Research any topic before you write.

Find related topics. | Discover entities. | See connections. | Build a topical map.

Blazegraph

Blazegraph is an open source triplestore and graph database, written in Java. It has been abandoned since 2020. One user is Wikimedia Deutshland, for the Wikidata SPARQL endpoint. It is licensed under the GNU GPL (version 2). Amazon acquired the Blazegraph developers and the Blazegraph open source development was essentially stopped in April 2018.

Applications, Acqui-hiring by Amazon Web Service (AWS) & Prominent users

Use the mouse wheel or two fingers (on touchscreens) to zoom in and out of the map.

Research this topic

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

Explore this topic

Start with a few of the strongest sections from the source topic. These are research directions, not a list of keywords you must use.

Acqui-hiring by Amazon Web Service (AWS)

Acqui-hiringAmazon NeptuneTrademarkDomain nameCEO
5 related topics

Prominent users

Wikimedia FoundationOpenStreetMapGraphQLQuery language
4 related topics

Notable features

RDFGremlinQuery plan
3 related topics

Overview

TriplestoreGraph databaseWikimedia DeutshlandWikidataSPARQL
6 related topics

Key facts & relationships

High-confidence facts extracted from structured source data. Use them as anchors for further research.

Developer
Systap
License
GNU GPL (version 2)
Preview release
2.1.6rc / 3 February 2020 (2020-02-03)
Repository
github.com/blazegraph/database
Stable release
2.1.5 / 19 March 2019 (2019-03-19)
Type
Graph database

Topics to explore

Browse the full topic structure. Each item opens a new analysis centered on that subject.

Overview

Prominent users

Notable features

Acqui-hiring by Amazon Web Service (AWS)

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

Blazegraph

Nodes23
Edges22
Triples23
Avg. degree1.91
Density0.086957
Components1

How this topic connects Entity context

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

Blazegraph

Top relations

related to Prominent users · 7
Blazegraph → GraphQL, OpenStreetMap, Sophox, SPARQL, The Datatourisme, The Wikimedia Foundation, Wikidata Query Service
related to history · 4
Blazegraph → Bigdata, February, Since, The
related to Acqui-hiring by Amazon Web Service (AWS) · 3
Blazegraph → Amazon Neptune, AWS, CEO
Developer · 1
Blazegraph → Systap
License · 1
Blazegraph → GNU GPL (version 2)
Preview release · 1
Blazegraph → 2.1.6rc / 3 February 2020 (2020-02-03)
Repository · 1
Blazegraph → github.com/blazegraph/database
Stable release · 1
Blazegraph → 2.1.5 / 19 March 2019 (2019-03-19)
Type · 1
Blazegraph → Graph database
Website · 1
Blazegraph → blazegraph.com

Important terminology Word statistics

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

Important terminology

wikidata sparql amazon database version query open source java service com graph written gnu gpl since wikimedia endpoint aws release

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
BlazegraphDeveloperSystap1.00infobox
BlazegraphLicenseGNU GPL (version 2)1.00infobox
BlazegraphPreview release2.1.6rc / 3 February 2020 (2020-02-03)1.00infobox
BlazegraphRepositorygithub.com/blazegraph/database1.00infobox
BlazegraphStable release2.1.5 / 19 March 2019 (2019-03-19)1.00infobox
BlazegraphTypeGraph database1.00infobox
BlazegraphWebsiteblazegraph.com1.00infobox
BlazegraphWritten inJava1.00infobox
Blazegraphis aopen source triplestore and graph database0.90text
Blazegraphrelated to Acqui-hiring by Amazon Web Service (AWS)Amazon Neptune0.60section
Blazegraphrelated to Acqui-hiring by Amazon Web Service (AWS)AWS0.60section
Blazegraphrelated to Acqui-hiring by Amazon Web Service (AWS)CEO0.60section

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
    For writers, content strategists, SEOs, marketers and creators — from quick topic research to advanced semantic analysis.