In recent years, the Semantic Web has been established as one of the promising advances in the area of Knowledge Representation, receiving substantial research and development effort under the leadership of the World Wide Web Consortium. The underlying motivation is to allow computer programs to read and reason about the vast amount of data in the interconnected Web, towards advances in artificial intelligence and machine learning.
In our lab, we focus both on the creation of high-quality semantic Linked Data, an on the effective consumption of the data by potential end-users. In particular, we represent books from the Jewish Bookshelf as Linked Data and develop web apps for accessing the analyzing the data.
eLinda is an explorer for Linked Data that helps in understanding the rich content of a given RDF graph. The core functionality is an exploration path, where each step produces a bar chart (histogram) that visualizes the distribution of classes in a set of nodes (URIs). Three types of explorations are supported: subclass distribution, property distribution, and object distribution for a property of choice. eLinda is implemented as a web app and is available online.