Walking Linked Data: a Graph Traversal Approach to Explain Clusters

Link traversal is one of the biggest advantages of Linked Data, as it allows the serendipitous discovery of new knowledge thanks to the natural connections between data of different sources. Our general problem is to understand how such a property can benefit the Knowledge Discovery process: in particular, we aim at using Linked Data to explain the patterns of data that have been extracted from a typical data mining process such as clustering. The strategy we propose here is Linked Data traversal, in which we explore and build on-the-fly an unknown Linked Data graph by simply deferencing entities' URIs until we find, by following the links between entities, a valid explanation to our clusters. The experiments section gives an insight into the performance of such an approach, in terms of time and scalability, and show how the links easily gather knowledge from different data sources.