Towards enhancing linked data retrieval in conversational UIs using Large Language Models

Mussa, Omar, Rana, Omer

, Goossens, Benoît, Orozco-terWengel, Pablo and Perera, Charith

2024. Towards enhancing linked data retrieval in conversational UIs using Large Language Models. Presented at: 25th International Conference on Web Information Systems Engineering, WISE 2024, Doha, Qatar, 2-5 December 2024. Published in: Barhamgi, Mahmoud, Wang, Hua and Wang, Xin eds. Web Information Systems Engineering – WISE. Lecture Notes in Computer Science , vol.15439 Singapore: Springer Nature, pp. 246-261. 10.1007/978-981-96-0573-6_18

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Official URL: https://doi.org/10.1007/978-981-96-0573-6_18

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.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	Springer Nature
ISBN:	9789819605729
ISSN:	0302-9743
Last Modified:	05 Dec 2025 11:30
URI:	https://orca.cardiff.ac.uk/id/eprint/182938

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