Comprehending Semantic Types in JSON Data with Graph Neural Networks

TL;DR

This paper introduces a graph neural network approach to predict semantic types in JSON data by leveraging JSON Path structures, enhancing understanding of complex JSON documents for data processing tasks.

Contribution

The work extends semantic type prediction from relational to JSON data, utilizing graph neural networks to capture structural information in JSON collections.

Findings

Model outperforms existing state-of-the-art in several cases

Demonstrates effective understanding of complex JSON structures

Potential applications in JSON data processing tasks

Abstract

Semantic types are a more powerful and detailed way of describing data than atomic types such as strings or integers. They establish connections between columns and concepts from the real world, providing more nuanced and fine-grained information that can be useful for tasks such as automated data cleaning, schema matching, and data discovery. Existing deep learning models trained on large text corpora have been successful at performing single-column semantic type prediction for relational data. However, in this work, we propose an extension of the semantic type prediction problem to JSON data, labeling the types based on JSON Paths. Similar to columns in relational data, JSON Path is a query language that enables the navigation of complex JSON data structures by specifying the location and content of the elements. We use a graph neural network to comprehend the structural information within collections of JSON documents. Our model outperforms a state-of-the-art existing model in several cases. These results demonstrate the ability of our model to understand complex JSON data and its potential usage for JSON-related data processing tasks.