WorkDifferentWithAI.com Academic Paper Alert!
Written by Bradley P. Allen, Lise Stork, Paul Groth
Category: “AI for IT”
Article Section: Practical AI Applications; Enterprise AI for Sales Forecasting
Publication Date: 2023-10-01
SEO Description: “Exploring role of Large Language Models in evolving Knowledge Engineering practices.”
AI-Generated Paper Summary
The academic paper, titled “Knowledge Engineering using Large Language Models”, authored by Bradley P. Allen, Lise Stork, and Paul Groth, discusses the evolving field of knowledge engineering with the emergence of advanced large language models (LLMs). The paper highlights the potential roles of LLMs in creating hybrid neuro-symbolic knowledge systems and facilitating knowledge engineering in natural language. It proposes numerous open research questions to address these directions. The authors aim to change traditional knowledge engineering approaches that primarily rely on information expressed in formal languages, emphasizing the LLMs’ proficiency in understanding and working with natural language.
Keywords
Knowledge Engineering, Large Language Models, Hybrid Neuro-Symbolic Knowledge Systems, Natural Language, Open Research Questions
Author’s Abstract
Knowledge engineering is a discipline that focuses on the creation and maintenance of processes that generate and apply knowledge. Traditionally, knowledge engineering approaches have focused on knowledge expressed in formal languages. The emergence of large language models and their capabilities to effectively work with natural language, in its broadest sense, raises questions about the foundations and practice of knowledge engineering. Here, we outline the potential role of LLMs in knowledge engineering, identifying two central directions: 1) creating hybrid neuro-symbolic knowledge systems; and 2) enabling knowledge engineering in natural language. Additionally, we formulate key open research questions to tackle these directions.