Argumentation Trails and Topic Maps


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With argumentation trails we introduce an approach of finding relevant associations between arbitrary terms. An argumentation trail between two terms is an ordered list of cooccurrences, providing a connected path from the origin to the endpoint of the argumentation. Within this paper the automatic generation of argumentation trails is examined and assessed. Furthermore, the
formal representation of these trails as Topic Maps is implemented. This enables the integration of argumentation trails with further background information to support sensemaking or other discourse enriching techniques for academic or political debates.

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Argumentation Trails and Topic Maps

  1. 1. Automatic Extraction of Topic Maps based Argumentation Trails Text Mining Services Conference Leipzig, 2009/03/25 Marco Büchler, Lutz Maicher, Frederik Baumgardt, Benjamin Bock Natural Language Processing Group Department of Computer Science University of Leipzig
  2. 2. Starting Point: Panionion ‏
  3. 3. <ul><li>Computation of argumentation trails on fragmentary texts </li></ul><ul><li>Surplus and relation between Topic Maps and argumentation trails </li></ul><ul><li>Results </li></ul><ul><li>Further work / conclusion </li></ul>Agenda
  4. 4. Technical details
  5. 5. Text source
  6. 6. <ul><li>Co-occurrence as underlying graph </li></ul><ul><ul><li>- de Saussure (1898/1916 ): </li></ul></ul><ul><ul><ul><li>Structuralism assumes that meaning is the result of structural relations between word forms </li></ul></ul></ul><ul><ul><ul><li>The fundamental structural relations are syntagmatic and paradigmatic relations [Heyer & Bordag 2007] </li></ul></ul></ul><ul><li>Argumentation trails vs. </li></ul><ul><li>Lexical Chaining </li></ul><ul><ul><li>- fragmentary texts </li></ul></ul>Underlying graph
  7. 7. <ul><ul><ul><li>“ Definition/Motivation”: </li></ul></ul></ul><ul><ul><ul><ul><ul><li>What's the average path length in a graph? </li></ul></ul></ul></ul></ul><ul><ul><ul><li>Average path length is typically not larger than7. </li></ul></ul></ul><ul><ul><ul><li>Simple proof of concept (Using XING): </li></ul></ul></ul><ul><ul><ul><ul><li>Every person of my contacts has in </li></ul></ul></ul></ul><ul><ul><ul><li>average about 73 contacts (1. and 2. </li></ul></ul></ul><ul><ul><ul><li>level) </li></ul></ul></ul><ul><ul><ul><li>log 73 (6,800,000,000)= 5,28 </li></ul></ul></ul>Small World
  8. 8. Methodology
  9. 9. Topic Maps ‏
  10. 10. Data model of Topic Maps (Topics) Nikolaikirche variant St. Nicholas Church St. Nikolai name English scope 1165 occurrence www.nikolaikirche occurrence foundation type website type
  11. 11. Data model of Topic Maps (Associations) St. Nikolai Leipzig association container-containee ass. role role player container containee role type
  12. 12. Data model of Topic Maps (Summary) ‏ <ul><li>one topic represents one subject in a data source </li></ul><ul><ul><li>names represent the names of the subject </li></ul></ul><ul><ul><ul><li>names might have variants </li></ul></ul></ul><ul><ul><li>occurrences represent properties of the subject </li></ul></ul><ul><ul><li>associations represent relationships between subjects </li></ul></ul><ul><ul><ul><li>flexibility through roles </li></ul></ul></ul><ul><ul><ul><li>n-ary associations </li></ul></ul></ul><ul><ul><li>all types and scopes are (set of) Topics </li></ul></ul><ul><ul><ul><li>in a topic map everything is a topic </li></ul></ul></ul>
  13. 13. What are Topic Maps (ISO 13250)? <ul><li>Topic Maps are highly-networked data sources </li></ul><ul><ul><ul><li>one topic for each subject </li></ul></ul></ul><ul><ul><ul><li>relationships of subjects are associations between topics </li></ul></ul></ul><ul><li>Topic Maps have a human-centric data model </li></ul><ul><ul><ul><li>vocabulary for documenting information fits human cognition </li></ul></ul></ul><ul><ul><ul><li>network resembles human cognition </li></ul></ul></ul><ul><li>Topic Maps have an integration model </li></ul><ul><ul><ul><li>whenever two topics represent the same subject, they have to be merged </li></ul></ul></ul><ul><ul><ul><li>always one information access hub for each subject </li></ul></ul></ul><ul><ul><ul><li>high terminological flexibility and schema-free </li></ul></ul></ul><ul><ul><ul><li>use in knowledge federation and sensemaking </li></ul></ul></ul><ul><li>Topic Maps is an international industry standard (ISO 13250) ‏ </li></ul>
  14. 14. Extraction of typed significant terms Corpus is categorized in several classification schemas. Split corpus into several sub corpora Medusa age gender geography .... Categorized co-occurrences/terms Tomcat/ Prefuse Age gender geography (Source:Taken from bachelor thesis slides of Marcus Puchalla.) ‏
  15. 15. Results
  16. 16. Several graph properties
  17. 17. Visualisation of two argumentation trails
  18. 18. Marco Büchler Topic-Maps-Ontologie for the Argumentation Trails Topic Maps and Argumentation Trails
  19. 23. - Reduction of graph comlexity - e. g. by semantic pre-clustering or - authors restrictions - Weighting of argumentation trails - e. g. Trails containing hubs should be weighted lower - Improvements in visualisation - Clustering of similar trails to a bunch of semanitic similar trails - Improvements in typing nodes and especially edges Further work / conclusion