Towards Enhancing Linked Data Retrieval in Conversational UIs using Large Language Models

TL;DR

This paper explores integrating Large Language Models into conversational UIs to improve Linked Data retrieval, enabling more accurate SPARQL queries and better understanding of complex user queries without retraining models.

Contribution

It introduces a novel approach to incorporate LLMs into conversational interfaces, enhancing RDF data extraction and query accuracy without the need for retraining.

Findings

Enhanced RDF entity extraction accuracy

Improved system expressivity for complex queries

Better context-aware interactions in web systems

Abstract

Despite the recent broad adoption of Large Language Models (LLMs) across various domains, their potential for enriching information systems in extracting and exploring Linked Data (LD) and Resource Description Framework (RDF) triplestores has not been extensively explored. This paper examines the integration of LLMs within existing systems, emphasising the enhancement of conversational user interfaces (UIs) and their capabilities for data extraction by producing more accurate SPARQL queries without the requirement for model retraining. Typically, conversational UI models necessitate retraining with the introduction of new datasets or updates, limiting their functionality as general-purpose extraction tools. Our approach addresses this limitation by incorporating LLMs into the conversational UI workflow, significantly enhancing their ability to comprehend and process user queries effectively. By leveraging the advanced natural language understanding capabilities of LLMs, our method improves RDF entity extraction within web systems employing conventional chatbots. This integration facilitates a more nuanced and context-aware interaction model, critical for handling the complex query patterns often encountered in RDF datasets and Linked Open Data (LOD) endpoints. The evaluation of this methodology shows a marked enhancement in system expressivity and the accuracy of responses to user queries, indicating a promising direction for future research in this area. This investigation not only underscores the versatility of LLMs in enhancing existing information systems but also sets the stage for further explorations into their potential applications within more specialised domains of web information systems.