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Knowledge extraction: Knowledge discovery, Extraction from natural language sources & Examples

Knowledge extraction is the creation of knowledge from structured (relational databases, XML) and unstructured (text, documents, images) sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing. Although it is methodically similar to…

Language: English [EN]
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Knowledge extraction topic overview

The analysis highlights Knowledge discovery, Extraction from natural language sources and Examples as prominent areas in the source structure around Knowledge extraction. 1 topic appears in more than one source area, which can help identify connections that are less obvious in a linear reading.

Related topics
90
Source areas
5
Connected nodes
96
Extracted relationships
20
Concept neighborhoods
42
Bridge connections
96

What this topic covers Research coverage

Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.

Knowledge discovery · 39 topics
Overview · 17 topics
Extraction from natural language sources · 15 topics
Examples · 14 topics
Extraction from structured sources to RDF · 6 topics

Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.

Explore all related topics Closing gaps

Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.

Overview

Examples

Extraction from structured sources to RDF

Extraction from natural language sources

Knowledge discovery

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

How Knowledge extraction connects Entity context

The extracted context around Knowledge extraction shows recurring relationship patterns in the source. For example, Knowledge extraction → Because, HTML, If, In, The Another extracted example is Knowledge extraction → As, Individual, NLP, Typical NLP. Use these groups to spot repeated connection types before inspecting the individual relationships.

Knowledge extraction

Top relations

related to Extraction from natural language sources · 5
Knowledge extraction → Because, HTML, If, In, The
related to Linguistic annotation / natural language processing (NLP) · 4
Knowledge extraction → As, Individual, NLP, Typical NLP
is a · 2
Knowledge extraction → creation of knowledge from structured, transformation of Wikipedia into structured data and also the mapping to existing knowledge
related to Tools · 1
Knowledge extraction → The

Important terminology

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

knowledge data extraction text rdf ontology entity information language relational structured process databases discovery natural used table ontologies existing entities

Knowledge extraction relationships Subject–Predicate–Object triples

TTTA extracted 20 structured relationships around Knowledge extraction. Examples in this analysis include Knowledge extraction → is a → creation of knowledge from structured and Knowledge extraction → is a → transformation of Wikipedia into structured data and also the mapping to existing knowledge. The table shows each extracted connection, where it came from and its confidence.

SubjectPredicateObjectConfidenceSrc
Knowledge extractionis acreation of knowledge from structured0.90text
Knowledge extractionis atransformation of Wikipedia into structured data and also the mapping to existing knowledge0.90text
RDFinstance ofOverviewAfter the standardization of knowledge representation languages0.80text
OWLinstance ofOverviewAfter the standardization of knowledge representation languages0.80text
much research has been conducted in the areainstance ofOverviewAfter the standardization of knowledge representation languages0.80text
especially regarding transforming relational databases into RDFinstance ofOverviewAfter the standardization of knowledge representation languages0.80text
identity resolutioninstance ofOverviewAfter the standardization of knowledge representation languages0.80text
knowledge discoveryinstance ofOverviewAfter the standardization of knowledge representation languages0.80text
ontology learninginstance ofOverviewAfter the standardization of knowledge representation languages0.80text
DBpedia is establishedinstance ofare extracted with the help of a domain-specific lexicon to link these at entity linking.In entity linking a link between the extracted lexical terms from the source text and th…0.80text
Knowledge extractionrelated to Extraction from natural language sourcesThe0.60section
Knowledge extractionrelated to Extraction from natural language sourcesBecause0.60section

Related concept clusters Concept neighborhoods

The concept neighborhoods around Knowledge extraction bring nearby vocabulary together. In this analysis, examples include Data, Discovery and Knowledge. Use the clusters to find adjacent concepts and terminology that may deserve separate research.

  • Knowledge extraction
    • Data
    • Discovery
    • Knowledge
    • Information
    • Existing
    • Also
    • Software
    • Language
    • Nlp
    • Annotation
    • Databases
    • Ie
  • knowledge extraction
    • Data
    • Structured
    • Discovery
    • Nlp
    • Knowledge
    • Language
    • Information
    • Natural
    • Annotation
    • Existing
    • Relational
    • Sources
  • knowledge
    • Data
    • Discovery
    • Existing
    • Also
    • Software
    • Language
    • Nlp
    • Annotation
    • Databases
    • Structured
    • Natural
    • Relational
  • relational databases
    • Relational
    • Also
    • Structured
    • Transformation
    • Rdf
    • Sources
    • Extraction
    • Mapping
    • Xml
    • Knowledge
    • Entities
    • Ie
  • xml
    • Rdf
    • Structured
    • Sources
    • Databases
    • Relational
    • Language
    • Ie
    • Learning
    • Transformation
    • Linking
    • Mapping
    • Nlp
  • information extraction
    • Structured
    • Natural
    • Nlp
    • Knowledge
    • Language
    • Information
    • Annotation
    • Methods
    • Relational
    • Sources
    • Used
    • Also
  • natural language processing
    • Natural
    • Annotation
    • Following
    • Sources
    • Text
    • Nlp
    • Transformation
    • Used
    • Rdf
    • Also
    • Ontology
    • Structured
  • relational schema
    • Structured
    • Transformation
    • Rdf
    • Ie
    • Language
    • Learning
    • Sources
    • Information
    • Mapping
    • Nlp
    • Xml
    • Also

Connections between topic areas Semantic bridges

For Knowledge extraction, one of the stronger structural bridges in this analysis connects Knowledge extraction with Knowledge discovery. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.

Min side: 3
Knowledge extraction — Knowledge discovery · splits 57 ⟂ 40
Knowledge extraction — Overview · splits 79 ⟂ 18
Knowledge extraction — Extraction from natural language sources · splits 81 ⟂ 16
Knowledge extraction — Examples · splits 82 ⟂ 15
Knowledge extraction — Extraction from structured sources to RDF · splits 90 ⟂ 7

Map overview Semantic statistics

Knowledge extraction

Nodes97
Edges96
Triples20
Avg. degree1.98
Density0.020619
Components1

Source & methodology

TTTA analyzes the structure around Knowledge extraction to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Knowledge discovery, Extraction from natural language sources & Examples, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.

Source: Wikipedia — Knowledge extraction · EN edition · Analysis: TopicsToTalkAbout

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