Knowledge Engineering using Large Language Models
This work addresses foundational issues in knowledge engineering for AI researchers and practitioners, but it is incremental as it outlines potential roles rather than presenting new results.
The paper tackles the challenge of integrating large language models (LLMs) into knowledge engineering, proposing two directions: hybrid neuro-symbolic systems and natural language-based knowledge engineering, while outlining open research questions.
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.