Taxonomies in Search

Taxonomies in SearchAn SLA Webinar
Aug 10, 1:00pm-2:00pm EST
Marjorie Hlava, President
mhlava@accessinn.com
Access Innovations, Inc.
www.accessinn.com
Leveraging your content semantically

Agenda
How search works
Measuring accuracy in search
Precision
Recall
Relevance
Search theoretical basis
Bayes, Boole and the rest of the guys
The taxonomy effect

How does search work?
Many parts
Search software – of course
Computer network
Parsing of text
Well formed or structured text
CLEAN DATA
Computer software – network
Computer hardware
Telecommunications connection
Training sets for statistical systems

Technical parts of search
Search technology
Ranking algorithms
Query language
Federators
Cache
Inverted index
Other enhancements
Presentation Layer

My Main Frustration
Select hardware
Select software
Design system
Try to load the data
Add the taxonomy
That’s BACKWARDS

Data First!
What are you building the system for?
Assess the data
Do the design
Decide what else needs to be added
Taxonomy terms
Other controls
Find a system that will work with your data

Access Innovations – Complex FarmWith Perfect Search
Query
Federators
Query Servers
Search Harmony Presentation
Layer
Deploy
Hub
Index
Builders
Cleanup, etc.
Repository XIS (cache)
Cache
Builders
Source
Data

CUSTOM
CONNECTOR
EMAIL
CONNECTOR
DATABASE
CONNECTOR
FILE
TRAVERSER
WEB
CRAWLER
MANAGEMENT API
QUERY API
CONTENT API
Data Harmony Governance API
SEARCH
SERVER
FILTERSERVER
FAST Search example
Core Architectural Components
Administrator’s
Dashboard
Web
Content
Vertical
Applications
Pipeline
Query
Pipeline
Files,
Documents
QUERY
PROCESSOR
Portals
Index DB
Databases
DOCUMENT
PROCESSOR
Results
Custom
Front-Ends
Alerts
Email,
Groupware
Search harmony
Mobile
Devices
Custom
Applications
Content
Push
MAIstro
Agent DB

Relevance
Recall
Precision
Accuracy – Hits, miss, noise
Ranking
Linguistics
Query Processing
Results Processing
Display
Search refinement
Usability
Business Rules
9

Relevance
How well a set of returned documents answers the information need
“Accuracy”
Related to objective of search
Different user communities
Information resources
Tension of user needs and context available
A confidence “guessimate”
10

The formulas
Recall = Number of relevant items retrieved
Number of relevant items in the collection
Precision = Number of relevant items retrieved
Number of items retrieved
Relevance = Germane (Precision)
Pertinent (Recall)

Measuring Relevance
Concepts
Context
Age of documents
Completeness (recall)
Quality
Statistically determined ?
Nope, it is subjective
Someone has to determine the rightness of the item
A confidence factor = canard!

Kinds of search
Bayesian –
FAST
Lucene
Autonomy / Verity
Boolean
Dialog
Endeca
Perfect Search
Ranking algorithms
Google
13

Search Theoretical BasisThose Famous Guys
Boole
Bayes
Bayesian Techniques
Turney
Turney algorithm
Enriched structured data
Marco Dorigo
Ant Colony
This is only a sample
of a large body of research

George Boole and Boolean algebra
George Boole
Mathematician
1815-1864
Boolean algebra
An algebraic system of logic
AND, OR, NOT, ANDNOT,
Dialog, BRS, Stairs
15

Boolean representation
Venn diagram showing the intersection of sets A AND B (in violet),
The union of sets A OR B (all the colored regions),
And set A XOR B (all the colored regions except the violet).
The "universe" is represented by the rectangular frame.
16

Bayes and Bayes’ Theorem
Thomas Bayes
Mathematician
1702 - 1761
Bayesian theorem
Uses probability inductively
Established a mathematical basis for probability inference
WHAT?
A means of calculating,
from the number of times an event has not occurred,
the probability that it will occur in future trials
17

Bayesian methods - Cautions
A user might wish to change the distribution of probabilities.
A user will make a novel request for information in a previously unanticipated way.
The computational difficulty of exploring a previously unknown network.
The quality and extent of the prior beliefs used in Bayesian inference processing.

Bayesian cautions (cont.)
A Bayesian network is only as useful as the prior knowledge is reliable.
An optimistic or pessimistic expectation of the quality of these prior beliefs will distort the entire network and invalidate the results.
Must ensure the selection of the statistical distribution induced in modeling the data.
Must have the proper distribution model to describe the data.
That is you have to constantly train and retrain the data

Peter Turney and the Turney Algorithm
Peter D. Turney, Canada, present
Learning algorithms for keyphraseextraction
Tree Induction Algorithm
Lexical Semantics
GenEx – with human input
80% acceptable
Extraction vs. generation and sentiment of words
(hits(word AND "excellent") hits (poor))log2 ---------------------------------------- (hits(word AND "poor") hits (excellent))

Marco Dorigo and Ant Colony Optimization
Marco Dorigo
Research director for the Belgian Fonds de la RechercheScientifique
Research director of the IRIDIA lab at the UniversitéLibre de Bruxelles
Ant Colony Optimization
metaheuristicfor combinatorial optimization problems
Swarm intelligence
Value importance vs. heuristic importance
Useful in search prediction
21

Natural Language Processing
Syntactic
Semantic
Morphological
Phraseological
Lemmatization (stemming)
Statistical
Grammatical
Common Sense

Basic areas of Automatic Language Processing (ALP)
Auto Translation
Auto Indexing
Auto Abstracting
Artificial Intelligence
Searching
Spell Checking
Semantic Web
Natural Language Processes (NLP)
Computational Linguistics

Statistical Search
Cluster analysis
Neural networks
Co-occurrence
Bayesian inference
Latent Semantic
Etc.
24

Inverted Files and Boolean
are basic to all search
Searchable Index
Inverted
File
Index
Taxonomy
Thesaurus
Hierarchical Display

Sample Slide for Inverted File Index Demonstration
Outline of Presentation
Define key terminology
Thesaurus tools
Features
Functions
Costs
Thesaurus construction
Thesaurus tools
Why & when?

Simple Inverted File Index
key
of
outline
presentation
terminology
thesaurus
tools
when
why
&
1
2
3
4
construction
costs
define
features
functions

Complex Inverted File Index
Example 1
key - L2, P2, H
of - Stop
outline - L1, P1, T
presentation - L1, P3, T
terminology - L2, P3, H
thesaurus - (1) - L3, P1, H
(2) - L7, P1, SH
(3) - L8, P1, SH
tools - (1) - L3, P2, H
(2) - L8, P2, SH
when - L9, P3, H
why - L9, P1, H
& - Stop
1 - Stop
2 - Stop
3 - Stop
4 - Stop
construction - L7, P2, SH
costs - L6, P1, H
define - L2, P1, H
features - L4, P1, SH
functions - L5, P1, SH

Word and Term Parsing
Stemming
-ing, -ed, -es, -’s, -s’, etc.
Depluralization
Truncation
Left and right
Wild cards
Organi*ation
Variant Spellings
Centre, center
Hyphens

The taxonomy effect
Where do the terms go?
How are they used in search
What other ways can I use the taxonomy in search?

Site search
Search of 53 crawled sites including journals, books, web site, conference sites, etc.
Navigation
Bookstore search
Search database for Journals and pubs
For search all publications

Navigate the full taxonomy “tree”
BROWSE
Auto-completion using the taxonomy
Guide the user
Taxonomy Driven Search Presentation

A quick look behind the scenes
Database
Management
System
Search thesaurus
Validate term entry
Block invalid terms
Record candidates
Establish rules for
term use
Suggest indexing
terms
Thesaurus
tool
Indexing
tool
Validate terms
Add terms and rules
Change terms and rules
Delete terms and rules

Thesaurus
Term Record
view
Taxonomy
view

Where does the subject metadata go?
Apply to content itself
Use meta name field in HTML header
Connect search to the keywords in the SQL or other database tables

HTML Header

RDBMS Connection
Taxonomy term table

Suggested taxonomy descriptors

Integrate taxonomy to enhance findability
Browsable categories of a directory
Browsable faceted navigation
Smart search for term equivalents
Taxonomy terms (original or modified) as labels
Navigation aids incorporate taxonomy terms and relationships

More Taxonomy Enrichment
Spelling alternatives and correction
Related concepts
Statistical information about the metadata
Navigation or drill downs
Search refinement
Recursive sets
Concept linking
Dictionary lookup (in taxonomy glossary)

Brand is repeated in several spots and tied to search as well

Raw Full text data feeds
Data Base Plus Search Workflow
XIS Creation
SQL for ecommerce
Printed source materials
Add metadata
Data Crawls on 53+ sources
XIS repository
Taxonomy terms
Load to
Perfect Search
MAI Concept Extractor
Taxonomy Thesaurus Master
MAI Rule Base
Search Harmony Display Search
Save data to search and repositories at the same time

Raw Full text data feeds
Data Base Plus Search Workflow
XIS Creation
SQL for ecommerce
Printed source materials
XIS repository
Data Crawls on data sources
Add metadata
Load to
Search
MAI Concept Extractor
MAI Rule Base
Search Harmony Display Search
Taxonomy Thesaurus Master
Source data
Taxonomy terms
Search data
Clean and enhance data

Client Data
Full Text
HTML, PDF,
Data Feeds, etc.
Taxonomy In Sharepoint
Automatic Summarization
Search
Presentation:90% accuracy
Browse by Subject
Auto-completion
Broader Terms
Narrower Terms
Related Terms
Machine Aided Indexer (M.A.I.™)
Repository
Search
Software
Inline Tagging
Client taxonomy
Client Taxonomy
Metadata and Entity Extractor
Thesaurus Master

What we covered
How search works
Search theoretical basis
Bayes, Boole and the rest of the guys
The taxonomy effect

Do the data FIRST
What do you have?
What does it need?
How would you LIKE to access it?
Look at the data BEFORE you create the specifications
DTD built without data is not going to work
Then choose the system that will support your data

Next Month
Same time, same station
Solving the Challenge of Connecting People and Author NetworksJay Ven Eman, Ph.D.September 14As online digital publishing continues to grow, taxonomies can be increasingly useful in connecting people with author networks through directory creation with author disambiguation and subject metadata tagging to increase the usefulness of information for researchers and community-building.

About Access Innovations
49
Access innovations are experts in content creation, enrichment and conversion services. We provide services to semantically enrich and tag and raw text into highly structured data. We deliver clean ,well formed, metadata enriched ,data so our clients can reuse repurpose, store, and find their knowledge assets. We go beyond the standards to build taxonomies and other data control structures as a solid foundation for data.
Our services and software allow organizations to use and present their information to both internal and external constituents by leveraging search, presentation, e-commerce . We change search to found!
Quick Facts
Founded in 1978
Headquartered in Albuquerque
Privately held
Delivered more than 2000 engagements

Thank you for your attention!
Slides will be available on SLA Taxonomy Division and Access Web sites tomorrow
Taxonomies in Search: http://www.accessinn.com/library/presentations/sla-taxonomies-in-search-aug10-2011.pptx
Marjorie M. K. Hlava
Access Innovations / Data Harmony
mhlava@accessinn.com
+505.998.0800

Taxonomies in Search

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