Evidence from Metadata
LBSC 796/CMSC 828o
Session 6 – March 1, 2004
Douglas W. Oard
Agenda
•	Questions

•	Controlled vocabulary retrieval

•	Generating metadata

•	Metadata standards

•	Putting the pieces together
Supporting the Search Process
Problems with “Free Text” Search
•	Homonymy
–	Terms may have many unrelated meanings
–	Polysemy (related meanings) is less of a problem

•	Synonymy
–	Many ways of saying (nearly) the same thing

•	Anaphora
–	Alternate ways of referring to the same thing
Behavior Helps, But not Enough
•	Privacy limits access to observations

•	Queries based on behavior are hard to craft
–	Explicit queries are rarely used
–	Query by example requires behavior history

•	“Cold start” problem limits applicability
A “Solution:” Concept Retrieval
•	Develop a concept inventory
–	Uniquely identify concepts using “descriptors”
–	Concept labels form a “controlled vocabulary”
–	Organize concepts using a “thesaurus”

•	Assign concept descriptors to documents
–	Known as “indexing”

•	Craft queries using the controlled vocabulary
Two Ways of Searching
Boolean Search Example
•	Canine AND Fox
–	Doc 1
•	Canine AND Political action
–	Empty
•	Canine OR Political action
–	Doc 1, Doc 2
Applications
•	When implied concepts must be captured
–	Political action, volunteerism, …
•	When terminology selection is impractical
–	Searching foreign language materials
•	When no words are present
–	Photos w/o captions, videos w/o transcripts, …
•	When user needs are easily anticipated
–	Weather reports, yellow pages, …
Yahoo
Text Categorization
•	Goal: fully automatic descriptor assignment

•	Machine learning approach
–	Assign descriptors manually for a “training set”
–	Design a learning algorithm find and use patterns
•	Bayesian classifier, neural network, genetic algorithm, …
–	Present new documents
•	System assigns descriptors like those in training set

Supervised Learning
Example: kNN Classifier
Machine Assisted Indexing
•	Goal:  Automatically suggest descriptors
–	Better consistency with lower cost

•	Approach rule-based expert system
–	Design thesaurus by hand in the usual way
–	Design an expert system to process text
•	String matching, proximity operators, …
–	Write rules for each thesaurus/collection/language
–	Try it out and fine tune the rules by hand
 Machine Assisted Indexing Example
Thesaurus Design
•	Thesaurus must match the document collection
–	Literary warrant

•	Thesaurus must match the information needs
–	User-centered indexing

•	Thesaurus can help to guide the searcher
–	Broader term (“is-a”), narrower term, used for, …

Challenges
•	Changing concept inventories
–	Literary warrant and user needs are hard to predict

•	Accurate concept indexing is expensive
–	Machines are inaccurate, humans are inconsistent

•	Users and indexers may think differently
–	Diverse user populations add to the complexity

•	Using thesauri effectively requires training
–	Meta-knowledge and thesaurus-specific expertise
Named Entity Tagging
•	Machine learning techniques can find:
–	Location
–	Extent
–	Type

•	Two types of features are useful
–	Orthography
•	e.g., Paired or non-initial capitalization
–	Trigger words
•	e.g., Mr., Professor, said, …

Normalization
•	Variant forms of names (“name authority”)
–	Pseudonyms, partial names, citation styles

•	Acronyms and abbreviations
–	Organizations, political entities, projects, …

•	Co-reference resolution
–	References to roles or objects rather than names
–	Anaphoric pronouns for an antecedent name
Example: Bibliographic References
Types of Metadata Standards
•	What can we describe?
–	Dublin Core

•	How can we convey it?
–	Resource Description Framework (RDF)

•	What can we say?
–	LCSH, MeSH, …

•	What does it mean?
–	Semantic Web
Dublin Core
•	Goals: 
–	Easily understood, implemented and used
–	Broadly applicable to many applications
•	Approach:
–	Intersect several  standards (e.g., MARC)
–	Suggest only “best practices” for element content
•	Implementation:
–	16 optional and repeatable “elements”
•	Refined using a growing set of “qualifiers”
–	“Best practice” suggestions for content standards
Dublin Core Elements
Content
•	Title
•	Subject
•	Description
•	Type
•	Audience
•	Coverage
•	Related resource
•	Rights
Instantiation
•	Date
•	Format
•	Language
•	Identifier

Responsibility
•	Creator
•	Contributor
•	Source
•	Publisher
Resource Description Framework
•	XML schema for describing resources

•	Can integrate multiple metadata standards 
–	Dublin Core, P3P, PICS, vCARD, …

•	Dublin Core provides a XML “namespace”
–	DC Elements are XML “properties
•	DC Refinements are RDF “subproperties”
–	Values are XML “content”
A Rose By Any Other Name …
Semantic Web
•	RDF provides the schema for interchange

•	Ontologies support automated inference
–	Similar to thesauri supporting human reasoning

•	Ontology mapping permits distributed creation
–	This is where the magic happens 

Adversarial IR
•	Search is user-controlled suppression
–	Everything is known to the search system
–	Goal: avoid showing things the user doesn’t want

•	Other stakeholders have different goals
–	Authors risk little by wasting your time
–	Marketers hope for serendipitous interest

•	Metadata from trusted sources is more reliable
Index Spam
•	Goal: Manipulate rankings of an IR system

•	Multiple strategies:
–	Create bogus user-assigned metadata
–	Add invisible text (font in background color, …)
–	Alter your text to include desired query terms
–	“Link exchanges” create links to your page
Putting It All Together
Before You Go!
   On a sheet of paper, please briefly answer the following question (no names):

   What was the muddiest point in today’s lecture?