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 - PowerPoint PPT Presentation

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  • Ontological Foundations for Scholarly Debate Mapping TechnologyCOMMA 08, 29 May 2008Neil BENN, Simon BUCKINGHAM SHUM, John DOMINGUE, Clara MANCINI

  • OutlineBackground: Access vs. AnalysisResearch ObjectivesDebate Mapping ontologyExample: Representing & analysing the Abortion DebateConcluding Remarks

  • Access vs. AnalysisNeed to move beyond accessing academic documentssearch 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 ApproachesBibliometrics approachFocus on citation relationThus, 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 ApproachesSemanticsMultiple concept and relation typesConcepts and relations specified in an ontologyOntology-based representation to support more intelligent information retrievalTool examples: ESKIMO, CS AKTIVE SPACE, ClaiMaker, Bibster

  • Bibster

  • Research ObjectivesNone considers the macro-discourse of knowledge domainsDiscourse analysis should be a priority other forms of analysis are partial indices of discourse structureWhat 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 OntologyBased on logic of debate theorised in Yoshimi (2004) and demonstrated by Robert Horn Issues, Claims and Argumentssupports and disputes as main inter-argument relationsSimilar to IBIS structureConcerned with macro-argument structureWhat are the properties of a given debate?

  • Ex: Using Wikipedia Source

  • Issues

  • Propositions and Arguments

  • Publications and Persons

  • Explore New FunctionalityFeatures of the debate not easily obtained from raw source materialE.g. Detecting clusters of viewpoints in the debateA macro-argumentation featureAs appendix to supplement (not replace) source materialReuse citation network clustering technique

  • Reuse MismatchNetwork-based techniques require single-link-type network representationsSimilarity assumed between nodesTypically co-citation as similarity measure

  • Inference RulesImplement ontology axioms for inferring other meaningful similarity connectionsRules-of-thumb (heuristics) not lawsCo-membershipCo-authorship

  • Inference RulesAll inferences interpreted as Rhetorical Similarity in debate contextNeed to investigate cases where heuristics breakdownMutual SupportMutual Dispute

  • Applying the Rules

  • Cluster AnalysisVisualisation and clustering performed using NetDraw

  • Debate Viewpoint Clusters

  • Reinstating Semantic TypesVisualisation and clustering performed using NetDrawBASIC-ANTI-ABORTION-ARGUMENTBASIC-PRO-ABORTION-ARGUMENTBODILY-RIGHTS-ARGUMENTABORTION-BREAST-CANCER-HYPOTHESISTACIT-CONSENT-OBJECTION-ARGUMENTEQUALITY-OBJECTION-ARGUMENTCONTRACEPTION-OBJECTION-ARGUMENTRESPONSIBILITY-OBJECTION-ARGUMENTJUDITH_THOMSONDON_MARQUISPETER_SINGERERIC_OLSONDEAN_STRETTONMICHAEL_TOOLEY

  • Two Viewpoint ClustersBASIC-ANTI-ABORTION-ARGUMENTBASIC-PRO-ABORTION-ARGUMENTJUDITH_THOMSONPETER_SINGERDEAN_STRETTONDON_MARQUISERIC_OLSONJEFF_MCMAHANJEFF_MCMAHAN

  • Concluding RemarksNeed for technology to support knowledge domain analysisFocussed specifically on the task of analysing debates within knowledge domainsOntology-based representation of debateAim to capture macro-argument structureWith goal of exploring new types of analytical resultse.g. clusters of viewpoints in the debate (which is enabled by reusing citation network-based techniques)

  • Limitations & Future WorkThe 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 upViewpoint clustering validationCurrently only intuitively validPossibility of validating against positions identified by domain expertsMatching against philosophical camps identified on Horn debate maps of AI domain

  • Thank you

    This talk is about AN ontology to facilitate representing and analysing scholarly debatesTask of providing new insights into the makeup of a particular knowledge domain

    What are the canonical papers? Who are my counterparts? What is the leading edge? What are the research issues? How is this work positioned with respect to the rest of the field?Bibliometrics is the study of bibliographic data using mathematical and statistical methods. (Kampa, 2002) Also encapsulates empirical methods based on the study of relationships in bibliographies (Kampa, 2002). For example, citation analysis and collaboration measures.Network-based analysis can discover macro-structures, trends, and patterns (and implicit knowledge)These techniques can potentially uncover important patterns and emerging trends that scholars had previously overlooked

    Citation-based tools are about mining publications.Ontology-based tools are about marking up resources to improve retrieval (by giving them meaning w.r.t some program or some model of the world)Bibliometrics is the study of bibliographic data using mathematical and statistical methods. (Kampa, 2002) Also encapsulates empirical methods based on the study of relationships in bibliographies (Kampa, 2002). For example, citation analysis and collaboration measures.Network-based analysis can discover macro-structures, trends, and patterns (and implicit knowledge)These techniques can potentially uncover important patterns and emerging trends that scholars had previously overlooked

    Citation-based tools are about mining publications.Ontology-based tools are about marking up resources to improve retrieval (by giving them meaning w.r.t some program or some model of the world)Bibliometrics is the study of bibliographic data using mathematical and statistical methods. (Kampa, 2002) Also encapsulates empirical methods based on the study of relationships in bibliographies (Kampa, 2002). For example, citation analysis and collaboration measures.

    Citation-based tools are about mining publications.Ontology-based tools are about marking up resources to improve retrieval (by giving them meaning w.r.t some program or some model of the world)Bibliometrics is the study of bibliographic data using mathematical and statistical methods. (Kampa, 2002) Also encapsulates empirical methods based on the study of relationships in bibliographies (Kampa, 2002). For example, citation analysis and collaboration measures.Network-based analysis can discover macro-structures, trends, and patterns (and implicit knowledge)These techniques can potentially uncover important patterns and emerging trends that scholars had previously overlooked

    Citation-based tools are about mining publications.Ontology-based tools are about marking up resources to improve retrieval (by giving them meaning w.r.t some program or some model of the world)Yoshimis aim is to develop a theory of the structure of debate. His research question is, How are debates structure? My research question is, How does scholarly debate relate to other aspects of knowledge domains?In future talks this slide can be expanded to include another network-based service viewpoint evaluation using connectionist network-based reasoning (cf. Thagards theory of Explanatory Coherence)These are rules of thumb (heuristics) rather than rigid laws:They transpose the heuristics from co-citation analysisAnd then they provide a new generalisation in the form of +ADDITIVE inference (language of CCR used to indicate that we are interpreting these relations in a discourse context)Explain that mutual support and mutual dispute are being interpreted in the same wayWe can see which authors are rhetorically close. Was it possible to determine this using just the raw data?In theory this approach is supposed to promote interoperability. However, this has not been demonstrated here. So perhaps this could be the focus of future work.

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