Data integration

As explained in Knowledge Graphs and LLMs in Action

The process of combining data from multiple sources into a coherent and consistent representation.

Data integration combines structured, semistructured, and unstructured sources into a coherent knowledge graph. Reliable integration preserves accuracy, consistency, identifiers, context, and domain meaning. section 1.5.1 section 4.0

A practical integration pipeline typically:

• Transforms CSV files, relational databases, APIs, and other schemas into a homogeneous graph. section 4.0
• Reconciles names and identifiers, then merges entities and relationships during post-processing. section 4.0
• Adds typed relationships, ontologies, and inferred links so graph structure reflects domain meaning. section 1.5.1

Biomedical integration

A clinical knowledge graph can merge electronic health records with genomics, proteomics, transcriptomics, ontologies, and publications. Patient, drug, and disease nodes then support precision-medicine analysis while privacy, missing information, and heterogeneous data remain operational constraints. section 4.4

Figure 4.12 A single patient’s clinical oncology journey (courtesy of David Hughes; https://www.graphable.ai/blog/patient-journey-mapping)

Trace how integrated clinical data can represent a patient journey across complex care, complementing clinical pathways. section 4.4

How Knowledge Graphs and LLMs in Action covers it

How deep it goes

Explains transformation, reconciliation, merging, inference, and analysis while deferring detailed implementation to individual pipelines.

Examples

Uses drug discovery, clinical knowledge graphs, precision medicine, and patient journey mapping as concrete applications.

What you can do after

Apply integration patterns to build coherent biomedical graphs for retrieval, hypothesis generation, and clinical decision support.

How it connects

Connects knowledge graphs, ontologies, entity resolution, large language models, electronic health records, and multi-omics data.

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Related concepts

• data sourceuses · A data source is an origin, system, repository, or process from which data is obtained for use or processing.
• data clean roomused by · A data clean room is a controlled environment in which parties can match or analyze data without directly exposing sensitive records to one another.
• data handlingpart of · Data handling is the set of practices and operations for collecting, organizing, transforming, storing, governing, protecting, and using data.
• data quality monitoringhas part · Data quality monitoring is the ongoing observation and evaluation of data against criteria for quality, such as validity, completeness, consistency, and freshness.
• entity resolutionhas part · Entity resolution is the process of determining which records or mentions refer to the same real-world entity despite differences in their representations.
• intelligent advisory systemused by · An intelligent advisory system is an intelligent system that provides recommendations or alerts to support human decision-making without independently taking action.

This explainer was generated by Manning's knowledge graph from the text of Knowledge Graphs and LLMs in Action. The section links point to the parts of the book it draws on.