Source-linked AI summary
Text Data Integration
Md Ataur Rahman, Dimitris Sacharidis, Oscar Romero, Sergi Nadal
TL;DR
Integrating structured and unstructured data remains difficult because text lacks formal schemas and must be conceptualized before it can be aligned with structured sources. This chapter surveys the challenges and techniques involved, including ontology learning, information extraction, and language-model-based approaches. It presents textual data as useful for mitigating sparsity, discovering join-paths, and augmenting structured data, while identifying unresolved integration challenges.
Problem
Text contains valuable knowledge, but its schemaless and semantically heterogeneous nature makes it difficult to align with structured sources in existing integration systems.
Method
The chapter surveys data integration, information extraction, ontology learning, knowledge graphs, and language-model approaches for conceptualizing and integrating textual data.
Results
Textual data supports data sparsity mitigation, join-path discovery across disjoint datasets, and augmentation through newly extracted concepts and relationships.
Takeaways & Limitations
Text integration combines methods from multiple disciplines to provide richer unified views of heterogeneous data sources.
Takeaways & Limitations
Textual-data integration remains constrained by heterogeneity, semantic ambiguity, scalability, schema evolution, and knowledge representation challenges.
Abstract
from arXiv · showhide
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.
1.1 Introduction
Integrating textual data with structured sources is needed because text contains valuable knowledge but is difficult to represent and align with schemas. The chapter motivates three benefits—mitigating data sparsity, discovering join-paths, and augmenting data—while outlining major integration challenges and approaches.
- 1.1.1 Text Everywhere, Yet Hard to Integrate: Textual data is widely available and valuable, but integrating it with structured sources requires extracting structured information from plain text.State-of-the-art platforms often store textual sources as plain instances, creating a need for additional conceptualization before integration.
- 1.1.2.1 Mitigating Data Sparsity: Integrating text can mitigate data sparsity by supplying missing values that arise when integrated structured sources provide only partial views.Clinical texts can be conceptualized and integrated with structured health datasets to enrich missing disease attributes.
- 1.1.2.2 Data Discovery: Textual sources can enable data discovery by revealing conceptual links and join-paths between independently generated datasets without explicit connections.The chapter illustrates this with clinical books supplying inferred concepts and relationships between disjoint disease and complication datasets.
- 1.1.2.3 Data Augmentation: Textual data can augment structured sources by introducing concepts and relationships, such as an associative prescription table linking patients to medications.Medical notes provide relationships that bridge two tables lacking common attributes and support a unified query view.
- 1.1.3 Challenges in Integrating Textual Data: The chapter identifies heterogeneity, semantic ambiguity, scalability, schema evolution, and knowledge representation as central challenges in textual-data integration.These challenges concern aligning informal text with schemas, processing large volumes, accommodating newly extracted concepts, and supporting machine inference and querying.
- 1.1.4 Objectives: The chapter surveys challenges, state-of-the-art integration and conceptualization techniques, and unresolved gaps in text conceptualization and information extraction.It emphasizes methods spanning data integration, information extraction, ontology learning, and related approaches.
1.2 State of the Art
Text integration requires combining methods from knowledge graphs, language models, NLP, information retrieval, data integration, and ontology learning. State-of-the-art approaches conceptualize text through ontology learning, knowledge-graph construction, information extraction, and advanced language modeling, but remain constrained by heterogeneity, sparsity, and model limitations.
- Text integration draws on KGs, LLMs, NLP, IR, DI, and Ontology Learning because no single field solves the task alone.
- Data Integration: Semantic heterogeneity makes fixed-schema techniques inadequate for text, motivating Ontology Learning and Knowledge Graph Construction as intermediate structuring processes.
- Ontology Learning and Knowledge Graph Construction: Ontology learning organizes text through terms, concepts, concept hierarchies, and relations extracted with NLP techniques and lexico-syntactic or parsing methods.
- Ontology Learning and Knowledge Graph Construction: Concept and relation representation remains difficult because current technologies do not reliably learn the high-level representations humans use for robust generalization and reasoning.
- Information Extraction: 15% of integrated values are reported as missing, while textual conceptualization can structure unstructured sources and help address data sparsity.
- Information Extraction: Missing-value imputation methods largely learn repairs from structured sources, leaving textual information underused for data cleaning.
- Information Extraction: Transformer-based language models need large annotated datasets and retraining after schema changes, whereas zero-shot LLMs can hallucinate and lose precision or recall on complex text.
- Advanced Language Modeling: Going Beyond: Smaller LLMs under 7B parameters can outperform PLMs by connecting loosely related concept hierarchies, making information-extraction pipelines more generic.
1.3 Open Problems and Future Directions
Integrating structured and unstructured data requires combining techniques from multiple domains because the sources differ in structure and context. Current language-model-based approaches remain constrained by annotation, schema evolution, performance, hallucination, and resource requirements.
- Open Problems and Future Directions: Data integration benefits from techniques spanning knowledge graphs, information extraction, and other domains because structured data follows schemas while text does not.Knowledge graphs provide schema-based semantic representation, whereas text requires information extraction to make its knowledge usable for integration.
- Open Problems and Future Directions: Advanced KGC methods require large domain-specific annotated datasets with rich context and must be re-annotated and retrained when schemas change.They also exhibit bias toward frequent entity types, inconsistent performance, hallucination, and resource-intensive training.
- Open Problems and Future Directions: Structured data's limited context and the lack of annotations in organizational text reduce the suitability of language models for data integration.The integrated schema can evolve, requiring additional re-annotation and retraining.
- Open Problems and Future Directions: Adapting language-model-based methods to structured data with limited context remains an open research area.Zero-shot LLMs are described as unsuitable because of unpredictability, inconsistent performance, hallucination, and difficulty maintaining recall and precision over large text corpora.
1.4 Conclusion
Recent advances in NLP, Semantic Web, and knowledge-graph technologies have made structured–unstructured data integration timely and relevant. Organizations increasingly need to integrate and analyze both data types for insights and data-driven decisions.
- Conclusion: Advances in NLP, Semantic Web, and knowledge-graph technologies have made structured–unstructured data integration timely and relevant.Named entity recognition and relation extraction have become crucial techniques for processing unstructured data.
- Conclusion: Organizations increasingly need to integrate and analyze structured and unstructured data to gain insights and make data-driven decisions.