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DBpedia: History & Applications
DBpedia (from "DB" for "database") is a project aiming to extract structured content from the information created in the Wikipedia project. This structured information is made available on the World Wide Web using OpenLink Virtuoso. DBpedia allows users to semantically query relationships and properties of Wikipedia resources, including links to other…
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DBpedia topic overview
The analysis highlights History and Applications as prominent areas in the source structure around DBpedia.
Related topics
80
Source areas
8
Connected nodes
88
Extracted relationships
97
Concept neighborhoods
36
Bridge connections
88
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.
Use cases · 28 topics
DBpedia Spotlight · 13 topics
Background · 11 topics
Overview · 10 topics
Examples · 8 topics
Dataset · 6 topics
Archivo ontology database · 2 topics
History · 2 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.
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Developers
Leipzig University · University of Mannheim
License
GNU General Public License
Release
10 January 2007 (19 years ago) (2007-01-10)
Repository
github.com/dbpedia/
Stable release
DBpedia 2016-10 / 4 July 2017
Type
Semantic Web · linked data
Explore all related topics Closing gaps
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.
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.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
How DBpedia connects Entity context
Entity context groups extracted connections by relationship type. Counts show recurrence in the analyzed source; the examples are research signals, not claims about importance.
The extracted context around DBpedia shows recurring relationship patterns in the source. For example, DBpedia → As, Bio2RDF, CIA World Factbook, DBLP, DBpedia Spotlight, DBtune Jamendo, Eurostat, Faviki, Freebase, GeoNames, IBM Watson's Jeopardy, Knowledge Sharing Platform, Linked Open Data, MusicBrainz, Open Data, OpenCalais, OpenCyc, Project Gutenberg, RDF, Samsung Another extracted example is DBpedia → Apache License, Berlin, Clients, DBpedia Spotlight, Free University, Instead, Internationalization, It, Java, Java/Scala API, June, PHP, The, The DBpedia Spotlight, This, Web, Web Based Systems Group, Wikipedia. Use these groups to spot repeated connection types before inspecting the individual relationships.
DBpedia
Top relations
related to Use cases· 32
DBpedia → As, Bio2RDF, CIA World Factbook, DBLP, DBpedia Spotlight, DBtune Jamendo, Eurostat, Faviki, Freebase, GeoNames, IBM Watson's Jeopardy, Knowledge Sharing Platform, Linked Open Data, MusicBrainz, Open Data, OpenCalais, OpenCyc, Project Gutenberg, RDF, Samsung
related to DBpedia Spotlight· 18
DBpedia → Apache License, Berlin, Clients, DBpedia Spotlight, Free University, Instead, Internationalization, It, Java, Java/Scala API, June, PHP, The, The DBpedia Spotlight, This, Web, Web Based Systems Group, Wikipedia
related to Examples· 13
DBpedia → Data, For, Japanese, Koi Cupid, Mia Ikumi, RDF, Since DBpedia, SPARQL, SQL-like, Super Doll Licca-chan, Tokyo Mew Mew, Wikipedia, Wikipedia's
related to Archivo ontology database· 9
DBpedia → Archivo, As, For, From, June, Ontologies, OWL, SHACL, Web Ontology Language
related to Dataset· 6
DBpedia → English Wikipedia, For, From, RDF, Resource Description Framework, The
related to history· 6
DBpedia → Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, Sören Auer, Zachary Ives
Developers· 2
DBpedia → Leipzig University, University of Mannheim
Type· 2
DBpedia → linked data, Semantic Web
Written in· 2
DBpedia → Java, Scala
License· 1
DBpedia → GNU General Public License
Important terminology
Frequent words and multi-word phrases across the lead, headings, infobox and body. Useful for terminology coverage.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
Relationships are represented as Subject → Predicate → Object triples. Confidence describes extraction strength, not the real-world importance of a relationship; lower-confidence rows may be contextual rather than explicit.
TTTA extracted 97 structured relationships around DBpedia. Examples in this analysis include DBpedia → Developers → Leipzig University and DBpedia → Developers → University of Mannheim. The table shows each extracted connection, where it came from and its confidence.
Subject
Predicate
Object
Confidence
Src
DBpedia
Developers
Leipzig University
1.00
infobox
DBpedia
Developers
University of Mannheim
1.00
infobox
DBpedia
License
GNU General Public License
1.00
infobox
DBpedia
Release
10 January 2007 (19 years ago) (2007-01-10)
1.00
infobox
DBpedia
Repository
github.com/dbpedia/
1.00
infobox
DBpedia
Stable release
DBpedia 2016-10 / 4 July 2017
1.00
infobox
DBpedia
Type
Semantic Web
1.00
infobox
DBpedia
Type
linked data
1.00
infobox
DBpedia
Website
dbpedia.org
1.00
infobox
DBpedia
Written in
Scala
1.00
infobox
DBpedia
Written in
Java
1.00
infobox
Super Doll Licca-chan
instance of
Mia Ikumi and on this author's works
0.80
text
No triples extracted yet.
Related concept clusters Concept neighborhoods
Concept neighborhoods group terms that sit near one another in the extracted graph. They are useful for discovering adjacent language and context, but proximity does not by itself imply a factual relationship.
The concept neighborhoods around DBpedia bring nearby vocabulary together. In this analysis, examples include Data, Spotlight and Information. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
DBpedia
Data
Spotlight
Information
Project
Web
Million
Including
Ontology
Uses
Datasets
Open
Semantic
dbpedia
Data
Spotlight
Information
Project
Web
Million
Including
Ontology
Uses
Datasets
Open
Semantic
database
Project
Free
License
Ontology
Dataset
Semantic
Use
Web
Dbpedia
Information
Java
Made
world wide web
Free
Project
Made
Spotlight
Using
Data
External
Including
Java
Linked
University
Web
linked data
Links
Project
Open
Dbpedia
Data
Linked
Set
Spotlight
Also
Web
Dataset
Datasets
open data
Open
Dbpedia
Linked
Set
Project
Web
Dataset
Datasets
University
Also
Uses
World
database rights
Project
Free
License
Ontology
Dataset
Semantic
Use
Web
Dbpedia
Information
Java
Made
web pages
Wikipedia
Free
Spotlight
Data
Java
University
World
External
License
Linked
Links
Ontology
Connections between topic areas Semantic bridges
Semantic bridges connect parts of the graph that would otherwise be more separate. A bridge is a structural signal in this analysis, not a claim that the two topic areas are equivalent.
For DBpedia, one of the stronger structural bridges in this analysis connects DBpedia with Use cases. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.
DBpedia
Nodes89
Edges88
Triples97
Avg. degree1.98
Density0.022472
Components1
Source & methodology
TTTA analyzes the structure around DBpedia to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History & Applications, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
