess)– Who has x ? – These are the people who brought you.....
Say it another way
• Term standardization
• Term forms
• Term relationships
• Term associations
• Rule building / domain creation
Word Standardization
• Split out chemical & drug terms– Separates chemical & drug terms for special treatment
• Split out homonyms, non-English terms, and authority terms
– Separates objects, proper names, place names, and dates for special treatment
• Run spelling standardization program– Identifies variant spellings
Word Standardization
•Run word standardization program
– ie, ing, -ed, -s, es, pre-, non-, and “-”
• Match preferred terms and synonyms
Term Forms
• Noun
• Adjective
• Verb, adverb
• Singular, plural
• Initial articles
• Spelling variants
Term Forms
• Punctuation
• Capitalization
• Abbreviations
Term Relationships
• Generic
• Hierarchical
• Systematic
• Alphabetic
• Instance
• Poly-hierarchical
Term Associations
• Cross references
• All and some rule
• Associative terms
• Related terms
“Rule building”* process
• Put terms in context
• Group like categories
• Consider relationships
• Standardize variants
• Meld to a single concept rule
• How much is really automatic???
Domains
• Taxonomy
• Term Record - thesaurus
• Hierarchical Browse-able list
• Handout in Booth 150
What else can we have?
• Proximity
• Stemming (lemmatization)
• Truncation
• Statistical clustering
• Bayesian and others
Other terms and tools
• Neural networks
• Word normalization
• Lexical (word) networks
• Distance mapping
• Pattern recognition
Moving toward the search engines
• Term weighting
• Frequency counts
• Relevance
• Precision
• Recall
Classification of
• Evolving model…
• Noun Extractors
• Rule Based Systems
• Semantic Processors
• Fuzzy Search Systems
• Filtering Systems
“Automatic Classification Systems”
(Semi) Automatic Indexing
• Basic theories
• Thesaurus construction
• Natural language processing
• Domain specific
Noun extractors• Noun Extractors
• Use stop word list and frequency counts– Semio – Word Perfect 5.0– Recon
• Prebuilt domains– Autonomy– Net Owl– Newsindexer
Rules Based Systems
• Rule Based– Data Harmony– API– DTIC– Mapit
Semantic Processors
• Synth Bank
• n-Stein - expected
• Quiver - beta
Fuzzy Search Systems
• Dr. Link
• Sovereign Hill
Filtering Systems
• Screaming Media
• Data Harmony
New Directions• Topic Maps - TAO
– Topic– Associations– Occurrences
• Relational Indexing
• Index Visualization
• Based on term records
• Add the search engines….
What’s a user to do?
• Enjoy the presentation
• What about a database producer?– Look the options,– Build from the basics– Evaluate the new tools– See it work before you buy
• Give me your card I will email the presentation tonight
Thank You
• Marjorie M.K. Hlava
• President, Access Innovations, Inc.
• www.accessinn.com
• Chairman, Data Harmony
• 505-998-0800
• Booth 150