Leveraging Large Language Models for Sensor Data Retrieval
Por favor, use este identificador para citar o enlazar este ítem:
http://hdl.handle.net/10045/141551
Título: | Leveraging Large Language Models for Sensor Data Retrieval |
---|---|
Autor/es: | Berenguer, Alberto | Morejón, Adriana | Tomás, David | Mazón, Jose-Norberto |
Grupo/s de investigación o GITE: | Procesamiento del Lenguaje y Sistemas de Información (GPLSI) | Web and Knowledge (WaKe) |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos |
Palabras clave: | Sensor data | Large language models | Word embeddings | Data retrieval | FAIR principles |
Fecha de publicación: | 15-mar-2024 |
Editor: | MDPI |
Cita bibliográfica: | Applied Sciences. 2024, 14(6): 2506. https://doi.org/10.3390/app14062506 |
Resumen: | The growing significance of sensor data in the development of information technology services finds obstacles due to disparate data presentations and non-adherence to FAIR principles. This paper introduces a novel approach for sensor data gathering and retrieval. The proposal leverages large language models to convert sensor data into FAIR-compliant formats and to provide word embedding representations of tabular data for subsequent exploration, enabling semantic comparison. The proposed system comprises two primary components. The first focuses on gathering data from sensors and converting it into a reusable structured format, while the second component aims to identify the most relevant sensor data to augment a given user-provided dataset. The evaluation of the proposed approach involved comparing the performance of various large language models in generating representative word embeddings for each table to retrieve related sensor data. The results show promising performance in terms of precision and MRR (0.90 and 0.94 for the best-performing model, respectively), indicating the system’s ability to retrieve pertinent sensor data that fulfil user requirements. |
Patrocinador/es: | This research was partially funded by MCIN/AEI/10.13039/501100011033 and by the European Union Next Generation EU/PRTR as part of the projects TED2021130890B-C21 and PID2021-122263OB-C22, as well a by REMARKABLE project (HORIZON-MSCA-2021-SE-0 action number: 101086387). The APC was funded by CIAICO/2022/019 project from Generalitat Valenciana. Alberto Berenguer has a contract for predoctoral training with “Generalitat Valenciana” and the European Social Fund, funded by grant number ACIF/2021/507. |
URI: | http://hdl.handle.net/10045/141551 |
ISSN: | 2076-3417 |
DOI: | 10.3390/app14062506 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.3390/app14062506 |
Aparece en las colecciones: | INV - GPLSI - Artículos de Revistas INV - WaKe - Artículos de Revistas Investigaciones financiadas por la UE |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
---|---|---|---|---|
Berenguer_etal_2024_ApplSci.pdf | 1,1 MB | Adobe PDF | Abrir Vista previa | |
Todos los documentos en RUA están protegidos por derechos de autor. Algunos derechos reservados.