- Various formats of semantically linked data (RDF, etc.)
- The semantic web is limited to a scientific domain
- Data is raw, and has inconsistencies
- Most of the time you are not interested in the whole set of properties
- The vast amount of data and types often leads to disorientation
- Operate on linked data
- See the data (connections, coherences, etc.)
- Analyze data (compare common properties of a set)
- Lowered barriers to ‘work’ with semantically rich data and in turn provide new data.
- Definition of an object model that that provides a ‘view’ on linked data.
- Typed collections featuring properties can be distilled from linked data sources (RDF, Freebase, Last.fm, Delicious)
- A generic browser interface that allows to navigate and analyze such data
- Such a browser should allow filtering (faceted browsing principle) and provide various types of visualization to compare and analyze data.
- Describe a feasible approach to convert various data sources
– Can this be done automatically, semi-automatically?
– Because data is semantically annotated (RDF, OWL, etc.) software can reason about it and autonomously convert it to a defined collection format.
- Rich web interface
- HTML 5
- Visualizations are based on Canvas using the Processing.js visualization framework
- Ruby on Rails
- REDIS (fast storage engine)
- Elastic Lists (Moritz Stefaner)
- Pivot (Microsoft)
- Parallax (Freebase Browser)
- SIMILE Projects at the MIT
- ASKKEN – Visual Freebase Resource browser
- Informationsvisualisierung_im_Semantic Web / Michael Aufreiter
At the heart of Pivot are “Collections.” They combine large groups of similar items on the Internet, so we can begin viewing the relationships between individual pieces of information in a new way. By visualizing hidden patterns, Pivot enables users to discover new insights while interacting with thousands of things at once.