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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. 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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  1. Ontological Foundations for Scholarly Debate Mapping Technology Neil BENN, Simon BUCKINGHAM SHUM, John DOMINGUE, Clara MANCINI COMMA ‘08, 29 May 2008

  2. Outline • Background: Access vs. Analysis • Research Objectives • Debate Mapping ontology • Example: Representing & analysing the Abortion Debate • Concluding Remarks

  3. 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?

  4. 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

  5. CiteSpace

  6. 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

  7. Bibster

  8. 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

  9. 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?

  10. Ex: Using Wikipedia Source

  11. Issues

  12. Propositions and Arguments

  13. Publications and Persons

  14. 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

  15. Reuse Mismatch • Network-based techniques require single-link-type network representations • ‘Similarity’ assumed between nodes • Typically ‘co-citation’ as similarity measure

  16. Inference Rules Co-authorship Co-membership • Implement ontology axioms for inferring other meaningful similarity connections • Rules-of-thumb (heuristics) not laws

  17. Inference Rules Mutual Dispute Mutual Support • All inferences interpreted as ‘Rhetorical Similarity’ in debate context • Need to investigate cases where heuristics breakdown

  18. Applying the Rules

  19. Cluster Analysis Visualisation and clustering performed using NetDraw

  20. Debate ‘Viewpoint Clusters’

  21. 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

  22. 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

  23. 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)

  24. 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

  25. Thank you

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