Constructing Experts Profiles from Linked Open Data

Inspired from Linked Open Data (LOD) initiative, Latif et al. have developed a tool which can establish links between authors of digital journals with relevant semantic resources available in LOD [1]. The proposed system is able to disambiguate authors and can: 1) locate, 2) retrieve, and 3) structure the relevant semantic resources. Furthermore, the system constructs comprehensive aspect oriented authors’ profiles from heterogeneous datasets of LOD on the fly. They investigated the potentials of such an approach on a digital journal known as Journal of Universal Computer Science (J.UCS). It is their strong belief that this kind of applications can motivate researchers and developers to investigate different application areas where Linked Open Data can contribute, bring added value, and can take the idea of open access further.

Because of the strong resemblance to our application, some interesting aspects of their approach will be used in this project.

Open Innovation Problem Solver Search

This project presents a case in which technical scientists are linked to each other. The intention is to connect scientists that have similar interests. In “Open Innovation” experts of different institutions and companies try to collaborate and increase the rate of technological innovation. M. Stankovic wrote a paper [1] to check if linked data contributes in these efforts.