Transcript GOMS Analysis & Web Site Usability

The LINDI Project
Linking Information for New Discoveries
Two Main Thrusts:
Statistical language
analysis techniques
for extracting
propositions
UIs for building and
reusing hypothesis
seeking strategies.
LINDI: Target Components
1.
2.
3.
4.
Special UI for retrieving appropriate
docs
Language analysis on docs to detect
causal relationships between concepts
Probabilistic representation of concepts
and relationships
UI + User: Hypothesis creation
Design Goals of LINDI UI
Support for the development of
extended search strategies
1. Text filtering and manipulation tool to
help the development of strategies
2. Text visualization and analysis tool to
help the formulation of hypotheses
The User Interface

A general search interface should support
–
–
–
–
–
–


History
Context
Comparison
Operators: Intersection, Union, Slicing
Operator Reuse
Visualization (where appropriate)
We have an initial implementation
It needs lots of work
Scenario:
Explore Functions of a Gene

Objective
– Determine the functions of a newly sequenced
Gene X.

Known facts
– Gene X co-expresses (activated in the same
cell) with Gene A, B, C
– The relationship of Gene A, B, C with certain
types of diseases (from medical literature)

Question
– What types of diseases are Gene X related to?
Explore Functions of New Gene X
Medical Literature
Possible Function
For Gene-X
Query
Query
Gene-A
Gene-B
Gene-C
Keywords
Keywords
Keywords
Projection
Keywords
Intersection
Keywords
Slicing
Mapping
Keywords
Slide adapted from K. Patel
Explore Functions of New Gene X
Medical Literature
Possible Function
For Gene-X
Query
Query
Gene-A
Gene-B
Gene-C
Keywords
Keywords
Keywords
Projection
Keywords
Intersection
Keywords
Slicing
Mapping
Keywords
Slide adapted from K. Patel
Architecture of LINDI UI
Data Layer
 Annotation Layer
 User Interface Layer

Data Layer

Purpose
– Hide different formats of text collections

Components
– Data: Abstractions representing records of a
text collection
– Operations: performed on the data

Data
– A set of records
– Each record is a set of tuples with types

Operations
– union, intersection, projection, mapping
Annotation Layer

Purpose
– Associate data set with operations that
produced them (history)
– History is a first class object

Advantage
– Streamline a sequence of operations
– Reuse operations
– Parameterize operations
User Interface

This version completed Aug 10, 2000
– Designed by Marti Hearst and Hao Chen
– Code written by Hao Chen

Direct manipulation of information objects
and access operations
–
–
–
–
–



Query
Intersection
Union
Mapping
Slicing
Record and reuse of past operations
Parameterization of operations
Streamlining of operations
Initial Palette
Query Structure Determined
by Collection Type
Query Operation Results
Projection Operation and
Subsequent Results
GA
GB
GC
Parameterized Query:
Repeat operations with different values
Intersection over Projected Attribute
Intersection over Projected Attribute
Example Interaction with UI Prototype
1 Query on Gene names
2 Project out only mesh headings
3 Intersect the results
4 Map to create a ranking
5 Slice out the top-ranked.
Second Version of UI
LINDI Miner
 Circa May 2002

– Designed by Marti Hearst
– Implemented by Melody Ivory
Emphasize reusing results of prior
text analysis
 See lindi-miner.ppt

The Language Analysis
Component
Goal:
Extract Propositions from Text
and Make Inferences
 Why Extract Propositions from Text?

– Text is how knowledge at the
propositional level is communicated
– Text is continually being created and
updated by the outside world
Example: Etiology

Given
– medical titles and abstracts
– a problem (incurable rare disease)
– some medical expertise

find causal links among titles
– symptoms
– drugs
– results
Traditional Semantic
Grammars

Example (Burton & Brown 79)
– Interpreting “What is the current thru the
CC when the VC is 1.0?”
<request> := <simple/request> when <setting/change>
<simple/request> := what is <measurement>
<measurement> := <meas/quant> <prep> <part>
<setting/change> := <control> is <control/value>
<control> := VC
– Resulting semantic form is:
(RESETCONTROL (STQ VC 1.0) (MEASURE CURRENT CC))
Example:
Statistical Semantic Grammar

To detect causal relationships
between medical concepts
– Title:
Magnesium deficiency implicated in increased stress
levels.
– Interpretation:
<nutrient><reduction> related-to
<increase><symptom>
– Inference:
» Increase(stress, decrease(mg))
Statistical Semantic Grammars

Empirical NLP has made great strides
– But mainly applied to syntactic structure

Semantic grammars are powerful, but
– Brittle
– Time-consuming to construct

Idea:
– Use what we now know about statistical
NLP to build up a probabilistic grammar