Ontological Foundations for Scholarly Debate Mapping Technology
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Ontological Foundations for Scholarly Debate Mapping Technology
COMMA ‘08, 29 May 2008
Neil BENN, Simon BUCKINGHAM SHUM, John
DOMINGUE, Clara MANCINI
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Outline
• Background: Access vs. Analysis• Research Objectives• Debate Mapping ontology• Example: Representing & analysing
the Abortion Debate• Concluding Remarks
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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?
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Two ‘KDA’ Approaches
1. 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
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CiteSpace
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Two ‘KDA’ Approaches
2. 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
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Bibster
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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
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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?
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Ex: Using Wikipedia Source
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Issues
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Propositions and Arguments
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Publications and Persons
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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
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Reuse Mismatch
• Network-based techniques require single-link-type network representations– ‘Similarity’ assumed between nodes– Typically ‘co-citation’ as similarity measure
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Inference Rules
• Implement ontology axioms for inferring other meaningful similarity connections
• Rules-of-thumb (heuristics) not laws
Co-membership Co-authorship
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Inference Rules
• All inferences interpreted as ‘Rhetorical Similarity’ in debate context
• Need to investigate cases where heuristics breakdown
Mutual Support Mutual Dispute
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Applying the Rules
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Cluster Analysis
Visualisation and clustering performed using NetDraw
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Debate ‘Viewpoint Clusters’
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Reinstating Semantic Types
Visualisation and clustering performed using NetDraw
BASIC-ANTI-ABORTION-ARGUMENT
BASIC-PRO-ABORTION-ARGUMENT
BODILY-RIGHTS-ARGUMENTABORTION-BREAST-CANCER-HYPOTHESIS
TACIT-CONSENT-OBJECTION-ARGUMENT
EQUALITY-OBJECTION-ARGUMENT
CONTRACEPTION-OBJECTION-ARGUMENT
RESPONSIBILITY-OBJECTION-ARGUMENT
JUDITH_THOMSONDON_MARQUIS
PETER_SINGERERIC_OLSON
DEAN_STRETTON
MICHAEL_TOOLEY
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Two Viewpoint Clusters
BASIC-ANTI-ABORTION-ARGUMENT
BASIC-PRO-ABORTION-ARGUMENT
JUDITH_THOMSON
PETER_SINGER
DEAN_STRETTON
DON_MARQUIS
ERIC_OLSON
JEFF_MCMAHAN JEFF_MCMAHAN
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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)
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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
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Thank you