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

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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

  • Concluding Remarks


Access vs. Analysis

  • 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

  • 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


Two ‘KDA’ Approaches

  • 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


Research Objectives

  • 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

  • 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?


Ex: Using Wikipedia Source


Issues


Propositions and Arguments


Publications and Persons


Explore New Functionality

  • 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

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

    • ‘Similarity’ assumed between nodes

    • Typically ‘co-citation’ as similarity measure


Inference Rules

Co-authorship

Co-membership

  • Implement ontology axioms for inferring other meaningful similarity connections

  • Rules-of-thumb (heuristics) not laws


Inference Rules

Mutual Dispute

Mutual Support

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

  • Need to investigate cases where heuristics breakdown


Applying the Rules


Cluster Analysis

Visualisation and clustering performed using NetDraw


Debate ‘Viewpoint Clusters’


Reinstating Semantic Types

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

BASIC-PRO-ABORTION-ARGUMENT

JUDITH_THOMSON

PETER_SINGER

DEAN_STRETTON

JEFF_MCMAHAN

JEFF_MCMAHAN

ERIC_OLSON

DON_MARQUIS

BASIC-ANTI-ABORTION-ARGUMENT


Concluding Remarks

  • 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

  • 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


Thank you


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