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Text Data Integration

Md Ataur Rahman, Dimitris Sacharidis, Oscar Romero, Sergi Nadal

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.

Text Data Integration

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.

Paper Structure

This paper contains 15 sections, 4 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Text Everywhere, Yet Hard to Integrate
  3. The Critical Role of Text in Data Integration
  4. Mitigating Data Sparsity
  5. Data Discovery
  6. Data Augmentation
  7. Challenges in Integrating Textual Data
  8. Objectives
  9. State of the Art
  10. Data Integration
  11. Ontology Learning and Knowledge Graph Construction
  12. Information Extraction
  13. Advanced Language Modeling: Going Beyond
  14. Open Problems and Future Directions
  15. Conclusion

Figures (4)

  • Figure 1: Discovering join-paths accross disjoint datasets via text.
  • Figure 2: Relevant Fields for Data Integration with Text.
  • Figure 3: Extended Layer Cake for OL, KGC and KR from Text (extended from BCM05).
  • Figure 4: Types of Relationships depending on the underlying Concepts.