Text Data Integration

2026-03-27

AI Summary

This study addresses the challenge of effectively integrating structured data with unstructured text, a longstanding barrier in data management. It presents the first systematic argument for the necessity of textual data integration and introduces a unified framework that synergistically combines natural language processing, knowledge extraction, and traditional data integration techniques. By leveraging semantic alignment, the framework achieves deep integration between textual content and structured schemas, thereby tackling key challenges inherent in heterogeneous data integration. The work comprehensively surveys existing methodologies and outstanding issues, establishing a theoretical foundation for the emerging field of textual data integration and offering clear guidance for future research and practical implementation.

Abstract

Data comes in many forms. From a shallow perspective, they can be viewed as being either in structured (e.g., as a relation, as key-value pairs) or unstructured (e.g., text, image) formats. So far, machines have been fairly good at processing and reasoning over structured data that follows a precise schema. However, the heterogeneity of data poses a significant challenge on how well diverse categories of data can be meaningfully stored and processed. Data Integration, a crucial part of the data engineering pipeline, addresses this by combining disparate data sources and providing unified data access to end-users. Until now, most data integration systems have leaned on only combining structured data sources. Nevertheless, unstructured data (a.k.a. free text) also contains a plethora of knowledge waiting to be utilized. Thus, in this chapter, we firstly make the case for the integration of textual data, to later present its challenges, state of the art and open problems.

Problem

• Data Integration
• Unstructured Data
• Text Data
• Heterogeneous Data
• Structured Data

Innovation

• Text Data Integration
• Unstructured Data
• Data Integration
• Knowledge Extraction
• Heterogeneous Data