Visualizing ScholarlyDiscourse in eScience

Simon Buckingham ShumKnowledge Media Institute

Open University, UK

In collaboration with:John Domingue, Enrico Motta, Gary Li, Victoria Uren, Marc Eisenstadt (Open U.), Austin Tate (Edinburgh U.), Nigel Shadbolt, Dave De Roure (Southampton U.), Albert Selvin

(Verizon, USA), Maarten Sierhuis (NASA, USA)

Visualization for eScience Workshop, National eScience CentreEdinburgh, 23-24 Jan, 2003

Why focus on discourse?

• Researchers spend a lot of time talking and arguing, in meetings and documents

• New opportunities for eScience collaborative technologies- conversations in meetings- arguments in the literature

• Discourse has rhetorical structure• Semantic hypertext tools enable us to

construct and map this visually

2 EPSRC projects at the OU are developing tools

Compendium…real-time mapping of discussions and

domain modelling in meetings

ClaiMaker…modelling research literatures as a

networks of claims and counter-claims

Example 1: Compendium

• Visual mapping of discussions and domains

• Semantic hypertext for connecting ideas and resources

• Simple but powerful visual language based on IBIS (Issue-Based Information System)

• Group memory for collaborators

– Working memory: shared visual maps of discussions created during meetings

– Long term memory: recover discussions/rationale from months back

• Interoperability with other tools:

– Generates documentation and web discussions

– An intuitive interface for populating complex models

• Large, long term case studies documented

Compendium discussion map from a project meeting, capturing open

issues, options, decisions, and linking in other resources

Maps built in meetings can be exported to create

other formats of document, e.g. analysing

Y2K threats to an organisation (Verizon,

USA)

Compendium for Visual Modelling

Compendium’s visual maps can be used to elicit data

to populate modelling tools,

e.g. simulation of a Mars lander team

(NASA Ames)

Compendium for Visual Modelling

Compendium for Remote Collaboration

• The eScience CoAKTinG Project is integrating a suite of technologies to support the following kinds of eScience collaboration:

• Mapping discussions in virtual meetings (Compendium, Open U.)

• Multimedia meeting replay/navigation (HyStream, Southampton U.)

• Coordination and synthesis activities (I-X Process Panels – Edinburgh U.)

• Peripheral awareness of colleagues presence and availability (BuddySpace, Open U.)

Time-delayed attendee replays precise moment by highlighting

relevant node in the Compendium discussion map (interface mockup)

CoAKTinG scenario 1

BuddySpace: Enriching presence+messaging with semantics and visualizations

I-X Process Panel: coordinated, active ‘To Do’ lists

Example 2: ClaiMaker

• Will prose always be the dominant format to disseminate, critique and debate research?

• Research literatures are huge networks of claims and debates: ClaiMaker renders this visible and analysable as a semantic web

• Use to review, model and analyse complex networks of ideas– Who disagrees with this paper?

– What evidence is there for this prediction?

– What impact did this paper have?

– What is the intellectual history of this idea?

A menu-driven web interface to annotate publications with new concepts, and

make connections between concepts using a set of link-types derived from discourse and argumentation theory, and commonly used in research.

Adding to the Network

Adding to the Network

Drawing new conceptual structures

via a mapping interface

(an alternative to the menu/form interface)

What documents challenge this one?

1. Extract concepts for this document2. Trace concepts on which they build3. Trace concepts challenging this set4. Show root documents

Focusing on a concept from previous view

Searching for PatternsTextual listing of results for a search on ‘machine

learning’ with a certain type of connection

Interactive applet to see and browse the structure of the search results

Simple linear SVMRules made with CHARADE outperform Naive Bayes and decision trees

Decision Forest classifier improves on C4.5 and kNN

Simple linear SVM is among the best reported text categorizers

CDM performs moderately better than Naive Bayes and decision trees

Optimised rules outperform Naive Bayes and decision trees

Decision trees and Naive Bayes perform well for text categorization

SVMs are well suited to text categorization

Support Vector Machines (SVM)

Naive Bayes underperforms other classifiersNaive Bayes is the worst classifier

Nearest Neigbour is one of the best categorizers

SVM and kNN outperform other classifiers

Which classifier is best?

Rule learning

Instance based learning

Bayesian learning

Decision tree learning

Machine learning

Graph-theoretic cluster detection. Borrowing from scientometric techniques for identifying emerging research fields, instead of citations, we use inter-concept links.

Visual Knowledge Services

Visualizing Argumentation

Springer-Verlag, 2002www.VisualizingArgumentation.info

• Argument mapping for scholarly publishing, scientific and public policy debates, education, teamwork, and organisational memory

To know more…

• EPSRC CoAKTinG Project: www.aktors.org/coaktingwww.CompendiumInstitute.orgkmi.open.ac.uk/projects/buddyspace

• EPSRC Scholarly Ontologies Project: kmi.open.ac.uk/projects/scholonto

claimaker.open.ac.uk

• Visualizing Argumentation book:www.VisualizingArgumentation.info