Creating knowledge bases and ontologies is a time consuming task that relies
on a manual curation. AI/NLP approaches can assist expert curators in
populating these knowledge bases, but current approaches rely on extensive
training data, and are not able to populate arbitrary complex nested knowledge
schemas.
Here we present Structured Prompt Interrogation and Recursive Extraction of
Semantics (SPIRES), a Knowledge Extraction approach that relies on the ability
of Large Language Models (LLMs) to perform zero-shot learning (ZSL) and
general-purpose query answering from flexible prompts and return information
conforming to a specified schema. Given a detailed, user-defined knowledge
schema and an input text, SPIRES recursively performs prompt interrogation
against GPT-3+ to obtain a set of responses matching the provided schema.
SPIRES uses existing ontologies and vocabularies to provide identifiers for all
matched elements.
We present examples of use of SPIRES in different domains, including
extraction of food recipes, multi-species cellular signaling pathways, disease
treatments, multi-step drug mechanisms, and chemical to disease causation
graphs. Current SPIRES accuracy is comparable to the mid-range of existing
Relation Extraction (RE) methods, but has the advantage of easy customization,
flexibility, and, crucially, the ability to perform new tasks in the absence of
any training data. This method supports a general strategy of leveraging the
language interpreting capabilities of LLMs to assemble knowledge bases,
assisting manual knowledge curation and acquisition while supporting validation
with publicly-available databases and ontologies external to the LLM.
SPIRES is available as part of the open source OntoGPT package:
this https URL monarch-initiative/ontogpt.

SPIRES 是一种基于大型语言模型的知识提取方法，使用零 - shot 学习和通用查询回答，以根据特定模式返回信息，支持知识管理和获取的手动曲调。