Accidental knowledge discoveries occur most frequently during capricious and unplanned search and browsing of data. This type of undirected, random, and exploratory search and browsing of data results in Serendipity – the art of unsought finding. In our previous work we extracted a set of serendipity-fostering design features for developing intelligent user interfaces on Semantic Web and Linked Data browsing environments. The features facilitate the discovery of interesting and valuable facts in (linked) data which were not initially sought for. In this work, we present an implementation of those features called FERASAT. FERASAT provides an adaptive multigraph-based faceted browsing interface to catalyze serendipity while browsing Linked Data. FERASAT is already in use within the domain of science, technology & innovation (STI) studies to allow researchers who are not familiar with Linked Data technologies to explore heterogeneous interlinked datasets in order to observe and interpret surprising facts from the data relevant to policy and innovation studies. In addition to an analysis of the related work, we describe two STI use cases in the paper and demonstrate how different serendipity design features are addressed in those use cases.