ConvTab: A Context-Preserving, Convolutional Model for Ad-Hoc Table Retrieval

Agarwal, Vibhav; Bhardwaj, Akansha; Rosso, Paolo; Cudré-Mauroux, Philippe

doi:10.1109/bigdata52589.2021.9671828

Cited by 5 publications

(2 citation statements)

References 34 publications

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“…In Ref. [31], the authors propose a method to preserve table structure and context in semantic representations. The proposed ConvTab method, utilising Convolutional Neural Networks, enhances table classification and semantic feature generation for query-table similarity, demonstrating significant improvements in standard table retrieval metrics over existing methods.…”

Section: Word Embeddings For Table Retrievalmentioning

confidence: 99%

Leveraging Large Language Models for Sensor Data Retrieval

Berenguer,

Morejón,

Tomás

et al. 2024

Applied Sciences

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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.

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Section: Word Embeddings For Table Retrievalmentioning

confidence: 99%

Leveraging Large Language Models for Sensor Data Retrieval

Berenguer,

Morejón,

Tomás

et al. 2024

Applied Sciences

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“…Focusing on table retrieval, in [16] tables were considered as 2D images, and data were then handle by traditional neural approaches to image processing (e.g. CNNs).…”

Section: Related Workmentioning

confidence: 99%

Contextual word embeddings for tabular data search and integration

Pilaluisa

Tomás

Navarro-Colorado

et al. 2022

Neural Comput & Applic

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This paper presents a new approach to retrieve and further integrate tabular datasets (collections of rows and columns) using union and join operations. In this work, both processes were carried out using a similarity measure based on contextual word embeddings, which allows finding semantically similar tables and overcome the recall problem of lexical approaches based on string similarity. This work is the first attempt to use contextual word embeddings in the whole pipeline of table search and integration, including for the first time their use in the join operation. A comprehensive analysis of their performance was carried out on both retrieving and integrating tabular datasets, comparing them with context-free models. Column headings and cell values were used as contextual information and their impact on each task was evaluated. The results revealed that contextual models significantly outperform context-free models and a traditional weighting schema in ad hoc table retrieval. In the data integration task, contextual models also improved the results on union operation compared to context-free approaches.

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