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Fernando Sancho Caparrini


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The use of Topic Maps could be extended to most of the research fields that work with information and in which the semantics is a main component. This paper presents the implementation of this tool for Philological analysis, applied to the example of theatrical productions. Throughout the paper we can see the use of the Topic Map concepts within the construction of each element (schema, populating, layers, etc.). This being the first approach to this matter applied in humanities, it is relevant the achievements related to the semantic of the texts, which is not only maintained, but it is essential for this subject of study. The results of these procedures can be measured in a quantitative level, but also, and more significant in a mix between philological approaches and semantic studies. This is all because of the internal construction of the Topic Map and the different visualizations created from it.

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Fernando Sancho Caparrini

  1. 1. TopicMapsfor<br />Philologicalanalysis<br />TMRA 2009<br />Miriam Peña Pimentel (U. of Western Ontario)<br />Juan Luis Suárez (U. of Western Ontario)<br />Fernando Sancho Caparrini (U. of Seville)<br />TheHispanicBaroque Project<br />
  2. 2. Overview<br /><ul><li>Work Context
  3. 3. Topic Map Creation: Schema & Population
  4. 4. Analysis & Results
  5. 5. New Measurements
  6. 6. Other Visualizations
  7. 7. Conclusions and future work</li></li></ul><li>Global context:<br />The Hispanic Baroque Project<br /><ul><li>Mapping of Baroque:
  8. 8. Methodology for Mapping Culture.
  9. 9. Tools:
  10. 10. Comparison tools.
  11. 11. Description tools.
  12. 12. Semantic annotation of Cultural Objects:
  13. 13. Art, Text, History, Music...</li></li></ul><li>Inf. Retrieval<br />Analysis<br />Documents<br />Visualization<br />Statistical<br />Networks<br />Scheme<br />MAthematical<br />Philological<br />TopicMap<br />TimeLines<br />
  14. 14. fromTopicMaps …<br /><ul><li>Model of knowledge representation.
  15. 15. Semantic linking between data, concepts and sources.
  16. 16. Schema layer vs. Data layers: different levels of abstraction.
  17. 17. “Layers as independent TMs” takes us to “independence of interpretation”.</li></li></ul><li>calderón de la barca<br />Calderón (Spain, 1600-1681).<br />Writer for the King and the Church.<br />Representative playwright of the Baroque.<br />Casa con dos puertas<br /> (A House with two Doors)<br />El sitio de Bredá<br /> (The Siege of Breda)<br />El príncipeconstante<br /> (The Constant Prince)<br />1640<br />
  18. 18. Theplays<br />Casa con dos puertas (H2D)<br /> Love story <br /> Family Honor / Trick<br />El sitio de Bredá (SB)<br /> Historic drama <br /> [Calderon’s contemporary Event]<br />El príncipeconstante (CP)<br /> Didactic drama<br /> Honor & Religion<br />
  19. 19. InitialTopicMapSchema<br />Mainitems in ourTopicMap:<br /><ul><li>Characters
  20. 20. Places
  21. 21. Objects
  22. 22. Feelings
  23. 23. Actions
  24. 24. …</li></li></ul><li>PopulatingtheTopicMap<br /><ul><li>From the plays (interpretation):</li></ul>Ocurrence<br />Señor don Luis, ya sabéis<br />que estimo vuestras finezas,<br />supuesto que lo merecen<br />por amorosas y vuestras;<br />pero no puedo pagarlas,<br />que eso han de hacer las estrellas<br />y no hay de lo que no hacen<br />quien las tome residencia; <br />si lo que menos se halla<br />es hoy lo que más se precia<br />en la Corte, agradeced<br />el desengaño, si quiera,<br />por ser cosa que se halla<br />con dificultad en ella:<br />quedad con Dios.<br />(La Dama Duende, Jornada 1, Versos: 278-292)<br />Context: Play<br />Don Luis<br />aprecio / esteem<br />mostrar / to show<br />hablar de / totalkabout<br />Doña Beatriz<br />rechazar / toreject<br />hablar de / totalkabout<br />desengaño / unhappyloveaffair<br />corte / court<br />
  25. 25. PopulatingtheTopicMap<br /><ul><li>From “external” sources (they provide new contexts):
  26. 26. Geography: Ocaña is in Spain
  27. 27. History: “La rendición de Bredá” was in 1625
  28. 28. Mythology: Zeus is from Greek Mythology
  29. 29.
  30. 30. Academic annotations (more contexts)</li></li></ul><li>ResultsfromTopicMaps<br />TopicMap<br />Networks<br />
  31. 31. Play 1: H2D<br />“Casa con dos puertas”<br />Characters:<br />Lisardo<br />Marcela<br />Don Félix<br />Laura<br />Topics:<br />Honor<br />Trick<br />Jealousy<br />Ocaña (Place)<br />
  32. 32. Some results from THE analysis<br />
  33. 33. Play 2: SB<br />“El sitio de bredá” <br />Characters:<br />Marqués Espínola<br />Alonso Ladrón<br />C. Enrique de Vergas<br />Justino de Nasau<br />Topics:<br />Honor<br />Courage<br />Strategy<br />Surrender<br />
  34. 34. Play 3: CP<br />“el principeconstante”<br />Characters:<br />Don Fernando<br />Muley<br />Moorish King<br />Don Enrique<br />Topics:<br />Honor<br />Benevolence<br />Death<br />Ceuta(Place)<br />
  35. 35. Play 1<br />auxiliarTMs<br />Final TM<br />+<br />=<br />Play 2<br />+<br />Play 3<br />+<br />Schema<br />+<br />
  36. 36. Final TopicMap<br />Topics:<br />Honor<br />Spain<br />Courage<br />Strategy<br />War<br />Surrender<br />Jealousy<br />
  37. 37. Basic Analysis<br />
  38. 38. Extracting new semantic connections:<br /><ul><li>New measurements: Co-occurrence</li></ul>A and B are co-occurring in a fragment F (A co-ocF B) if there exist some associations as1, as2 and some topics C and D where A as1 C, and B as2 D in F. <br />Co-occurring grade of A and B:<br /> <br />Groc(A,B) = card{F: A co-ocF B}<br />Interpretation:<br />If two topics occur in semantic relations in the same fragments of the text a high number of times, they “must” have a semantic relation (at least, subjectively for the author), and has a high philological value for subsequent analysis.<br />
  39. 39. <ul><li>Computing Co-occurrence in one play.</li></li></ul><li>OtherVisualizations:<br />TopicMap<br />Timelines<br />
  40. 40. H2D<br />TimelineVisualizationusingsimile:<br />
  41. 41. Conclusions<br /><ul><li>TMs provide a comfortable tool to organize complex cultural information and generate the first levels of analysis.
  42. 42. Topic Maps (semantic) vs. Text Mining (syntax).
  43. 43. Representationsshow different approaches to analysis and allow different interpretations of the data.</li></li></ul><li>Work in progress<br />Topic Maps, visualizations and analysis of all Calderón’s comedies.<br />Extend this methodology to other cultural manifestations, cultural epochs and cultural objects such as:<br />Artwork, Music, Literature<br />Development of a tool that incorporates the different steps necessaries for this methodology.<br />Extraction of new semantic connections.<br />Development of measures allowing the comparison between TMs (reflecting what we will understand as “distance” between cultural objects).<br />
  44. 44. Tools:<br />Data Base Manager: FileMaker<br />TopicMap Manager: Wandora<br />GraphVisualizer: yEd<br />TimeLines: SimileTimeline<br />Multi-AgentSystem:NetLogo<br />Thankyou!<br /> (SSHRC)<br />