With the continuous growth of the Linked Data Cloud, adequate methods to efficiently explore semantic data are increasingly required.Faceted browsing is an established technique for exploratory search.Users are given an overview of a collection's attributes that can be used to progressively refine their filter criteria and delve into the data.However, manual facet predefinition is often inappropriate for at least three reasons:Firstly, heterogeneous and large scale knowledge graphs offer a huge number of possible facets.Choosing among them may be virtually impossible without algorithmic support.Secondly, knowledge graphs are often constantly changing, hence, predefinitions need to be redone or adapted.Finally, facets are generally applied to only a subset of resources (e.g., search query results).Thus, they have to match this subset and not the knowledge graph as a whole.Precomputing facets for each possible subset is impractical except for very small graphs.We present our approach for automatic facet generation and selection over knowledge graphs.We propose methods for (1) candidate facet generation and (2) facet ranking, based on metrics that both judge a facet in isolation as well as in relation to others.We integrate those methods in an overall system workflow that also explores indirect facets, before we present the results of an initial evaluation.
