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The vast scale and open-ended nature of knowledge graphs (KGs) makeexploratory search over them cognitively demanding for users. We introduce anew technique, polymorphic lenses, that improves exploratory search over a KGby obtaining new leverage from the existing preference models that KG-basedsystems maintain for recommending content. The approach is based on a simplebut powerful observation: in a KG, preference models can be re-targeted torecommend not only entities of a single base entity type (e.g., papers in thescientific literature KG, products in an e-commerce KG), but also all othertypes (e.g., authors, conferences, institutions; sellers, buyers). We implementour technique in a novel system, FeedLens, which is built over SemanticScholar, a production system for navigating the scientific literature KG.FeedLens reuses the existing preference models on Semantic Scholar -- people'scurated research feeds -- as lenses for exploratory search. Semantic Scholarusers can curate multiple feeds/lenses for different topics of interest, e.g.,one for human-centered AI and another for document embeddings. Although theselenses are defined in terms of papers, FeedLens re-purposes them to also guidesearch over authors, institutions, venues, etc. Our system design is based onfeedback 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 userengagement while reducing the cognitive effort required to complete a shortliterature review task. Our qualitative results also highlight people'spreference for this more effective exploratory search experience enabled byFeedLens.

FeedLens: Polymorphic Lenses for Personalizing Exploratory Search over Knowledge Graphs