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Illustration set in a futuristic knowledge lab where the potential of Large Language Models in Knowledge Engineering is explored. The central focus is a hybrid system where the LLM, depicted as a radiant neural cloud, intertwines with symbolic logic chains. Around this central system, researchers of diverse genders and descents are engaged in various tasks, from programming to analyzing the data, showcasing the evolution of Knowledge Engineering in the age of neuro-symbolic integration.

Knowledge Engineering using Large Language Models

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.

Read the full paper here

Last updated on October 24th, 2023.