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。

用结构化提示问询和递归语义抽取（SPIRES）：一种使用零样本学习填充知识库的方法

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

Equipping machines with comprehensive knowledge of the world's entities and
their relationships has been a long-standing goal of AI. Over the last decade,
large-scale knowledge bases, also known as knowledge graphs, have been
automatically constructed from web contents and text sources, and have become a
key asset for search engines. This machine knowledge can be harnessed to
semantically interpret textual phrases in news, social media and web tables,
and contributes to question answering, natural language processing and data
analytics. This article surveys fundamental concepts and practical methods for
creating and curating large knowledge bases. It covers models and methods for
discovering and canonicalizing entities and their semantic types and organizing
them into clean taxonomies. On top of this, the article discusses the automatic
extraction of entity-centric properties. To support the long-term life-cycle
and the quality assurance of machine knowledge, the article presents methods
for constructing open schemas and for knowledge curation. Case studies on
academic projects and industrial knowledge graphs complement the survey of
concepts and methods.

这篇文章概述了创建和管理大型知识库的基本概念和实践方法，重点介绍了发现和规范实体及其语义类型、自动提取基于实体的属性以及构建开放架构和知识管理的方法。

机器知识：全面知识库的创建和维护

Machine Knowledge: Creation and Curation of Comprehensive Knowledge  Bases

We describe Quizz, a gamified crowdsourcing system that simultaneously
assesses the knowledge of users and acquires new knowledge from them. Quizz
operates by asking users to complete short quizzes on specific topics; as a
user answers the quiz questions, Quizz estimates the user's competence. To
acquire new knowledge, Quizz also incorporates questions for which we do not
have a known answer; the answers given by competent users provide useful
signals for selecting the correct answers for these questions. Quizz actively
tries to identify knowledgeable users on the Internet by running advertising
campaigns, effectively leveraging the targeting capabilities of existing,
publicly available, ad placement services. Quizz quantifies the contributions
of the users using information theory and sends feedback to the
advertisingsystem about each user. The feedback allows the ad targeting
mechanism to further optimize ad placement.
Our experiments, which involve over ten thousand users, confirm that we can
crowdsource knowledge curation for niche and specialized topics, as the
advertising network can automatically identify users with the desired expertise
and interest in the given topic. We present controlled experiments that examine
the effect of various incentive mechanisms, highlighting the need for having
short-term rewards as goals, which incentivize the users to contribute.
Finally, our cost-quality analysis indicates that the cost of our approach is
below that of hiring workers through paid-crowdsourcing platforms, while
offering the additional advantage of giving access to billions of potential
users all over the planet, and being able to reach users with specialized
expertise that is not typically available through existing labor marketplaces.

Quizz 是一个众包系统，利用广告投放来识别知识渊博的用户并评估其能力，借助用户回答未知问题来获取新的知识，通过控制实验并引入短期激励机制，可以在成本更低的情况下以高质量的方式收集和筛选专业话题领域的信息。