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Creating knowledge bases and ontologies is a time consuming task that relieson a manual curation. AI/NLP approaches can assist expert curators inpopulating these knowledge bases, but current approaches rely on extensivetraining data, and are not able to populate arbitrary complex nested knowledgeschemas.  Here we present Structured Prompt Interrogation and Recursive Extraction ofSemantics (SPIRES), a Knowledge Extraction approach that relies on the abilityof Large Language Models (LLMs) to perform zero-shot learning (ZSL) andgeneral-purpose query answering from flexible prompts and return informationconforming to a specified schema. Given a detailed, user-defined knowledgeschema and an input text, SPIRES recursively performs prompt interrogationagainst GPT-3+ to obtain a set of responses matching the provided schema.SPIRES uses existing ontologies and vocabularies to provide identifiers for allmatched elements.  We present examples of use of SPIRES in different domains, includingextraction of food recipes, multi-species cellular signaling pathways, diseasetreatments, multi-step drug mechanisms, and chemical to disease causationgraphs. Current SPIRES accuracy is comparable to the mid-range of existingRelation Extraction (RE) methods, but has the advantage of easy customization,flexibility, and, crucially, the ability to perform new tasks in the absence ofany training data. This method supports a general strategy of leveraging thelanguage interpreting capabilities of LLMs to assemble knowledge bases,assisting manual knowledge curation and acquisition while supporting validationwith publicly-available databases and ontologies external to the LLM.  SPIRES is available as part of the open source OntoGPT package:https://github.com/ monarch-initiative/ontogpt.

Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learning