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Exploring highly interconnected humanities data: are faceted browsers always the answer?

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Slides from a talk given at the Annual Bliss Classification Association Lecture, Apr 2013, London, UK

Slides from a talk given at the Annual Bliss Classification Association Lecture, Apr 2013, London, UK

Published in: Technology, Education

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  • 1. Michele PasinInformation ArchitectNature Publishing Groupmichele.pasin@nature.comExploring highlyinterconnected humanitiesdata: are faceted browsersalways the answer?1
  • 2. Outline1. Background. Interaction models in searchinterfaces: retrieval model vs explorational model3. Evaluation & Conclusions. Are facetedbrowsers always the answer?2. Use-case. DJFacet, an app targeting multi-result, highly structured DH datasets2
  • 3. 1 - Background3
  • 4. Two models of interactionRetrieval Model Exploration modelvsDYNAMIC  TAXONOMIES  AND  FACETED  SEARCH,  Theory,  Practice  and  Experience.  Giovanni  Maria  Sacco,  Yannis  Tzitzikas,  Springer-‐‑‒Verlag,  2009  (Chapter  1,  ʻ‘The  Modelʼ’).4
  • 5. Retrieval model: structured searchQueryResult5
  • 6. Retrieval model: guided searchQueryResult6
  • 7. Exploration model: faceted searchQueryResult7
  • 8. Exploration model: faceted searchQueryResult8
  • 9. Facet 1value 1-1value 1-2value 1-3value 1-4.............Facet 2value 2-1value 2-2value 2-3value 2-4.............Facet 3value 3-1...........................The Retrieval model explainedInformation Space9
  • 10. Facet 1value 1-1value 1-2value 1-3value 1-4.............Facet 2value 2-1value 2-2value 2-3value 2-4.............Facet 3value 3-1...........................The Exploration model explainedInformation SpaceSelf AdaptingExploration Structures‘Dynamic Taxonomies Search Systems’10
  • 11. Faceted Browsers: key features- Widely tested, many implementations- Hearst (2002) “Finding the flow in web site search”- /facet (2006); Exhibit (2007); Humboldt (2008); Collex (2007);- Easy to use, user-centered- support both experts and non-experts- expose domain features- Implement a ‘schema-less’ approach- Nowivskie: allow to“explore lateral relationships” and “possibilities foralgorithmic serendipity in research”- Highly scalable / convergent- bottom-up classification / prevent inconclusive searches- Allow for a ‘relaxed’ faceted classification- faceted classifications vs dynamic taxonomies search systems- available structured data are often enough to bootstrap a FB11
  • 12. 2 - New Directions12
  • 13. Facet 1value 1-1value 1-2value 1-3value 1-4.............Facet 2value 2-1value 2-2value 2-3value 2-4.............Facet 3value 3-1...........................Extending the model: multiple result typesInformation SpaceResult-type(normally uniqueand stable)E.g.: cars,documents,people13
  • 14. Facet 1value 1-1value 1-2value 1-3value 1-4.............Facet 2value 2-1value 2-2value 2-3value 2-4.............Facet 3value 3-1...........................Extending the model: multiple result typesInformation SpacePeopleFacets: gender;surname; forename;title; etc...DocumentsFacets: language; date;category; place; etc...EventsFacets: date; transaction-type; possessionsexchanged; place; etc...evidenceforhaveparticipants14
  • 15. Data Model and facets in POMS15
  • 16. - Python/Django based- Easy to install / integrate- Back-end agnostic- Minimal look and feel- REST architecture- Supports pivotingAnd more:caching system, customfacets, hierarchical facets etc..DJango Facet: a Python multi-result FBShttp://www.michelepasin.org /software /16
  • 17. DJango Facet: a Python multi-result FBSfacetslist = [{appearance : {label : Person name ,uniquename : personname,model : Person ,dbfield : "name",displayfield : "name",grouping : [personinfo],} ,behaviour : [{resulttype : persons,querypath : name,},{resulttype : events,querypath : associatedpeople__name,},{resulttype : documents,querypath : associatedfactoids__associatedpeople__name,},]},]17
  • 18. Case studies: EMLOT <www.emlot.kcl.ac.uk>18
  • 19. Case studies: POMS <www.poms.ac.uk>19
  • 20. EMLOT: complex queries made simple20
  • 21. EMLOT: complex queries made simple20
  • 22. Facet:Place ofpublication:Facet:VenueName:Facet:PersonRole:Facet:Troupetype:EMLOT: complex queries.. simple?EventsSourcesPeopleTroupesVenuesTr. recordsLondon”“Phoenix/Cockpit”“Playwright”“Adult players”&&&&&&21
  • 23. Facet:Place ofpublication:Facet:VenueName:Facet:PersonRole:Facet:Troupetype:EMLOT: complex queries.. simple?EventsSourcesPeopleTroupesVenuesTr. recordsLondon”“Phoenix/Cockpit”“Playwright”“Adult players”&&&&&&Events happened at thePhoenix that involvedAdult Players andPlaywrights, and werereported in Documentspublished in London...21
  • 24. Facet:Place ofpublication:Facet:VenueName:Facet:PersonRole:Facet:Troupetype:EMLOT: complex queries.. simple?EventsSourcesPeopleTroupesVenuesTr. recordsLondon”“Phoenix/Cockpit”“Playwright”“Adult players”&&&&&&Events happened at thePhoenix that involvedAdult Players andPlaywrights, and werereported in Documentspublished in London...Events documented insources published inLondon, that are about thePhoenix and about peoplewho were simultaneouslyAdult Players andplaywrights...21
  • 25. 3 - Evaluation & Conclusions22
  • 26. Evaluation- Setup:- 8 people- face to face sessions of 30-60 minutes- recorded using screen-casting software- the performance is analysed and annotated afterwards- Purpose:- improving the general efficiency of DJFacet- testing the intuitiveness of the search and navigation facilities;- testing the comprehension of the specific facets we are using- testing the comprehension of the ‘multi-result’ approach- Tasks:- incremental difficulty- level 0: warming up, exploring the interface (facets and result types)- level 1: queries with 1 facet- level 2: queries involving 2 facets- level 3: queries involving 3 or more facets23
  • 27. Evaluation resultsComprehensionof the intendedmeaning offacets- In general, quite positive- Document-class and document-type are veryambiguous- Some of the terms within the facets are not easyto interpret: eg the ‘staging context’ event-type.Generic UIissues- Facets’ role in a search is more intuitive whenthey are open- Clear separation between controls and results- Result-type switches are not obvious, peopleconfuse them with “other” facet controlsComprehensionof thesignificance ofresults- Pivoting action is not explained properly- People with no familiarity with the domain don’tget the implicit relations between result-types- People with familiarity with the domain performquite well24
  • 28. Evaluation results: future work- Cues that help users understand the DB model:- static section in the help menu- dynamic ‘query explanation’ mechanism- via graphical diagram providing a visual representation of the query- via a natural language rendering of the query- Messages that help users notice the ‘pivoting’ action:- popups or messages before changing result-type- make this control less prominent when filters are already selected25
  • 29. Conclusions- Are FB useful in the DH?- ..definitely YES!- a plethora of reasons:- easy to use, scalable, transparent, algorithmic serendipity etc..- But.... careful with:- rich domain models (and we want to reveal this richness)- fb may quickly become complicated (eg lots of search facets)- more than one result-type- need to provide ways to tackle ambiguity- domain-specific questions:- other types of entry points to the datasets will be more effective (eginteractive visualizations)26
  • 30. ... thanks!email  me  at:  michele.pasin@nature.comwww.michelepasin.org /software /djfacet27

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