1 / 34

Seeing

navigation. focus. software repositories. Bernd Fischer. attribute. browsing. context table. semi-structured data. Seeing. in the Clouds. join. visualization. mining. Galois connection. meet. Things. with tag clouds. object. Computer Science. knowledge discovery.

clive
Download Presentation

Seeing

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. navigation focus software repositories Bernd Fischer attribute browsing context table semi-structured data Seeing in the Clouds join visualization mining Galois connection meet Things with tag clouds object Computer Science knowledge discovery over concept lattices relation Stellenbosch information retrieval

  2. How do you find stuff on the Internet? • query concept-based browsing

  3. How do you find stuff on the Internet? Yikes! 3 370 000 results!

  4. How do you find stuff on the Internet? • query concept-based browsing lattice   

  5. How do you find stuff you didn’t look for? Retrieval: extract objects that satisfy a pre-defined criterion • query describes criterion • main operation is matching: check satisfaction against query • main goal is precision: show onlyrelevant objects Browsing: spontaneously explore a collection • focus describes current position and selection • main operation is navigation: change the focus • main goal is recall: show allrelevant objects

  6. How do you browse? focus selection (hierarchical)navigation structure

  7. How do you browsesemi-structureddata? What is semi-structured data? • What is structured data? • Structured data has... • ... a very high degree of regularity • ... an explicit, tight format (schema) • Typical examples: • spreadsheets • relational databases (SQL: structured query language)

  8. How do you browsesemi-structureddata? • What is semi-structured data? • Semi-structured data ... • ... contains both free-text and formatted fields • ... has large structural variance • ... is implicitly formatted • Typical examples: • product reviews • newspaper articles + meta-data • revision control logs

  9. How do you browsesemi-structureddata? Approach: • find a suitable abstract data representation • bag-of-words, graphs, binary relations, RDF triples, XML, ... • find a suitable hierarchy • metric spaces, graphs, concept lattices, ... • find a suitable visual representation • lists, graphs, tag clouds, city scapes, ... • find a navigation algorithm

  10. How do you represent data? Structured data is represented by n-ary relations or tables: • each object becomes a row • each column representsan attribute type • text remains unstructured

  11. How do you represent data? Structured data is represented by n-ary relations or tables: • each object becomes a row • each column representsan attribute type • text remains unstructured • set-valued attributes require normalization

  12. How do you represent data? Structured data is represented by n-ary relations or tables: • each object becomes a row • each column representsan attribute type • text remains unstructured • set-valued attributes require normalization Semi-structured data can be represented by binary relations: • text is split into words • each occurring value andword becomes an attribute • build context table: add cross if attribute applies to object • word appears in document, meta-data, references ...

  13. How do you find hierarchy in relations? Formal concept analysis: • formal context: (O, A, ~ₓ)

  14. How do you find hierarchy in relations? Formal concept analysis: • formal context: (O, A, ~ₓ) • common attributes: α(O) = {a∈A|∀o∈O:o ~ₓ a} α({08, 42} = α({08, 42} = {Fischer, browsing}

  15. How do you find hierarchy in relations? Formal concept analysis: • formal context: (O, A, ~ₓ) • common attributes: α(O) = {a∈A|∀o∈O:o ~ₓ a} • common objects: ω(A) = {o∈O|∀a∈A:o ~ₓ a} • concept: (O, A) s.t. α(O) = A ∧ ω(A) = O α({08, 42} = {Fischer, browsing} ω({Fischer,browsing} ω({Fischer,browsing} = {08,42} • extent • intent

  16. How do you find hierarchy in relations? Formal concept analysis: • formal context: (O, A, ~ₓ) • common attributes: α(O) = {a∈A|∀o∈O:o ~ₓ a} • common objects: ω(A) = {o∈O|∀a∈A:o ~ₓ a} • concept: (O, A) s.t. α(O) = A ∧ ω(A) = O  {08, 42}{F,browsing} {42}{tag} {08}{F,browsing,’00} {42}{F,G,browsing,tag,’14} • extent • intent

  17. How do you find hierarchy in relations? Formal concept analysis: • formal context: (O, A, ~ₓ) • common attributes: α(O) = {a∈A|∀o∈O:o ~ₓ a} • common objects: ω(A) = {o∈O|∀a∈A:o ~ₓ a} • concept: (O, A) s.t. α(O) = A ∧ ω(A) = O • sub-concept ordering: (O₁, A₁) ≤ (O₂, A₂) iff O₁⊆ O₂iff A₁⊇ A₂ • concept lattice: concepts of a context form a complete lattice  {08, 42}{F,browsing} {42}{tag} {08}{F,browsing,’00} {42}{F,G,browsing,tag,’14}

  18. Are we there yet? Nope. Concept lattices induce • enough structure for navigation... • ... but too much to show directly!

  19. How do you visualize concept lattices? Approach: • don’t show the lattice • use concepts as focus • visualize only focus concept • but in relation to lattice

  20. How do you visualize concepts? Approach: • don’t show the lattice • use concepts as focus • visualize only focus concept • but in relation to lattice • use extent to derive tag cloud

  21. How do youbuild tagcloudsforconcepts? • What is a tag cloud? • visual representation of text data • summarize large data set • emphasize important tags • single words or short phrases • importance reflected as size • frequency in document • number of tagged items • number of page hits • different layout methods

  22. How do youbuild tagcloudsforconcepts?  • intent looks like tag cloud... • ... but is common to all objects⇒ all tags same size • instead: collect all attributesfrom all objects in extent • can be expressed inconcept lattice: • also add extent via object identifiers • intent shown as largest tags • smaller tags are relatedinformation {08, 42}{Fischer,browsing} 08 42 20002014browsingFischer Greene tag

  23. The ConceptCloud Browser • date • controls • message • file • author by: Gillian Greene, US

  24. The ConceptCloud Browser • most prolific • contributor

  25. How do younavigatewithtag clouds? Navigation modes: • refinement: narrow the selection • select a new tag • widening: extend the selection • remove a selected tag

  26. How do younavigatewithconceptlattices? • focus • concept • tag • concept (ω({t}), α(ω({t}))) if t∈A δ(t) = (α(ω({t})), ω({t})) if t∈O Navigation modes: • refinement: narrow the selection • select a new tag: f’ = f ∧ δ(t) • widening: extend the selection • remove a selected tag • focus • concept • focus • concept

  27. How do younavigatewithconceptlattices? • tag • concept (ω({t}), α(ω({t}))) if t∈A δ(t) = (α(ω({t})), ω({t})) if t∈O Navigation modes: • refinement: narrow the selection • select a new tag: f’ = f ∧ δ(t) • widening: extend the selection • remove a selected tag: f’ = f ∨ δ(t) • focus • concept • tag • concept • focus • concept • focus • concept • focus • concept

  28. How do younavigatewithconceptlattices? • tag • concept (ω({t}), α(ω({t}))) if t∈A δ(t) = (α(ω({t})), ω({t})) if t∈O Navigation modes: • refinement: narrow the selection • select a new tag: f’ = f ∧ δ(t) • widening: extend the selection • remove a selected tag: f’ = f ∨ δ(t) f’ = ∧i∈π(f) \ {t}δ(i) • join-based widening can beuseful as well • focus • concept  • tag • concept • focus • concept

  29. Navigation in the ConceptCloud Browser

  30. Navigation in the ConceptCloud Browser

  31. Navigation in the ConceptCloud Browser

  32. The Percept Browser by: Carl Kritzinger, Fireworks

  33. Conclusions & Future Work • Semi-structured data is common but hard to analyze • Tag clouds are a good visualization approach... • ... and the combination with concept lattices makes it easy to navigate and find related information • Flexible approach, generic tool • different data sets • different types of contexts (⇒ different types of analysis) • Scalability • DBLP, IMDb, Wikipedia? • Customizability • context extraction • tool scripting

  34. Conclusions & Future Work • Semi-structured data is common but hard to analyze • Tag clouds are a good visualization approach... • ... and the combination with concept lattices makes it easy to navigate and find related information • Flexible approach, generic tool • different data sets • different types of contexts (⇒ different types of analysis) • Scalability • DBLP, IMDb, Wikipedia? • Customizability • context extraction • tool scripting

More Related