Dataset Discovery via Line Charts

TL;DR

This paper introduces a novel method for discovering datasets from large repositories by matching line charts to datasets using a fine-grained cross-modal relevance learning model, supported by a new benchmark and extensive evaluations.

Contribution

The paper proposes FCM, a new approach that accurately matches line charts to datasets, and creates the first benchmark for dataset discovery via line charts.

Findings

FCM surpasses baselines by 30.1% in prec@50

FCM surpasses baselines by 41.0% in ndcg@50

Effective for dataset discovery through line chart queries

Abstract

Line charts are a valuable tool for data analysis and exploration, distilling essential insights from a dataset. However, access to the underlying dataset behind a line chart is rarely readily available. In this paper, we explore a novel dataset discovery problem, dataset discovery via line charts, focusing on the use of line charts as queries to discover datasets within a large data repository that are capable of generating similar line charts. To solve this problem, we propose a novel approach called Fine-grained Cross-modal Relevance Learning Model (FCM), which aims to estimate the relevance between a line chart and a candidate dataset. To achieve this goal, FCM first employs a visual element extractor to extract informative visual elements, i.e., lines and y-ticks, from a line chart. Then, two novel segment-level encoders are adopted to learn representations for a line chart and a dataset, preserving fine-grained information, followed by a cross-modal matcher to match the learned representations in a fine-grained way. Furthermore, we extend FCM to support line chart queries generated based on data aggregation. Last, we propose a benchmark tailored for this problem since no such dataset exists. Extensive evaluation on the new benchmark verifies the effectiveness of our proposed method. Specifically, our proposed approach surpasses the best baseline by 30.1% and 41.0% in terms of prec@50 and ndcg@50, respectively.