KNOW: A Real-World Ontology for Knowledge Capture with Large Language Models

TL;DR

This paper introduces KNOW, a comprehensive ontology designed to enhance large language models by capturing everyday human knowledge, focusing on universals like spacetime and social concepts, with practical tools for software integration.

Contribution

The paper presents KNOW, the first ontology tailored for real-world knowledge augmentation of LLMs, emphasizing simplicity, universality, and developer-friendly software libraries.

Findings

KNOW effectively captures essential human universals.

The ontology improves LLM performance in real-world tasks.

Software libraries facilitate easy integration into applications.

Abstract

We present KNOW--the Knowledge Navigator Ontology for the World--the first ontology designed to capture everyday knowledge to augment large language models (LLMs) in real-world generative AI use cases such as personal AI assistants. Our domain is human life, both its everyday concerns and its major milestones. We have limited the initial scope of the modeled concepts to only established human universals: spacetime (places, events) plus social (people, groups, organizations). The inclusion criteria for modeled concepts are pragmatic, beginning with universality and utility. We compare and contrast previous work such as Schema.org and Cyc--as well as attempts at a synthesis of knowledge graphs and language models--noting how LLMs already encode internally much of the commonsense tacit knowledge that took decades to capture in the Cyc project. We also make available code-generated software libraries for the 12 most popular programming languages, enabling the direct use of ontology concepts in software engineering. We emphasize simplicity and developer experience in promoting AI interoperability.