The Semantic Notebook App

In my previous post ‘Taking the digital notebook a step further‘ we talked about the various ways in which people use their notebook and how they draw connections between different ideas. We highlighted the pro’s and con’s of digital note taking on devices such as mobile phones and tablet computers. To end with we mentioned that a SerenA notebook would stand out as being unique by combining traditional note taking with novel computing techniques to make meaningful and serendipitous connections to people and information by discreetly extracting terms from the users notes using manually assigned keywords, NLP with semantic reasoning to traverse RDF data to find new and unexpected connections between people and ideas presented as Suggestions.

Our first prototype demonstrates some of our early stage ideas around the semantic notebook in a minimalistic user interface, code-named SerenA Classic. The UI is made up of the Android ICS (Ice Cream Sandwich) UI components to give the user a clean and familiar experience, something we found to be important in our early stages of testing. Below is a collection of screen shots from the SerenA Classic prototype detailing some of the key functionality.

SerenA Classic

SerenA Classic: Notebooks and Notes list

SerenA Suggestion

SerenA Classic: Suggestions and pinning to notebooks
SerenA Classic: User note

SerenA Classic: User note

By using SerenA Classic in forthcoming user study we hope to gain an understanding of how users respond to Suggestions. For example do they value them enough to pin them into their notebooks? And can they see the connections between Suggestions and their own notes? A positive outcome would be that users would find these Suggestions to be meaningful and unexpected or surprising.

When we are confident that our Suggestion ‘Engine’ is effective, we intend to develop our user interface and explore our ideas towards a delightful interface. We have a hunch that with a more playful, explorative, ambiguous and delightful interface, we can make Suggestions seem more serendipitous.

Jamie Shek
@jamieshek
Researcher
DJCAD, University of Dundee

 

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