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

Ontological Foundations for Scholarly Debate Mapping Technology

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

COMMA ‘08, 29 May 2008


Outline

Outline

  • Background: Access vs. Analysis

  • Research Objectives

  • Debate Mapping ontology

  • Example: Representing & analysing the Abortion Debate

  • Concluding Remarks


Access vs analysis

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

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

CiteSpace


Two kda approaches1

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

Bibster


Research objectives

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

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

Ex: Using Wikipedia Source


Issues

Issues


Propositions and arguments

Propositions and Arguments


Publications and persons

Publications and Persons


Explore new functionality

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

Reuse Mismatch

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

    • ‘Similarity’ assumed between nodes

    • Typically ‘co-citation’ as similarity measure


Inference rules

Inference Rules

Co-authorship

Co-membership

  • Implement ontology axioms for inferring other meaningful similarity connections

  • Rules-of-thumb (heuristics) not laws


Inference rules1

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

Applying the Rules


Cluster analysis

Cluster Analysis

Visualisation and clustering performed using NetDraw


Debate viewpoint clusters

Debate ‘Viewpoint Clusters’


Reinstating semantic types

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

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

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

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


Ontological foundations for scholarly debate mapping technology

Thank you


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