  Knowledge graphs (KGs) are essential in applications such as network
alignment, question-answering, and recommender systems (RSs) since they offer
structured relational data that facilitate the inference of indirect
relationships. However, the development of KG-based RSs capable of processing
user inputs in natural language faces significant challenges. Firstly, natural
language processing units must effectively handle the ambiguity and variability
in human language to interpret user intents accurately. Secondly, the system
must precisely identify and link entities, like product names, to their
corresponding nodes in KGs. To overcome these challenges, supported by Lenovo,
we developed a novel chatbot called "Prometheus," which integrates a KG with a
large language model (LLM), specifically designed for recommending computer
components. This chatbot can accurately decode user requests and deliver
personalized recommendations derived from KGs, ensuring precise comprehension
and response to their computer setup needs.
