Aemoo supports exploratory search over the Web. Through a simple keyword-based search interface, users can query Aemoo about the linking network of any entity, which is collected by aggregating knowledge from diverse sources such as linked data, Wikipedia, Twitter, and Google News.

We remark that Aemoo exploits all links existing in Wikipedia: DBpedia dataset based on the Wikipedia infoboxes is only 7% of such knowledge. Furthermore, it parses streams of data (currently Twitter and Google news), identifies additional links between entities, and allows the user to explore them by keeping the interaction homogeneous.

As soon as there is a link between two entities, Aemoo shows contextualized explanations of that link to the user.

Knowledge aggregation is performed according to cognitively-sound principles through the exploitation of knowledge patterns: This has been rigorously evaluated with a user study

Aemoo was evaluated by means of controlled, task-driven user experiments in order to assess its usability, and ability to provide relevant and serendipitous information as compared to two existing tools: Google and RelFinder. The related paper, accepted for publication to the Semantic Web Journal, can be downloaded here.

Aemoo uses knowledge patterns for selecting core as well as peculiar knowledge about an entity. Users can switch from exploring core to curious knowledge at any time.

Aemoo approach based on knowledge patterns opens several opportunities for studying innovative and effective ways of managing and interacting with knowledge that can only be achieved without Semantic Web technologies. Aemoo currently demonstrates some of them, and is constantly evolving for adding new ones.