Explaining and Querying Knowledge Graphs by Relatedness

We demonstrate RECAP, a tool that explains relatedness between entities in Knowledge Graphs (KGs) and implements a query by relatedness paradigm that allows to retrieve entities related to those in input. One of the peculiarities of RECAP is that it does not require any data preprocessing and can combine knowledge from multiple KGs. The underlying algorithmic techniques are reduced to the execution of SPARQL queries plus some local refinement. This makes the tool readily available on a large variety of KGs accessible via SPARQL endpoints. To show the general applicability of the tool, we will cover a set of use cases drawn from a variety of knowledge domains (e.g., biology, movies, co-authorship networks) and report on the concrete usage of RECAP in the SENSE4US FP7 project. We will underline the technical aspects of the system and give details on its implementation. The target audience of the demo includes both researchers and practitioners and aims at reporting on the benefits of RECAP in practical knowledge discovery applications.