Section: Research Program
Semantics-driven Data Manipulation
Participants : Emmanuel Pietriga, Caroline Appert, Hande Ozaygen, Mengying Du, Hugo Romat.
The Web of Data has been maturing for the last fifteen years and is starting to gain adoption across numerous application domains (Figure 1 ). Now that most foundational building blocks are in place, from knowledge representation, inference mechanisms and query languages  , all the way up to the expression of data presentation knowledge  and to mechanisms like look-up services  or spreading activation  , we need to pay significant attention to how human beings are going to interact with this new Web, if it is to “reach its full potential”  .
To be successful, interaction paradigms that let users navigate and manipulate data on the Web have to be tailored to the radically different way of browsing information enabled by it, where users directly interact with the data rather than with monolithic documents. The general research question addressed in this part of our research program is how to design novel interaction techniques that help users manipulate their data more efficiently. By data manipulation, we mean all low-level tasks related to manually creating new content, modifying and cleaning existing content, merging data from different sources, establishing connections between datasets, categorizing data, and eventually sharing the end results with other users; tasks that are currently considered quite tedious because of the sheer complexity of the concepts, data models and syntax, and the interplay between all of them.
Our approach is based on the conviction that there is a strong potential for cross-fertilization, as mentioned earlier: on the one hand, user interface design is essential to the management and understanding of webs of data; on the other hand, interlinked datasets enriched with even a small amount of semantics can help create more powerful user interfaces, that provide users with the right information at the right time.
We envision systems that focus on the data themselves, exploiting the underlying semantics and structure in the background rather than exposing them – which is what current user interfaces for the Web of Data often do. We envision interactive systems in which the semantics and structure are not exposed directly to users, but serve as input to the system to generate interactive representations that convey information relevant to the task at hand and best afford the possible manipulation actions.