Apidays New York 2024 - The value of a flexible API Management solution for O...
INSEMTIVES Tutorial ISWC2011 - Session3
1. Crowdsourcing the annotation of
dynamic Web content at seekda
Elena Simperl, University of Innsbruck, AT
Markus Rohde, University of Siegen, DE
ISWC 2010
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3. Context
• Web services portal
– Crawls for and indexes Web Services on the Web
– Currently more than 28,500 indexed and monitored
• Problems
– Services are not annotated or described
– Limited search results and possibilities
– Web APIs need to be confirmed by users
• Goal
– Obtain more annotations by involving users in the annotation
process
– Validate existing annotations, if any
– „Catch them & keep them“
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4. Design decisions
• Different annotation methods exist
– Keywords/tags
– Categories
– Natural language descriptions
– Lightweight/fully-fledged semantic web service descriptions (e.g.
WSMO/Light, OWL-S, etc.)
– Avoid complicated and demanding annotations (limit to tags, categories and
NL descriptions)
• Use lightweight RDF ontologies in the background (e.g. to ease the
search)
• SWS annotations might be integrated in the future
– Most users are not familiar with SWS
– Difficult to integrate within the search (diverse frameworks and variants)
– May hamper performance & usability
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7. Participatory Design
• Involve (end) users in prototyping
• Users = experts of use/practice
• Needs assessment -> Requirements Analysis
• Exploit users‘ expertise and creativity in design
processes
• Integrate evaluation in design processes
• Repeated prototyping cycles
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8. Online Participatory Design
• Seekda‘s users are
– anonymous
– distributed worldwide
• Online communication via website
• Creating opportunities for online participation
– Establish appropriate OPD process design
– Develop adequate OPD tool (= dashboard)
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9. Stakeholders‘ Benefits
Users‘ Benefit Seekda‘s Benefit
• Design follows users‘ • Getting direct input from
needs users/customers
• Implement own ideas • Focussing on central
• Insights in technology user requirements
and development • Getting to know
users/customers
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10. OPD Stakeholders/ Process Roles
• Project owner
– Initiation, Management, Coordination
– Facilitation
• Research/ Observer
– Expert as neutral consultant
• Technical committee
– Developers/Designers and Users
– Process Decisions
• User committee
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11. OPD Workshop - Procedure
• In General:
1. Technical committee chooses number of features out of
forum discussions
2. Features open for user voting
3. Feature selection, implementation
4. Collect user feedback
• Duration per cycle: 6 weeks
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12. OPD Workshop – Cycles
•1st Cycle
• Beginning November: Start of workshop (dashboard, technical committee,
introduction)
• 2 weeks later: Identification of 5 most important features/wishes
• 1 week later: Selection of 2 most popular features
• Beginning of Dec: Short tasks for users
• Mid December: End of cycle, feedback analysis
• OPD Dashboard Improvements
•2nd Cycle
• Execution dates: January – March
• Goals for this cycle
• Increase motivation
• Increase activity of participants
• Focus more on usability/design and incentives
• Changes
• Tasks first
• Split into smaller parts, sequentially
• Explained through screencasts
• Example: go to the portal, search for xyz, identify redundant elements, most
important, …
• OPD Dashboard Improvements
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13. PD of the Dashboard
On-site PD workshop: Requirements for the PD dashboard
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19. OPD Workshop - Results
• Numbers
~ 250 votes
~ 160 forum posts
15-20 active users
• User Background
• Web Services experts
• Developers
• Random visitors
• Feedback/Implementation
• 18 suggested features
• 6 concrete features implemented (ongoing)
• Several implemented usability/design improvements
• Conclusions & Next Steps (ongoing)
• Introduce challenge procedures
• Ask specifically about guided processes (wizards)
• Integrate OPD workshop directly from the platform
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20. Evalution (I)
• Six interviews (~60 min) with participants
– Experiences
– General evaluation
– Critique, improvements
• Limitations of written communication -> Multi-media
• Performance problems
• Positive: Video instruction
• Improvement: Awareness features/ notifications
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21. Evaluation (II)
• Central features and Usability have been
improved
• High quality feedback from users
• Improved planning of features/
implementation based on early discussion
with users
• (Perceived) assistance/ support for
developers/ designers
• „Yeah, I think it succeeded. We got a lot of contribution from people […] and it
showed this kind of workshop can work. This kind of methods.“
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22. Challenge 1: Amazon
Mechanical Turk
Goal: Initial annotations for new and undescribed APIs
Tasks available
• Confirm document is related to a Web API (yes/no)
• Provide/improve description
• Provide and confirm (bootstrapped) tags/concepts
• Provide and confirm (bootstrapped) service categories
• Rate document quality
Parameters
• Qualification test
• Min. approval rate per worker
• Approx. Time needed per task
• Reward
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27. Challenge 1: MTurk
Simple Annotation Wizard
Phase 1
1. Setup
• Initial set of 70 Web APIs
• No qualification needed
• Approx. Time needed: 15 minutes
• Reward: 0,10$
• Description + Screencast (Walkthrough)
2. Manual evaluation (seekda)
• Main focus on description and Yes/No question
• Determine whether qualification is needed for workers
• Determine whether wizard is understandable (usability)
• Determine whether review tasks are needed
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28. Challenge 1: Mturk Phase 1
Results
Total: 70 API documents, 23 distinct workers
Initial Question (Document about Web API, Yes/No)
• 49 documents correctly annotated (70%)
• 21 as Yes, this document is about a Web API
• 28 as No, this document is not related to a Web API
Description, Category, Tags
• ~ 15 submissions including all answers (description, category, tags)
• 4 very good and extensive submissions
• 8 complete and usable submissions
Phase 1, Problems
• Spam (10% - 15%)
• Only few added category and descriptions
• Most workers did not add tags
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29. Mturk: Phase 2 Changes
• Completion time decreased to 10min
• Reward increased to 20c
• Key questions are mandatory (description, tags,
category)
• More strict evaluation criteria
– e.g.: at least, 1 category, 2 tags and a meaningful
description have to be provided.
• Submitted a batch of 100
30. MTurk: Phase 2 Results
• 27 users (only 1 from the previous batch!)
• Completion Times
– Min: 10 sec
– Max: 9 min
• 10 wrong classifications
– 5 of them are web pages with high quality annotations
• For correct classifications:
– Mostly annotated with 2 tags
– Top level category identification accurate in most cases
– Mostly meaningful descriptions
– Over 80% are accurate/satisfying
31. MTurk: Phase 2 Results
•Large number of confirmed Web APIs
–Feed back to crawler/analysis framework
–Improving the initial index quality
• Large number of categorised/tagged Services/APIs
–Feed back to bootstrapping Service
–Improved search/navigation
• Detailed service descriptions for many Services at once
–Improved user experience
–Improved user experience/satisfaction
–Attract/motivate new visitors to participate
32. Challenge 2: Mashups
• Overall Goal
– Create and add mashup(s) using services / Web APIs listed on
the seekda portal
– Annotate used Services and APIs
• Timeline
– Duration: 4 weeks
• Introduction of Task through:
– Step by step guidelines
– Set of rules
– Example walkthrough
• Reward
– Gadget (Samsung Galaxy S)
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33. Challenge 3: Long-Term
Competition
• Provide annotations – become a top contributor
• Collect Points
• Changes and/or improvements to annotations
• New annotations
• Weighting according to annotation type
• Rank contributors
• Reputation is main award
• Allow users to donate money for good annotations
• Donated money will be awarded to the top annotators
• The more and better annotations provided…
• …the higher the reputation
• …the higher the financial incentive
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34. Conclusion
• Devising motivation methods for annotating Web
services is challenging
• Different possibilities were/are being explored through
challenges
– Mechanical Turk
– Mashups Challenge
– Long-Term Competition
• Users were closely kept in the development loop
through OPD
– Ensures that implemented features are usable
– Keeps users engaged in a “community”-like way
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