Download presentation
Presentation is loading. Please wait.
Published byClement Willis Modified over 9 years ago
1
Taxonomies: Hidden but Critical Tools Marjorie M.K. Hlava President Access Innovations, Inc.
2
Industry in change Technology changes Evolving standards Mergers New buzzwords Hard to tell what is real
3
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
4
Some of it is true What can we use? Automatic - semi - classification Depends….. Size of collection Cost of the effort
5
What’s in?? Taxonomies –thesauri –hierarchies - classification –categorization –browsing Wellformedness Bricks and mortar, i.e., profit
6
Options for Access/Control Keep track of the input –Thesaurus –Authority file Maximize the access –Search engine –Browse list Power of the word –McCain
7
What do we need? The basics... Authority file –People, places, things Taxonomy –Thesaurus* with authority file or document instance “Automatic” Classification
8
Thesaurus Construction Parts of a whole Noun and noun phrases People, places, things Actions and reactions Concepts and processes
9
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
10
Thesaurus - Format Related Terms - RT See - S See Also - SA Use - U Use For - UF “Wellformedness” = W3C
11
What are the parts? Natural Language Processing Term forms Term Relationships Term Associations
12
Natural Language Processing Morphological Lexical Analysis Syntactic Numerical Phraseological Semantic Analysis Pragmatic
13
Seven Major Parts of NLP 1. Morphological – plural – past tense to present
14
Seven Major Parts of NLP 2. Lexical Analysis – part of speech tagging 3. Syntactic analysis – non phrase id –proper name boundary
15
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
16
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.....
17
Say it another way Term standardization Term forms Term relationships Term associations Rule building / domain creation
18
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
19
Word Standardization Run word standardization program – ie, ing, -ed, -s, es, pre-, non-, and “-” Match preferred terms and synonyms
20
Term Forms Noun Adjective Verb, adverb Singular, plural Initial articles Spelling variants
21
Term Forms Punctuation Capitalization Abbreviations
22
Term Relationships Generic Hierarchical Systematic Alphabetic Instance Poly-hierarchical
23
Term Associations Cross references All and some rule Associative terms Related terms
24
“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???
25
Domains Taxonomy Term Record - thesaurus Hierarchical Browse-able list Handout in Booth 150
26
What else can we have? Proximity Stemming (lemmatization) Truncation Statistical clustering Bayesian and others
27
Other terms and tools Neural networks Word normalization Lexical (word) networks Distance mapping Pattern recognition
28
Moving toward the search engines Term weighting Frequency counts Relevance Precision Recall
29
Classification of Evolving model… Noun Extractors Rule Based Systems Semantic Processors Fuzzy Search Systems Filtering Systems “Automatic Classification Systems”
30
(Semi) Automatic Indexing Basic theories Thesaurus construction Natural language processing Domain specific
31
Noun extractors Noun Extractors Use stop word list and frequency counts –Semio –Word Perfect 5.0 –Recon Prebuilt domains –Autonomy –Net Owl –Newsindexer
32
Rules Based Systems Rule Based –Data Harmony –API –DTIC –Mapit
33
Semantic Processors Synth Bank n-Stein - expected Quiver - beta
34
Fuzzy Search Systems Dr. Link Sovereign Hill
35
Filtering Systems Screaming Media Data Harmony
36
New Directions Topic Maps - TAO –Topic –Associations –Occurrences Relational Indexing Index Visualization Based on term records Add the search engines….
37
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
38
Give me your card I will email the presentation tonight
39
Thank You Marjorie M.K. Hlava President, Access Innovations, Inc. www.accessinn.com Chairman, Data Harmony mhlava@accessinn.com 505-998-0800 Booth 150
Similar presentations
© 2025 SlidePlayer.com. Inc.
All rights reserved.