Martin Voigt presents a concept for developing a user interface and backend system to help end-users identify interesting resources from large semantic web datasets. The presentation outlines motivations like the growing amount of data and end-users' lack of programming skills. An iterative user study informed the design of an intuitive interface with visualization and interaction techniques. The backend uses objective measures, annotations, and recommendations to filter and analyze data accessible through a RESTful API. The goal is to assist end-users in exploring semantic web data through understandable visualizations and easy interaction tasks.

1. Madrid, 14.06.2013 Martin Voigt
How Could End-Users Identify
Interesting Resources?
Attract them visually!

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Structure
Motivation and problems
Iterative development of a user interface
Demo
Concept and implementation of a back-end
Conclusion
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Growing amount of data
Motivation
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[Key2012]

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How to get insights from the data?
Motivation
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How to get insights from the data?
Information Visualization!
Motivation
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Motivation
BUT
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InfoVis for Semantic Web data?

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Problem
End-users have no idea about creating effective InfoVis
and have no programming skills.
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Overall concept of VizBoard
Semantic-assisted InfoVis-Process [Voigt2013]
How to find interesting resources?
objective vs. subjective interestingness [Silberschatz1996]
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Data Upload
Data Pre-
Selection
Data & Vis
Selection
Visualization
Configuration
Perception &
Internalization
Data
Augmentation
Visualization
Recommendation
Visualization
Integration
Knowledge
Externalization
Data Reduction
SystemUser
Human Interaction
System Interaction
31 5 7 9
2 4 6 8 10
Data Upload
Data Pre-
Selection
Data & Vis
Selection
Visualization
Configuration
Perception &
Internalization
User
Human Interaction
31 5 7 9

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Main problems to solve
1st: Interactive, understandable user interface
2nd: Backend techniques to identify relevant resources
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Structure
Motivation and problems
Iterative development of a user interface
Demo
Concept and implementation of a back-end
Conclusion
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User Interface Concept
Prerequisites - Visualization
node link diagrams vs. nested views
insufficient screen estate  interaction required
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User Interface Concept
Prerequisites - Interaction
use widely adapted techniques
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User Interface Concept
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User Interface Concept
User Study - Setup
Task Load Index to track efforts,
e.g., mental or temporal
10 tasks based on “Visual Information-seeking
Mantra” [Shneiderman1996]
Questionnaire for additional feedback
10 students with less/no SemWeb knowledge
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User Interface Concept
User Study – Results
+ node link to get to know the data structure
+ navigation history and bookmarking helpful
- too much information shown
- too many options
- SW knowledge required
 functionality vs. user satisfaction
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User Interface Concept
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Structure
Motivation and problems
Iterative development of a user interface
Demo
Concept and implementation of a back-end
Conclusion
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Structure
Motivation and problems
Iterative development of a user interface
Demo
Concept and implementation of a back-end
Conclusion
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Assisting Backend
Implementation
• Java Jersey REST WS
• Jena TDB
• RMonto & RapidMiner
• JGraphT
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Data Repository
Storage, Filtering,
Recommendation
Augmentation
Data Access
Analyzer
Storage
Annotator
RESTful API
Pivoting
Filter
upload get | filter
1 2
3

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Structure
Motivation and problems
Iterative development of a user interface
Demo
Concept and implementation of a back-end
Conclusion
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Sum it up!
End-user need assistances to identify interesting
SemWeb resources (in big datasets)
Approach using objective measures, understandable
visualizations, and easy interaction
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Sum it up!
SWIs for end-users should be task-specific as possible
less is more: interaction,
information, SW intrinsics
Visualizing huge SW datasets in browsers is cumbersome
Current tasks
Subjective interestingness
Planning an advanced user study
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Thanks for listening. Questions?
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References
[Cyganiak2011] Cyganiak, R. & Jentzsch, A.: Linking Open Data cloud diagram.
http://lod-cloud.net/, 2011
[Key2012] Key, A.; Howe, B.; Perry, D. & Aragon, C.: VizDeck: self-organizing
dashboards for visual analytics. Proceedings of the 2012 ACM SIGMOD
International Conference on Management of Data, ACM, 2012, 681-684
[Shneiderman1996] Shneiderman, B.: The eyes have it: a task by data type
taxonomy for information visualizations. Visual Languages, 1996. Proceedings.,
IEEE Symposium on, 1996, 336-343
[Silberschatz1996] A. Silberschatz, A. & Tuzhilin, A.: What makes patterns
interesting in knowledge discovery systems. Knowledge and Data Engineering,
IEEE Transactions on, 8(6):970 -974, dec 1996.
[Voigt2013] M. Voigt, S. Pietschmann, K. Meißner: A Semantics-Based, End-
User-Centered Information Visualization Process for Semantic Web Data. Semantic
Models for Adaptive Interactive Systems, Springer, 2013
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