When we introduced the project, we wrote:

The Web is currently in a transition phase. After having been accessible on personal computers, it is now quickly moving to more and more ubiquity and entering in every part and moment of our lives. New devices and new ways to use them are being created. The ubiquity of the Web also creates an unseen abundance of information. Data is flowing onto the Web, created by users, generated by sensors, and stored in ever growing data farms. New ways to consume these data are still to be invented, allowing us to get only the essence of it. On-demand filtering and processing of this huge data flow is required to provide us with the information we need.

One decisive step in the transition towards an intelligent and ubiquitous Web is the availability of linked and structured data. Structured data is already present in the many databases, metadata attached to medias, and in the millions of spreadsheets created everyday across the world. The recent emergence of linked data radically changes the way structured data is being considered. By giving standard formats for the publication and interconnection of structured data, linked data transforms the Web into a giant database. However, even if the raw data is there, even if the publishing and interlinking technology is there, the transition from raw published data to interlinked semantic data still needs to be done.

DataLift wants to be a catalyst for a faster transition to the linked data Web. In order to achieve this goal, DataLift can count on the large amount of research conducted for fifteen years towards the development of the semantic Web. Standard languages to represent data and their schema, as well as many tools and programming interfaces allowing to manipulate the data are available. Now that research on the semantic Web is entering an experimental phase, DataLift will gather the most prominent results in ontology selection, data conversion, data interlinking, and semantic storage infrastructures in order to provide a comprehensive experimental platform able to accelerate the process of lifting raw published data towards interlinked semantic data.

In the course of the project, the DataLift platform will actually be used to lift existing datasets originating from a large variety of sources. DataLift will build a community of users that will test the platform on their data and give feedback on its usage. DataLift will also demonstrate the benefit of lifting data by developing innovative applications based on the usage of the data published through the platform.