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A proposal to study Calderon_GC'11
- 1. A proposal to study Calderon’s theatre: Topic Maps and Graph Databases… and a little bit of something else that we don’t know yet… but we’ll keep you posted. Miriam Peña-Pimentel The University of Western Ontario mpenapie@uwo.ca
- 3. Topic Maps?
- 9. Evolution of the research (GDB):
- 10. Targeting an specific objective:
- 11. One Character
- 13. Methodology
- 14. First results
- 16. Semantic linking between data, concepts and sources.
- 17. Schema layer vs. Data layers: different levels of abstraction.
- 19. Places
- 20. Objects
- 21. Feelings
- 22. Actions
- 25. Geography: Ocaña is in Spain
- 26. History: “La rendición de Bredá” was in 1625
- 27. Mythology: Zeus is from Greek Mythology
- 28. …
- 30. Play 1: H2D “Casa con dos puertas” Characters: Lisardo Marcela Don Félix Laura Topics: Honor Trick Jealousy Ocaña (Place)
- 31. Some results from THE analysis
- 32. Play 1 auxiliarTMs Final TM + = Play 2 + Play 3 + Schema +
- 33. Basic Analysis
- 34. Extracting new semantic connections Onlyfrom non-charactertopics 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. UsingCharacters as mediators
- 37. Targeting an specific objective One character: The Gracioso in Calderon´sComedias. Speech Act Theory: Verbs: classification of the actions based on the verb that better represents them. Context: the plot of the play determines the reaction of the character.
- 38. Collecting the data Description of the Comedia: -Title -Character -Acts -Verses Speech Act: -Verb -Description of the character´s participation Context: -Situation -Place -Character´s disposition
- 39. Graph Scheme for Data:
- 40. Graph Scheme for Data: Speech Act Context Description
- 42. Complete Graph … toobigfor visual analysis ... Current Data (~80%): 10 comedias ~ 4000 topics ~ 15,000 links Expected Data: 12 comedias ~ 5000 topics ~ 18,000 links
- 44. CombiningQueries ["Clas.Calvo" "Verbo" "Acto" "Contexto" "Sit.Tipo" ] ["Clas.Calvo" "Verbo" "Acto" "Contexto" "Sit.Honor" ] ["Clas.Calvo" "Verbo" "Acto" "Contexto" "Sit.Jerárquica“]
- 46. Topic Maps (semantic) vs. Text Mining (syntax).
- 47. Target of an specific objective and theory.
- 48. Moving from TMs to Graph Databases: Visualizations and further analysis.
Notas do Editor
- 15.- Topic maps allowed us to build a general analysis from the palys, where we included all the elements that conform the Plot.However, for a more specific target and objective, we center the research in the study of the Gracioso in the Comedias of Calderon. And we use the Speech act theory to analyze the participations of the character in the context of every participation in the play.
- 16.- As in the preivous case, the data is product of the analysis of the plays, from the reading we collect:*The information of each play: Title Character Acts Verses*The information about the Speech acts: The verb that better describes the dialogue This verb is based in two classifications Austin Calvo The description of the Character’s participation Interaction with other characters Information to complete the context prior to the scene…*The description of the context The Hierarchical situation among the characters involved in the scene The “Honor status” Type of situation (adventure, danger, romance)*The description of the space Open space Close space Fantasy space*The disposition of the characterWhilling to cooperate Honest
- 17-18.- As an evolution from the TM schema, here we use a more general concept based on a Graph Database, but the idea is very similar to the previous one (topics = nodes, links= semantic relations). The GDB could have also an scheme stablishing the different types of nodes it can store and the type of links you can make between them. Here we show the scheme used for our study, that store the different components of the records present in the graph.
- Where we can see the different components of out data
- 19.- As a sample, this is the representation of one of the records in the database. Here we can see the components of the record: Character, Space, Comedie, Act, Verse, etc.
- 20.- The current data, extracted from 10 Comedies is (approx): 4000 Topics 15000 links/relationsThe expected amount of data, that will be 12 comedies (approx):5000 topics 18000 links(A simplified view of the current global graph is shown)
- 21.- In this stage we have implemented a Query system that could be applied to a singular play, to a selection of plays or to the total of plays in the GDB.A query is specified by a traversal (a path on the schema) and the result is: two nodes are related for the traversal if a path ot this type existes between them. This is the example of a Query (red ) showing the apparition of the different closed spaces in the plays: one play will be connected with a closed space if there exists a path between them (in this case, they both appears in the same recorded act); as many different paths of this type exist, thicker will be the link between them. As a result, the system provides a weighted graph with the relations between the topics of the two types, and then we can obtain plots showing this relations.
- 22.- Ths system allows the use of combined queries:In this example we have three queries: According to Calvo’s verbs classification, how frequent they appear in the context with the different situations (hierarchical, honor and type).And which verb classification is the most frequent