Motivated by the scarcity of high-quality labeled biomedical text, as well as the success of data programming, we introduce KRISS-Search. By leveraging the Unified Medical Language Systems (UMLS) ontology, KRISS-Search addresses an interactive few-shot span recommendation task that we propose. We first introduce unsupervised KRISS-Search and show that our method outperforms existing methods in identifying spans that are semantically similar to a given span of interest, with >50% AUPRC improvement relative to PubMedBERT. We then introduce supervised KRISS-Search, which leverages human interaction to improve the notion of similarity used by unsupervised KRISS-Search. Through simulated human feedback, we demonstrate an enhanced F1 score of 0.68 in classifying spans as semantically similar or different in the low-label setting, outperforming PubMedBERT by 2 F1 points. Finally, supervised KRISS-Search demonstrates competitive or superior performance compared to PubMedBERT in few-shot biomedical named entity recognition (NER) across five benchmark datasets, with an average improvement of 5.6 F1 points. We envision KRISS-Search increasing the efficiency of programmatic data labeling and also providing broader utility as an interactive biomedical search engine.