Ontological Foundations for Scholarly Debate Mapping Technology
COMMA ‘08, 29 May 2008
Neil BENN, Simon BUCKINGHAM SHUM, John
DOMINGUE, Clara MANCINI
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
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
CiteSpace
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
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
• Implement ontology axioms for inferring other meaningful similarity connections
• Rules-of-thumb (heuristics) not laws
Co-membership Co-authorship
Inference Rules
• All inferences interpreted as ‘Rhetorical Similarity’ in debate context
• Need to investigate cases where heuristics breakdown
Mutual Support Mutual Dispute
Applying the Rules
Cluster Analysis
Visualisation and clustering performed using NetDraw
Debate ‘Viewpoint Clusters’
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
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
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
