Download Visualization of Relational Text Information

Visualization of Relational Text
Information
for Biomedical Knowledge
Discovery
James W. Cooper
IBM T J Watson Research Center
Hawthorne, NY
Overview
Prior work
 Java based text mining
 Computation of unnamed relations
 Graphical display of relations
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Relations between terms
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Noun phrase co-occurrence statistics [Roark,
Charniak]
Choose seed words and look for terms near them.
[Brin] [Gravano, Agichtein]
– Repeat
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Biomedical domain
– Blaschke used dictionary of common verbs
– Pustejovsky found inhibit relations
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Stevens, Palakal, Mostafa
– Detected abstract-wide co-occurrence using dictionary
of genes and useful verbs.
Graphical Displays
Biolayout – protein similarity
 ProtInAct – interactive system using yFiles
 Zhang – interactive 3D system
 Jenssen – gene network
 Leroy – GeneScene
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BioLayout –Enright and Ouzounis
Five related protein families and their
corresponding relationships.
Spheres represent proteins and lines
represent protein similarities.
ProInAct- Spencer and Bennett
Proteins clustered by functional interaction
Zhang-Protein interaction mapping
Jenssen – A literature network
Lines connect genes that have co-occurred in 1 or more papers.
Leroy –GeneScene
What would we like to do?
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Find scientifically meaningful connections
between important terms.
– Such as Swanson’s Reynaud’s disease – fish
oil connection.
Allow exploration of relations by user.
 Filter the relations by ontology or term
types
 Perform path analysis
 Let the user vary the graphical display.
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Data we analyzed
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Two sets of patent data
– 584 patents on Viagra and phosphodiesterase
inhibitors.
– 1514 patents on quinolones (like Cipro)
Recognized major technical terms in each
patent.
 Filtered organic chemical nomenclature.
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The Talent text mining system
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Text Analysis and Language Engineering
Tools
– Finds multiword noun phrases
– Does shallow parse
– Can extract NPs and VGs
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As well as all other sentence parts
The JTalent Library
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Java class library with JNI interface
– To Talent DLL
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Creates database load files of terms
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Paragraph
Sentence
Offset
Term type (NP, VG)
TalentShow Demo
The KSS Library
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Java class library of functions for
– Accessing a database (DB2, Access)
– Manipulating a search engine
– Manipulating tables of information created by
JTalent.
Database Tables
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Documents
– Title, author, URL, ID
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TermDocs
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Term
Paragraph
Sentence
Offset
Type
Dictionary of terms, types and IDs
– Such as MeSH
Computing term information
Compute unique terms from Termdocs
 Compute frequency
 Compute salience
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– Based on frequency
– Number of docs they appear in more than
once
Compute term relations
Named relations based on abbreviation
expansions.
 Unnamed relations based on proximity,
with weight based on how frequently they
occur near each other.
 Mutual information weight:
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 totalterms  paircount 

m  log 
freq1  freq2
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
Tuning Computed relations
Select only terms above a salience
threshold.
 Only relations in which one or both are
members of an ontology.
 Store relations in a database table for rapid
access:
 Term | weight | term
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Original System
Visual client
 SOAP server
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– Queries database to get relations
– Round trip for each new query
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Instead, we export the data for the user to
visualize as they wish.
Exporting relations
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Save relations and ontology information in xml file.
<relation>
– <term>
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<iq>78</iq>
<source>MeSH</source>
<relationDocuments>
– <doc> 34</doc
– </term>
– <term> </term>
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</relation>
This XML file is a portable version of the computed
relations that we can then use with any number of
viewers.
A Graphical Relations Viewer
Creates a Java Relations object for each
relation it reads from the XML file.
 Inserts them into a Trie structure based on
lower cased first term.
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– If there is already a Relation at that point, it
adds them to a Vector for that term.
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Creates an alphabetical list of all terms in a
2nd Trie.
Using the Viewer
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When you enter part of a
term, it shows all terms
starting with that fragment in
the left list box.
When you click on a term, it
shows all its relations in the
right list box.
Lexical Navigation
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Displays relations
between terms
graphically and allows
you to explore them
without formulating a
specific query.
Possible enhancements
Show only terms belonging to an ontology.
 Show only higher IQ terms
 Show the documents the relations occur in.
 Show the ontology reference.
 Show computed paths
 Show more kinds of named relations.
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– Inhibits, expresses
Evaluations of Information
Visualization
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Few, if any, graphical displays have been
evaluated thus far for effectiveness.
Usability studies are hard to construct and carry
out.
Intuition seems to show
– that exploration may result in discoveries.
– Relations more than one step apart seem best
displayed graphically.
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Remains to be shown that such visualizations are
actually useful.
Differences in Intent
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Displays may represent information your
system has discovered.
– Gene – protein relations
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Or they may represent data from which the
user may discover new information.
– New 2nd or 3rd order relationships
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These are rather different applications of
visualization technology
Summary
Java-based text mining system
 Database of terms and positions
 Computation of relations
 Export as XML
 Graphical relations viewer
 The value of such visual interfaces has not
yet been established.
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Acknowledgements
Bhavani Iyer – XML export
 Eric Brown – DictMatcher hash code
 Daniel Tunkelang – graphical layout
 Bob Mack – paper suggestions
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