Unstructured data

As explained in Knowledge Graphs and LLMs in Action

Unstructured data is information lacking a fixed tabular or schema-constrained organization, such as free-form documents.

Unstructured data is information without a fixed tabular or schema-constrained organization, including emails, chats, laws, research papers, news, social media, and free-form reports. Turning it into usable knowledge requires ingestion, processing, NLP, enrichment, machine learning, and data modeling. section 5.0 section 7.5.1

Account for the main engineering difficulties:

• Handle multiple languages, scripts, encodings, typos, pronoun references, varied writing styles, and domain-specific terminology. section 1.1
• Extract entities and relationships before mapping isolated information into a connected knowledge graph. section 5.0 section 1.5.3

Healthcare and drug discovery

Process regulatory reports, clinical documents, scientific publications, and databases to connect diseases, genes, drugs, and other biological entities. Validate integrity, accuracy, consistency, and context before using the resulting knowledge for retrieval or hypothesis generation. section 1.5.1 section 7.5

Figure 1.1 Example KG in the healthcare domain. Nodes (circles) represent entities, such as people, diseases, and anatomic parts. Edges (relationships) represent meaningful connections among entities. Nodes and edges have properties describing relevant details.

Use the healthcare knowledge-graph diagram to see how extracted entities become nodes and meaningful connections become typed, property-bearing edges. section 1.1

How Knowledge Graphs and LLMs in Action covers it

How deep it goes

Explains the extraction pipeline and linguistic obstacles, but stops short of detailed algorithmic or implementation specifications.

Examples

Uses emails, legislation, research papers, regulatory reports, clinical documents, and scientific publications as source artifacts.

What it assumes

Assumes familiarity with knowledge representation and knowledge graphs while teaching the distinction between structured and unstructured sources.

What you can do after

Build knowledge graphs from documents by extracting entities and relationships, then support retrieval, integration, and hypothesis generation.

How it connects

Connects natural language processing, large language models, knowledge graphs, data ingestion, entity extraction, and data modeling.

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Related concepts

• structured datacontrasts with · Structured data is information organized according to an explicit schema or predictable fields, types, records, or relationships for consistent machine processing.
• textincludes · Text is written or digitally encoded linguistic content represented as characters, words, tokens, or other textual units.
• retrieval-augmented generationused by · Retrieval-augmented generation is a technique that retrieves relevant information from external sources and supplies it as context to a generative model when producing an output.
• language modelused by · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
• deep learningused by · Deep learning is a machine-learning approach that uses neural networks with multiple computational layers to learn representations from data.

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.