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Thematizer

enriching cultural information online

Problem:

There are a lot of cultural data (meta-data, texts, videos, photos) available to the community, in Open Data format or not, that are not valued and sleep in data silos. These data could be used, non-exhaustively, in the areas of tourism (services or products creations highlighting the authenticity of the visited area) or in museums (creation of thematic visits based on visitors profiles)

Proposition:

We propose to work on an application able to request different local specialized cultural datasets and make links, through the result, with the huge, global, universal, Wikipedia and Google Map to enrich the cultural information returned to the visitor.

Prototype 1 (Friday):

One HTML page with a search text box and a button. It requests Wikipedia with the value, collect the JSON page's content, parse the Table of Content in order to keep only level 1 headers, display the result in both vertical list and word cloud.

Prototype 2 (Saturday):

One HTML page accessing the dataset from the Mediathèque of Valais (http://xml.memovs.ch/oai/oai2.php?verb=ListIdentifiers&metadataPrefix=oai_dc&set=k), getting all the "qdc" XML pages and displaying them in a vertical list. When you click on one of those topics, on the right of the page you will get some information about the topic, one image (if existing) and a cloud of descriptions. Clicking on one of the descriptions will then request Wikipedia with that value and display the content. If we have enough time we will also get a location tag from the Mediathèque XML file and then display a location on Google Map.

Demo

Resources (examples, similar applications, etc.):

This idea is based on a similar approach that has been developed during Museomix 2014 : http://www.museomix.org/prototypes/museochoix/ . We are trying to figure out how to extend this idea to other contexts than the museums and to other datasets than those proposed by that particular museum.

Data

Example of made requests:

Other potentially interesting datasets for future work:

Team

Links