Download Lecture slides [pptx] - UNC School of Information and Library Science

INFORMATION RETRIEVAL
data…
f r a m e w o r k fo r t o d ay ’ s l e c t u r e …
data
organizing
data
retrieving
data
tools
supporting
the process
Structured Data
Unstructured data
•information with a
high degree of
organization
•easy to put into a
relational database
•search is simple
and straightforward
•essentially the
opposite of
structured data
•natural language /
free text
STRUCTURED VS UNSTRUCTURED DATA
easy to envision structured data in terms of “tables”
Employee
Manager
Salary
Smith
Jones
50000
Chang
Smith
60000
Ivy
Smith
50000
Typically allows numerical range and exact match (for text)
queries, e.g., Salary < 60000 AND Manager = Smith.
4
Relational
Databases
•Structured data
•Designed to provide
search results with exact
answers
•Queries built on schema
of structured fields
•Lack of ranking
mechanism (initially)
•We know the schema in
advance, so semantic
correlation between
queries and data is clear
•We can get exact answers
Information Retrieval
Systems
tables in a MS Access
relational database –
defines each defining a
social networking site
Data entry form in a
MS Access relational
database – create each
record
Structured Data
Unstructured data
•information with a
high degree of
organization
•easy to put into a
relational database
•search is simple
and straightforward
•essentially the
opposite of
structured data
•natural language /
free text
structured vs UNSTRUCTURED data
typically refers to free text
email is a good example of unstructured data. it's indexed by date, time, sender, recipient, and
subject, but the body of an email remains unstructured
other examples of unstructured data include books, documents, medical records, and social
media posts
magazine article is an
example of
unstructured data
Relational
Databases
Information Retrieval
Systems
•Unstructured / semistructured data
•Designed to support
unstructured natural
language full text search
•Ranking mechanism is
very important – results
must be sorted by
relevance in order to
satisfy user’s
information need
•We get inexact,
estimated answers
Query
Representation
function
Matching
function
Document collection
(corpus)
Representation
function
Index
CATEGORIES
SUBJECT HEADINGS
Results
KWIC
Key word in context
KWIC
Key word in context
metadata
metadata
WHAT IS METADATA?
Classic definition: data about data
Metadata is structured information that describes, explains, locates, or otherwise
makes it easier to retrieve, use, or manage an information resource. (NISO)
3 primary “types”:
 Descriptive
 Structural
 Administrative (rights
management, preservation)
More Metadata: A Cataloging Record
http://search.lib.unc.edu/search?R=UNC
b4448196
THE IDEA OF FACETS
Facets are a way of labeling data
 A kind of Metadata (data about data)
 Can be thought of as properties of items
Facets vs. Categories
 Items are placed INTO a category system
 Multiple facet labels are ASSIGNED TO items
Facets Epicurious example http://www.epicurious.com/
Create INDEPENDENT categories (facets)
 Each facet has labels (sometimes arranged in a hierarchy)
Assign labels from the facets to every item
 Example: recipe collection
Ingredient
Cooking
Method
Chicken
Stir-fry
Bell Pepper
Curry
Course
Cuisine
Main Course
Thai
THE IDEA OF FACETS
Break out all the important concepts into their own facets
Sometimes the facets are hierarchical
 Assign labels to items from any level of the hierarchy
Preparation Method
Fry
Saute
Boil
Bake
Broil
Freeze
Desserts
Cakes
Cookies
Dairy
Ice Cream
Sorbet
Flan
Fruits
Cherries
Berries
Blueberries
Strawberries
Bananas
Pineapple
USING FACETS
Now there are multiple ways to get to each item
Preparation Method
Fry
Saute
Boil
Bake
Broil
Freeze
Fruit > Pineapple
Dessert > Cake
Preparation > Bake
Desserts
Cakes
Cookies
Dairy
Ice Cream
Sherbet
Flan
Fruits
Cherries
Berries
Blueberries
Strawberries
Bananas
Pineapple
Dessert > Dairy > Sherbet
Fruit > Berries > Strawberries
Preparation > Freeze
UNC Libraries Online Catalog
http://www.lib.unc.edu/
Let’s look at a database of magazine & journal articles…
…Academic Search Complete
>> UNC Libraries Homepage: http://www.lib.unc.edu/
>> E-Research Tools
>> Frequently Used
>> Academic Search Complete
[off-campus log in with onyen/password
ORGANIZATION / SEARCH
We organize to enable retrieval
The more effort we put into organizing information, the more
effectively it can be retrieved
The more effort we put into retrieving information, the less it
needs to be organized first
We need to think in terms of investment, allocation of costs
and benefits between the organizer and retriever
The allocation differs according to the relationship between
them; who does the work and who gets the benefit?