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Browsing Highly
Interconnected Humanities
 Databases Through Multi-
 Result Faceted Browsers



            Michele Pasin
            Department of Digital Humanities
            Kings College, London

            michele.pasin@kcl.ac.uk
            www.michelepasin.org/software/djfacet
Summary




1. Background. Interaction models in search
interfaces: retrieval model vs explorational model


2. Approach. DJFacet, a multi-result dynamic
taxonomies search system


3. Evaluation. Strengths and weaknesses; future
work
Background: two models of interaction



Retrieval Model                                                          Exploration model


                                                                vs


        	
            	
            	
            	
             	
              	
            	
     	
     	
     	
     	
     	
  
    	
         	
            	
            	
            	
       	
      	
            	
  
Retrieval model: structured search

                        Query




                                     Result
Explorational model: faceted search systems




                                      Result




      Query
Explorational model: faceted search systems




                 • Tested successfully in several
                 areas / with different back-ends

                 • Easy to use, user-centered
                 • Implement a schema-less approach
                 • Highly scalable / convergent
                 • Expose domain features
The Retrieval model explained


Facet #1            Information Space
facet-value #1
facet-value #2
facet-value #3
facet-value #4
.............


Facet #2
facet-value #1
facet-value #2
facet-value #3
facet-value #4
.............


Facet #3
facet-value #1
.............
..............
The Explorational model explained


Facet #1            Information Space
facet-value #1
facet-value #2
facet-value #3
facet-value #4
.............


Facet #2
facet-value #1
facet-value #2
facet-value #3
facet-value #4
.............


Facet #3
                   Self Adapting
facet-value #1
.............      Exploration Structures
..............
Extending the model: multiple result types


Facet #1            Information Space
facet-value #1
facet-value #2
facet-value #3
facet-value #4
.............


Facet #2
facet-value #1
facet-value #2
facet-value #3
facet-value #4
.............


Facet #3                 Result-type       E.g.: cars,
                        (normally unique   documents,
facet-value #1          and stable)
.............
                                           people
..............
Extending the model: multiple result types


Facet #1             Information Space
facet-value #1
facet-value #2               Events
facet-value #3               Facets: date; transaction-
facet-value #4               type; spiritual benefits;
.............                place; etc...


Facet #2                 reference
                         to                               evidence
facet-value #1
facet-value #2
                                                          for
facet-value #3
facet-value #4
.............
                   People                       Documents
                  Facets: gender;               Facets: language; date;
Facet #3          surname; forename;
                  title; etc...
                                                category; place; etc...


facet-value #1
.............
..............
DJango Facet: a Python multi-result FSS



                       - Python/Django based

                       - Easy to install / integrate

                       - Back-end agnostic

                       - Minimal look and feel

                       - REST architecture

                       - Supports pivoting

                       - Includes a caching system
DJango Facet: a Python multi-result FSS

facetslist = [
           {'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',
                            },
                     ]},
                ]
Case studies: POMS   <www.poms.ac.uk>
Case studies: EMLOT   <www.emlot.kcl.ac.uk>
EMLOT: complex queries made simple
EMLOT: complex queries.. still complex!

Facet:                               Tr. records
Place of     “London”
publication:
                &&                   Events
Facet:
Venue       “Phoenix/
            Cockpit”                 Sources
Name:

Facet:          &&                   People
Person
Role:       “Playwright”
                                     Troupes
Facet:         &&
Troupe                               Venues
            “Adult players”
type:
Evaluation

- 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

- Setup:
 - 8 people
 - face to face sessions of 30-60 minutes
 - recorded using screen-casting software
 - the performance is analysed and annotated afterwards

- 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 facets
Evaluation results


 Comprehension     - In general, quite positive
 of the intended   - Document-class and document-type are very
 meaning of        ambiguous
 facets            - Some of the terms within the facets are not easy
                   to interpret: eg the ‘staging context’ event-type.

 Generic UI        - Facets’ role in a search is more intuitive when
 issues            they are open
                   - Clear separation between controls and results
                   - Result-type switches are not obvious, people
                   confuse them with “other” facet controls

 Comprehension     - Pivoting action is not explained properly
 of the            - People with no familiarity with the domain don’t
 significance of   get the implicit relations between result-types
 results           - People with familiarity with the domain perform
                   quite well
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 before changing result-type
 - make this control less prominent when filters are already selected


- An evaluation on the other DB is planned for September:
 - new version of DJFacet available soon
 - more details about the evaluation to be published in autumn
... thanks!


  	
     	
     	
  

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DH11: Browsing Highly Interconnected Humanities Databases Through Multi-Result Faceted Browsers

  • 1. Browsing Highly Interconnected Humanities Databases Through Multi- Result Faceted Browsers Michele Pasin Department of Digital Humanities Kings College, London michele.pasin@kcl.ac.uk www.michelepasin.org/software/djfacet
  • 2. Summary 1. Background. Interaction models in search interfaces: retrieval model vs explorational model 2. Approach. DJFacet, a multi-result dynamic taxonomies search system 3. Evaluation. Strengths and weaknesses; future work
  • 3. Background: two models of interaction Retrieval Model Exploration model vs                                        
  • 4. Retrieval model: structured search Query Result
  • 5. Explorational model: faceted search systems Result Query
  • 6. Explorational model: faceted search systems • Tested successfully in several areas / with different back-ends • Easy to use, user-centered • Implement a schema-less approach • Highly scalable / convergent • Expose domain features
  • 7. The Retrieval model explained Facet #1 Information Space facet-value #1 facet-value #2 facet-value #3 facet-value #4 ............. Facet #2 facet-value #1 facet-value #2 facet-value #3 facet-value #4 ............. Facet #3 facet-value #1 ............. ..............
  • 8. The Explorational model explained Facet #1 Information Space facet-value #1 facet-value #2 facet-value #3 facet-value #4 ............. Facet #2 facet-value #1 facet-value #2 facet-value #3 facet-value #4 ............. Facet #3 Self Adapting facet-value #1 ............. Exploration Structures ..............
  • 9. Extending the model: multiple result types Facet #1 Information Space facet-value #1 facet-value #2 facet-value #3 facet-value #4 ............. Facet #2 facet-value #1 facet-value #2 facet-value #3 facet-value #4 ............. Facet #3 Result-type E.g.: cars, (normally unique documents, facet-value #1 and stable) ............. people ..............
  • 10. Extending the model: multiple result types Facet #1 Information Space facet-value #1 facet-value #2 Events facet-value #3 Facets: date; transaction- facet-value #4 type; spiritual benefits; ............. place; etc... Facet #2 reference to evidence facet-value #1 facet-value #2 for facet-value #3 facet-value #4 ............. People Documents Facets: gender; Facets: language; date; Facet #3 surname; forename; title; etc... category; place; etc... facet-value #1 ............. ..............
  • 11. DJango Facet: a Python multi-result FSS - Python/Django based - Easy to install / integrate - Back-end agnostic - Minimal look and feel - REST architecture - Supports pivoting - Includes a caching system
  • 12. DJango Facet: a Python multi-result FSS facetslist = [ {'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', }, ]}, ]
  • 13. Case studies: POMS <www.poms.ac.uk>
  • 14. Case studies: EMLOT <www.emlot.kcl.ac.uk>
  • 15. EMLOT: complex queries made simple
  • 16. EMLOT: complex queries.. still complex! Facet: Tr. records Place of “London” publication: && Events Facet: Venue “Phoenix/ Cockpit” Sources Name: Facet: && People Person Role: “Playwright” Troupes Facet: && Troupe Venues “Adult players” type:
  • 17. Evaluation - 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 - Setup: - 8 people - face to face sessions of 30-60 minutes - recorded using screen-casting software - the performance is analysed and annotated afterwards - 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 facets
  • 18. Evaluation results Comprehension - In general, quite positive of the intended - Document-class and document-type are very meaning of ambiguous facets - Some of the terms within the facets are not easy to interpret: eg the ‘staging context’ event-type. Generic UI - Facets’ role in a search is more intuitive when issues they are open - Clear separation between controls and results - Result-type switches are not obvious, people confuse them with “other” facet controls Comprehension - Pivoting action is not explained properly of the - People with no familiarity with the domain don’t significance of get the implicit relations between result-types results - People with familiarity with the domain perform quite well
  • 19. 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 before changing result-type - make this control less prominent when filters are already selected - An evaluation on the other DB is planned for September: - new version of DJFacet available soon - more details about the evaluation to be published in autumn
  • 20. ... thanks!