Term co occurrence analysis as an interface to digital libraries
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Term Co-occurrence Analysis as an Interface to Digital Libraries. Jan W. Buzydlowski Howard D. White Xia Lin College of Information Science and Technology Drexel University, Philadelphia, Pennsylvania, USA. Digital Library Research. First Wave How to store it Next Wave

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Term Co-occurrence Analysis as an Interface to Digital Libraries

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Term co occurrence analysis as an interface to digital libraries

Term Co-occurrence Analysis as an Interface to Digital Libraries

Jan W. Buzydlowski

Howard D. White

Xia Lin

College of Information Science and Technology

Drexel University, Philadelphia, Pennsylvania, USA


Digital library research

Digital Library Research

  • First Wave

    • How to store it

  • Next Wave

    • How to retrieve it (IR)

      • Text Mining

      • Visual Information Retrieval Interface (VIRI)

  • Term Co-occurrence Analysis (TCA)

    • Co-occurrence vs. lexical associations

    • Maps vs. lists


Term definition

Term Definition

  • Unit of Analysis

    • Words

    • Documents

    • Authors

    • Journals

  • Section of Focus

    • Abstract/Text

    • Title

    • Bibliography

    • Keywords


Example

Words in Title

Term

Co-occurrence

Analysis

Interface

Digital

Library

Authors in Bibliography

Salton-G

Chen-C

White-HD

Ding-Y

Cleveland-W

McCain-K

Lin-X

Schvaneveldt-R

Kamada-T

Fruchterman-T

Example


Term co occurrence methodology

Term Co-occurrence Methodology

  • User determines which terms are of interest

    • Via a seed term

    • From a pre-defined list

  • The system returns the pair-wise co-occurrence counts of the terms over the collection of records


Example1

Example

  • Unit: Author; Section: Bibliography

  • User Supplied List: Plato, Aristotle, Smith, Brown

  • For a given data set (N = 4 unique terms)

    • Article 1: Plato, Aristotle, Smith, …

    • Article 2: Plato, Smith, …

    • Article 3: Plato, Aristotle, Smith, Brown, …

  • The following co-citations (C(4,2) = 6) are found

    • COMBINATIONCOUNTARTICLES

    • Plato and Smith31, 2, 3

    • Plato and Aristotle21, 3

    • Plato and Brown13

    • Aristotle and Smith21, 3

    • Aristotle and Brown13

    • Smith and Brown13


Term co occurrence significance

Term Co-occurrence Significance

  • The frequent co-occurrence of term pairs within a set of documents indicates a strong association between those terms, whereas a infrequent count indicates the opposite

    • The association you would expect is borne out by the frequency

    • The frequency you compute suggests a level of association

  • Pain and ManagementPain and Obtainment

  • Plato and AristotlePlato and Cher

  • Science and NatureScience and National Tattler

  • A and BC and D


Term co occurrence uses

Term Co-occurrence Uses

  • Allows a user to get a “foothold” with just one term

    • One seed term returns many other related terms

  • Allows a user to get a “overview” with user-supplied/system-supplied terms

    • Co-occurrence counts with visualization


Seeding

Seeding

  • User types in

    • One term, e.g., Plato

    • Boolean expression, e.g., Plato AND Brown

  • System supplies top n terms, in ranked order of frequency of co-occurrence with the initial term


Example2

Example

  • For Plato seed:

  • ARISTOTLE

  • PLUTARCH

  • CICERO

  • HOMER

  • BIBLE

  • EURIPIDES

  • ARISTOPHANES

  • XENOPHON

  • AUGUSTINE

  • HERODOTUS

  • KANT-I

  • AESCHYLUS

  • SOPHOCLES

  • THUCYDIDES

  • OVID

  • HESIOD

  • DIOGENES-LAERTI

  • HEIDEGGER-M

  • DERRIDA-J

  • PINDAR

  • NIETZSCHE-F

  • HEGEL-GWF

  • VERGIL

  • AQUINAS-T


Need for visualization

Need for Visualization

  • Given a list of user- / system-supplied terms

    • Find the frequency of co-occurrence of each pair-wise combination of terms

      • Plato AND Aristotle = 1,920

      • Plato AND Plutarch = 380,

    • Too many numbers to take in at once

      • C(25, 2) = (25 * 24)/ 2 = 300 pairs

  • Three major visualization techniques

    • Multidimensional Scaling (MDS)

    • Self-Organizing (Kohonen) Maps (SOMs)

    • PathFinder Networks (PFNETs)


Term co occurrence analysis as an interface to digital libraries

P Arabie

JH Ward

JC Gower

M Wish

RN Shepard

RR Sokal

JB Kruskal

SC Johnson

PHA Sneath

JD Carroll

PE Green

JA Hartigan

HA Skinner

VE McGee

RK Blashfield

White’s MDS map of 15 co-cited classificationists, ca. 1990


Term co occurrence analysis as an interface to digital libraries

White’s PFNet of co-cited authors in Biblical and literary hermeneutics, 1988-1997


Our system

Three tiered

User interface

Server

Database

Real-time and interactive

Significant data sources

ISI AHCI

MedLine

Live interface for retrieval

Our System


User interface seed

User Interface - Seed


User interface som

User Interface – SOM


Interface pfnet

Interface - PFNET


Interface visual information retrieval interface viri

Interface - Visual Information Retrieval Interface (VIRI)


User interface iv

User Interface IV


Database interface

Database Interface

  • API

    • String [ ] findRel( String, int )

    • Int [ ] findOcc( String [ ] )

  • Implemented on:

    • BRS

      • API via a wrapper

    • Oracle

      • API via JDBC

    • Noah

      • Specialized co-occurrence database

      • API via JNI


Future plans

Future Plans

  • User Study

    • Preference

      • Type of map, etc.

    • Cognitive map

      • How well does the map match experts’ mental models

  • Larger datasets

  • Additional data sources


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