Ontological foundations for scholarly debate mapping technology
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Ontological Foundations for Scholarly Debate Mapping Technology. Neil BENN, Simon BUCKINGHAM SHUM, John DOMINGUE, Clara MANCINI. COMMA ‘08, 29 May 2008. Outline. Background: Access vs. Analysis Research Objectives Debate Mapping ontology Example: Representing & analysing the Abortion Debate

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Ontological foundations for scholarly debate mapping technology

Ontological Foundations for Scholarly Debate Mapping Technology

Neil BENN, Simon BUCKINGHAM SHUM, John DOMINGUE, Clara MANCINI

COMMA ‘08, 29 May 2008


Outline
Outline Technology

  • Background: Access vs. Analysis

  • Research Objectives

  • Debate Mapping ontology

  • Example: Representing & analysing the Abortion Debate

  • Concluding Remarks


Access vs analysis
Access vs. Analysis Technology

  • Need to move beyond accessing academic documents

    • search engines, digital libraries, e-journals, e-prints, etc.

  • Need support for analysing knowledge domains to determine (e.g.)

    • Who are the experts?

    • What are the canonical papers?

    • What is the leading edge?


Two kda approaches
Two ‘KDA’ Approaches Technology

  • Bibliometrics approach

    • Focus on ‘citation’ relation

    • Thus, low representation costs (automatic citation mining)

    • Network-based reasoning for identifying structures and trends in knowledge domains (e.g. research fronts)

    • Tool examples: CiteSeer, Citebase, CiteSpace


Citespace
CiteSpace Technology


Two kda approaches1
Two ‘KDA’ Approaches Technology

  • Semantics

    • Multiple concept and relation types

    • Concepts and relations specified in an ontology

    • Ontology-based representation to support more ‘intelligent’ information retrieval

    • Tool examples: ESKIMO, CS AKTIVE SPACE, ClaiMaker, Bibster


Bibster
Bibster Technology


Research objectives
Research Objectives Technology

  • None considers the macro-discourse of knowledge domains

    • Discourse analysis should be a priority – other forms of analysis are partial indices of discourse structure

    • What is the structure of the ongoing dialogue? What are the controversial issues? What are the main bodies of opinion?

  • Aim to support the mapping and analysis of debate in knowledge domains


Debate mapping ontology
Debate Mapping Ontology Technology

  • Based on ‘logic of debate’ theorised in Yoshimi (2004) and demonstrated by Robert Horn

    • – Issues, Claims and Arguments

    • supports and disputes as main inter-argument relations

    • Similar to IBIS structure

  • Concerned with macro-argument structure

    • What are the properties of a given debate?



Issues
Issues Technology




Explore new functionality
Explore New Functionality Technology

  • Features of the debate not easily obtained from raw source material

  • E.g. Detecting clusters of viewpoints in the debate

    • A macro-argumentation feature

    • As appendix to supplement (not replace) source material

  • Reuse citation network clustering technique


Reuse mismatch
Reuse Mismatch Technology

  • Network-based techniques require single-link-type network representations

    • ‘Similarity’ assumed between nodes

    • Typically ‘co-citation’ as similarity measure


Inference rules
Inference Rules Technology

Co-authorship

Co-membership

  • Implement ontology axioms for inferring other meaningful similarity connections

  • Rules-of-thumb (heuristics) not laws


Inference rules1
Inference Rules Technology

Mutual Dispute

Mutual Support

  • All inferences interpreted as ‘Rhetorical Similarity’ in debate context

  • Need to investigate cases where heuristics breakdown


Applying the rules
Applying the Rules Technology


Cluster analysis
Cluster Analysis Technology

Visualisation and clustering performed using NetDraw



Reinstating semantic types
Reinstating Semantic Types Technology

BASIC-ANTI-ABORTION-ARGUMENT

BASIC-PRO-ABORTION-ARGUMENT

ABORTION-BREAST-CANCER-HYPOTHESIS

BODILY-RIGHTS-ARGUMENT

DON_MARQUIS

JUDITH_THOMSON

ERIC_OLSON

PETER_SINGER

EQUALITY-OBJECTION-ARGUMENT

CONTRACEPTION-OBJECTION-ARGUMENT

DEAN_STRETTON

RESPONSIBILITY-OBJECTION-ARGUMENT

MICHAEL_TOOLEY

TACIT-CONSENT-OBJECTION-ARGUMENT

Visualisation and clustering performed using NetDraw


Two viewpoint clusters
Two Viewpoint Clusters Technology

BASIC-PRO-ABORTION-ARGUMENT

JUDITH_THOMSON

PETER_SINGER

DEAN_STRETTON

JEFF_MCMAHAN

JEFF_MCMAHAN

ERIC_OLSON

DON_MARQUIS

BASIC-ANTI-ABORTION-ARGUMENT


Concluding remarks
Concluding Remarks Technology

  • Need for technology to support ‘knowledge domain analysis’

    • Focussed specifically on the task of analysing debates within knowledge domains

  • Ontology-based representation of debate

    • Aim to capture macro-argument structure

  • With goal of exploring new types of analytical results

    • e.g. clusters of viewpoints in the debate (which is enabled by reusing citation network-based techniques)


Limitations future work
Limitations & Future Work Technology

  • The ontology-based representation process is expensive (time and labour):

    • Are there enough incentives to makes humans participate in this labour-intensive task?

    • Need technical architecture (right tools, training, etc.) for scaling up

  • Viewpoint clustering validation

    • Currently only intuitively valid

    • Possibility of validating against positions identified by domain experts

      • Matching against ‘philosophical camps’ identified on Horn debate maps of AI domain


Ontological foundations for scholarly debate mapping technology

Thank you Technology