878s991

LBSC 878, Spring 2005, Week 1, Doug Oard

Overview

Course Objectives and Approach

Thinking broadly about “ISAR”

Some terminology

Data: Basic elements of meaning (assertions)
Information: Assertions together with the context needed for their interpretation
Knowledge: A basis for making decisions (Wilson’s "practical relevance")
Wisdom: A basis for guiding decisions

Defining IR (Blair)

Both Information Retrieval and Database Retrieval use queries to obtain information
Database Retrieval retrieves data and combines it to produce information.

The query provides the context needed for interpretation of the retrieval result

Information Retrieval retrieves objects (e.g., documents) that contain information

Some examples of Information Retrieval (IR) applications

Find something (an academic paper, an email I wrote two years ago, some class notes, ...) in a collection of written text, in any natural language, in any format (ASCII text, word processor files, scanned images of typeset pages, scanned images of handwritten manuscripts, ...)
Find something (an object, the work of a particular artist, a street address, ...) in a collection of still images of some type (photographs, blueprints, oil paintings, maps, ...)
Find something (spoken words, singing, instrumental music, ...) in a collection of recorded audio
Find something (a person, the depiction of some event, ...) in a collection of video (video tape, motion picture film, ...) that may or may not also contain synchronized audio and text.
Find someone (coworker, consultant, speaker, ...) with the expertise that is required to help you accomplish some goal.
Sift through a large stream of continuously generated materials (newswire stories, electronic mail, television programs, telephone calls, ...) to find something worthy of your attention
Explore a large collection of materials (documents, images, audio, video, ...) to identify some useful information (broad trends, new discoveries, unexpected events, ...)

Why is IR hard? (Blair)

An indexer must try to guess which terms every searcher will use to look for each document
A searcher must guess which terms the indexer chose
For full text retrieval, the searcher must guess which terms the author chose
Individual variability makes it impossible to do any of this perfectly
Retrieval effectiveness is thus a matter of degree, rather than an absolute
Effective retrieval thus becomes an iterative process
Techniques are known to help the user choose good search terms (Croft’s "magic")

But they tend to make the system less predictable, so it becomes harder to iterate

With well designed systems, it is often possible to eventually find useful documents

Desiderata (Croft, roughly in order)

Meta-questions

Focus Area Selection