Ontological Foundations for Scholarly Debate Mapping Technology

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