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Knowledge graph embedding

In representation learning, knowledge graph embedding (KGE), also called knowledge representation learning (KRL), or multi-relation learning, is a machine learning task of learning a low-dimensional representation of a knowledge graph's entities and relations while preserving their semantic meaning. Leveraging their embedded representation, knowledge…

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Explore the main themes, entities and connections around Knowledge graph embedding. Start with the topic map, then use the sections below for research and deeper semantic analysis.

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Overview

Definition

Embedding procedure

Applications

Models

Model performance

Libraries

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.

Map overview Semantic statistics

Knowledge graph embedding

Nodes57
Edges56
Triples49
Avg. degree1.96
Density0.035088
Components1

How this topic connects Entity context

See the strongest relationship patterns around the current topic before diving into the raw triples.

Knowledge graph embedding

Top relations

related to Embedding procedure · 8
Knowledge graph embedding → All, At, First, In, Iteration, The, Then, Usually
related to Model performance · 8
Knowledge graph embedding → FB15k, FB15k-237, More, Rossi, The, WN18, WN18RR, YAGO3-10
has application · 6
Knowledge graph embedding → Drug, In, It, Knowledge, The, Training
related to Models · 4
Knowledge graph embedding → Given, In, Rossi, The
related to Deep learning models · 3
Knowledge graph embedding → The, These, This
related to Tensor decomposition model · 2
Knowledge graph embedding → In, The

Important terminology Word statistics

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

Important terminology

embedding knowledge graph relation displaystyle entities fact models representation model relations triple used entity head matrix tail given tensor function

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
link predictioninstance ofknowledge graphs can be used for various applications0.80text
triple classificationinstance ofknowledge graphs can be used for various applications0.80text
entity recognitioninstance ofknowledge graphs can be used for various applications0.80text
clusteringinstance ofknowledge graphs can be used for various applications0.80text
and relation extractioninstance ofknowledge graphs can be used for various applications0.80text
RESCALinstance ofand other embedding models0.80text
DistMultinstance ofand other embedding models0.80text
ComplExinstance ofand other embedding models0.80text
and SimplE can be expressed as a special formulation of TuckER.MEIinstance ofand other embedding models0.80text
TuckERinstance ofPrevious models0.80text
RESCALinstance ofPrevious models0.80text
DistMultinstance ofPrevious models0.80text

Related concept clusters Concept neighborhoods

These clusters group vocabulary that occurs around closely connected concepts in the source material.

    Connections between topic areas Semantic bridges

    Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.

    Min side: 3
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