Prometheus Chatbot: Knowledge Graph Collaborative Large Language Model for Computer Components Recommendation

TL;DR

Prometheus Chatbot integrates knowledge graphs with a large language model to accurately interpret user queries and provide personalized computer component recommendations, addressing challenges in natural language understanding and entity linking.

Contribution

The paper introduces a novel KG-augmented LLM chatbot specifically designed for computer component recommendation, enhancing natural language processing and entity linking capabilities.

Findings

High accuracy in interpreting user requests.

Effective linking of entities to knowledge graph nodes.

Personalized recommendations tailored to user needs.

Abstract

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.