  The vast scale and open-ended nature of knowledge graphs (KGs) make
exploratory search over them cognitively demanding for users. We introduce a
new technique, polymorphic lenses, that improves exploratory search over a KG
by obtaining new leverage from the existing preference models that KG-based
systems maintain for recommending content. The approach is based on a simple
but powerful observation: in a KG, preference models can be re-targeted to
recommend not only entities of a single base entity type (e.g., papers in the
scientific literature KG, products in an e-commerce KG), but also all other
types (e.g., authors, conferences, institutions; sellers, buyers). We implement
our technique in a novel system, FeedLens, which is built over Semantic
Scholar, a production system for navigating the scientific literature KG.
FeedLens reuses the existing preference models on Semantic Scholar -- people's
curated research feeds -- as lenses for exploratory search. Semantic Scholar
users can curate multiple feeds/lenses for different topics of interest, e.g.,
one for human-centered AI and another for document embeddings. Although these
lenses are defined in terms of papers, FeedLens re-purposes them to also guide
search over authors, institutions, venues, etc. Our system design is based on
feedback from intended users via two pilot surveys (n=17 and n=13,
respectively). We compare FeedLens and Semantic Scholar via a third
(within-subjects) user study (n=15) and find that FeedLens increases user
engagement while reducing the cognitive effort required to complete a short
literature review task. Our qualitative results also highlight people's
preference for this more effective exploratory search experience enabled by
FeedLens.
