Andrea Splendiani. Towards a new paradigm for user interaction on the semantic web to support life sciences investigation. Revolution in biology: more and more data, dna sequencing proteomics, etc. But number of new drugs is decreasing, number of active antibiotics is decreasing, food crises. Life science has become an information intensive discipline, info is naturally distributed and heterogenous. Many relevant resources in RDF - need to share and integrate. Need a new language: ontologies. Key problem: studying the connections between parts. Developed some interactive visualisation tools for biological data – users finding interesting patterns in data, even without understanding all of the nuances of the visualisation elements. RDFscape: browsing and visual queries over a triple store. Visualisation gives context, can then browse at finer detail using RDF. Can start with queries from query pattern, then modify to a specific user's need using the underlying ontology (and instance data?) Want to present the same content using different presentations, which give the user different intuitions. E.g don't always show a single URI as a single node in a graph, since it can give an inaccurate intuition of the importance of a node in a structure.
Questions we want to look at:
- Who are UI's for? level of understanding of "model" needed
- How to specify labels
- Does the "traditional" model of UCD still work? How much prior knowledge of RDF should be required?
- visualiations: control, manipulation, management, navigation
- role of middle layer in integrating different viewpoints, multiple ontologies
- who has the rights and responsibilites to craft the representation?
- role of the visualisation as part of the interface, or becoming the whole UI
Andrea: next steps. Tag clouds are visually more interesting than tables of occurrence. Can we use colour code and location to show statistical significance. Problem: how can I know which selection will lead where I want? Perhaps show neighbourhood using colour/shade to indicate costs/distances. Daniel: allow 'pioneer' users to save successful explorations for other users. Duane: how to allow users to articulate goals, and others to profit from that? How to map natural language structures to the structures of the data?
Eric: visual designers will make use of shape, colour, line weight, location, distance etc to convey meaning to the user and to direct user's attention. Duance: where do the decisions get made that influence interpretation?
We don't have to visualise everything in the dataset, maybe just focus on standout elements and summaries/statistics. Some users may resist loss of information, even if displaying all data is not useful or even feasible.