DBpedia: A Nucleus for a Web of Open Data
Sören Auer1,3 , Christian Bizer2 , Georgi Kobilarov2 , Jens Lehmann1 , Richard
Cyganiak2 , and Zachary Ives3
1

Universität Leipzig, Department of Computer Science, Johannisgasse 26,
D-04103 Leipzig, Germany,{auer,lehmann}@informatik.uni-leipzig.de
2
Freie Universität Berlin, Web-based Systems Group, Garystr. 21,
D-14195 Berlin, Germany,
chris@bizer.de, georgi.kobilarov@gmx.de richard@cyganiak.de
3
University of Pennsylvania, Department of Computer and Information Science
Philadelphia, PA 19104, USA,auer@seas.upenn.edu, zives@cis.upenn.edu

Abstract DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web.
DBpedia allows you to ask sophisticated queries against datasets derived
from Wikipedia and to link other datasets on the Web to Wikipedia
data. We describe the extraction of the DBpedia datasets, and how theresulting information is published on the Web for human- and machineconsumption. We describe some emerging applications from the DBpedia
community and show how website authors can facilitate DBpedia content
within their sites. Finally, we present the current status of interlinking
DBpedia with other open datasets on the Web and outline how DBpedia
could serve as a nucleus for an emerging Web of open data.

1

Introduction

It is now almost universally acknowledged that stitching together the world’s
structured information and knowledge to answer semantically rich queries is
one of the key challenges of computer science, and one that is likely to havetremendous impact on the world as a whole. This has led to almost 30 yearsof research into information integration [15,19] and ultimately to the Semantic
Web and related technologies [1,11,13]. Such efforts have generally only gained
traction in relatively small and specialized domains, where a closed ontology,
vocabulary, or schema could be agreed upon. However, the broader Semantic
Web vision has not yet been realized, and one of the biggest challenges facing such
efforts has been how to get enough “interesting” and broadly useful information
into the system to make it useful and accessible to a general audience.
A challenge is that the traditional “top-down” model of designing an ontology
or schema before developing the data breaks down at the scale of the Web: bothdata and metadata must constantly evolve, and they must serve many different
communities. Hence, there has been a recent movement to build the Semantic Web grass-roots-style, using incremental and Web 2.0-inspired collaborative

approaches [10,12,13]. Such a collaborative, grass-roots Semantic Web requires
a new model of structured information representation and management: firstand foremost, it must handle inconsistency, ambiguity, uncertainty, data provenance [3,6,8,7], and implicit knowledge in a uniform way.
Perhaps the most effective way of spurring synergistic research along these
directions is to provide a rich corpus of diverse data. This would enable researchers to develop, compare, and evaluate different extraction, reasoning, anduncertainty management techniques, and to deploy operational systems on theWeb.
The DBpedia project has derived such a data corpus from the Wikipedia
encyclopedia. Wikipedia is heavily visited and under constant revision (e.g.,according to alexa.com, Wikipedia was the 9th most visited website in the third
quarter of 2007). Wikipedia editions are available in over 250 languages, with
the English one accounting for more than 1.95 million articles. Like many otherweb applications, Wikipedia has the problem that its search capabilities arelimited to full-text search, which only allows very limited access to this valuableknowledge base. As has been highly publicized, Wikipedia also exhibits manyof the challenging properties of collaboratively edited data: it has contradictorydata, inconsistent taxonomical conventions, errors, and even spam.
The DBpedia project focuses on the task of converting Wikipedia content
into structured knowledge, such that Semantic Web techniques can be employedagainst it — asking sophisticated queries against Wikipedia, linking it to otherdatasets on the Web, or creating new applications or mashups. We make thefollowing contributions:– We develop an information extraction framework, which converts Wikipedia
content to RDF. The basic components form a foundation upon which further research into information extraction, clustering, uncertainty management, and query processing may be conducted.
– We provide Wikipedia content as a large, multi-domain RDF dataset, which
can be used in a variety of Semantic Web applications. The DBpedia dataset
consists of 103 million RDF triples.
– We interlink the DBpedia dataset with other open datasets. This results ina large Web of data containing altogether around 2 billion RDF triples.
– We develop a series of interfaces and access modules, such that the dataset
can be accessed via Web services and linked to other sites.
The DBpedia datasets can be either imported into third party applications
or can be accessed online using a variety of DBpedia user interfaces. Figure 1
gives an overview about the DBpedia information extraction process and shows
how extracted data is published on the Web. These main DBpedia interfaces
currently use Virtuoso [9] and MySQL as storage back-ends.
The paper is structured as follows: We give an overview about the DBpedia
information extraction techniques in Section 2. The resulting datasets are described in Section 3. We exhibit methods for programmatic access to the DBpedia
dataset in Section 4. In Sections 5 we present our vision of how the DBpedia

Web 2.0
Mashups

SPARQL
Endpoint

Semantic Web
Browsers

SNORQLBrowser

LinkedData…

Traditional
Web Browser

…

Query
Builder

published via

Virtuoso

MySQL
loaded into

DBpedia datasets

Articles… Categories

Infobox

Extraction

Wikipedia Dumps
Article texts

DB tables

Figure 1. Overview of the DBpedia components.

datasets can be a nucleus for a Web of open data. We showcase several userinterfaces for accessing DBpedia in Section 6 and finally review related work in
Section 7.

2

Extracting Structured Information from Wikipedia

Wikipedia articles consist mostly of free text, but also contain different types ofstructured information, such as infobox templates, categorisation information,
images, geo-coordinates, links to external Web pages and links across different
language editions of Wikipedia.
Mediawiki4 is the software used to run Wikipedia. Due to the nature of thisWiki system, basically all editing, linking, annotating with meta-data is done
inside article texts by adding special syntactic constructs. Hence, structured information can be obtained by parsing article texts for these syntactic constructs.
Since MediaWiki exploits some of this information itself for rendering the userinterface, some information is cached in relational database tables. Dumps of the
crucial relational database tables (including the ones containing the article texts)
for different Wikipedia language versions are published on the Web on a regularbasis5 . Based on these database dumps, we currently use two different methods ofextracting semantic relationships: (1) We map the relationships that are alreadystored in relational database tables onto RDF and (2) we extract additional
information directly from the article texts and infobox templates within thearticles.
We illustrate the extraction of semantics from article texts with an Wikipedia
infobox template example. Figure 2 shows the infobox template (encoded within
45

http://www.mediawiki.org
http://download.wikimedia.org/

Figure 2. Example of a Wikipedia template and rendered output (excerpt).
a Wikipedia article) and the rendered output of the South-Korean town Busan. The infobox extraction algorithm detects such templates and recognizes
their structure using pattern matching techniques. It selects significant templates, which are then parsed and transformed to RDF triples. The algorithm
uses post-processing techniques to increase the quality of the extraction. MediaWiki links are recognized and transformed to suitable URIs, common units
are detected and transformed to data types. Furthermore, the algorithm candetect lists of objects, which are transformed to RDF lists. Details about the infobox extraction algorithm (including issues like data type recognition, cleansing
heuristics and identifier generation) can be found in [2]. All extraction algorithms
are implemented using PHP and are available under an open-source license6 .

3

The DBpedia Dataset

The DBpedia dataset currently provides information about more than 1.95 million ”things”, including at least 80,000 persons, 70,000 places, 35,000 music albums, 12,000 films. It contains 657,000 links to images, 1,600,000 links to relevant external web pages, 180,000 external links into other RDF datasets, 207,000
Wikipedia categories and 75,000 YAGO categories [16].
DBpedia concepts are described by short and long abstracts in 13 different languages. These abstracts have been extracted from the English, German,6

http://sf.net/projects/dbpedia

French, Spanish, Italian, Portuguese, Polish, Swedish, Dutch, Japanese, Chinese,
Russian, Finnish and Norwegian versions of Wikipedia.
Altogether the DBpedia dataset consists of around 103 million RDF triples.
The dataset is provided for download as a set of smaller RDF files. Table 1 gives
an overview over these files.
DatasetArticlesDescription
Triples
7.6M
Descriptions of all 1.95 million concepts within the English
Wikipedia including titles, short abstracts, thumbnails andlinks to the corresponding articles.
Ext. Abstracts
Additional, extended English abstracts.
2.1M
5.7M
Languages
Additional titles, short abstracts and Wikipedia article
links in German, French, Spanish, Italian, Portuguese, Polish, Swedish, Dutch, Japanese, Chinese, Russian, Finnish
and Norwegian.
Lang. Abstracts
Extended abstracts in 13 languages.
1.9M
Infoboxes
Data attributes for concepts that have been extracted from 15.5M
Wikipedia infoboxes.
External Links
Links to external web pages about a concept.
1.6M
Article Categories Links from concepts to categories using SKOS.
5.2M
Categories
Information which concept is a category and how categories
1M
are related.
Yago Types
Dataset containing rdf:type Statements for all DBpedia in- 1.9 M
stances using classification from YAGO [16].
Persons
Information about 80,000 persons (date and place of birth
0.5Metc.) represented using the FOAF vocabulary.
Page Links
Internal links between DBpedia instances derived from the
62M
internal pagelinks between Wikipedia articles.
RDF Links
Links between DBpedia and Geonames, US Census, Mu180K
sicbrainz, Project Gutenberg, the DBLP bibliography andthe RDF Book Mashup.

Table 1. The DBpedia datasets.
Some datasets (such as the Persons or Infoboxes datasets) are semanticallyrich in the sense that they contain very specific information. Others (such as
the PageLinks dataset) contain meta-data (such as links between articles) without a specific semantics. However, the latter can be beneficial, e.g. for deriving
measures of closeness between concepts or relevance in search results.
Each of the 1.95 million resources described in the DBpedia dataset is identified by a URI reference of the form http://dbpedia.org/resource/Name ,where Name is taken from the URL of the source Wikipedia article, which has
the form http://en.wikipedia.org/wiki/Name . Thus, each resource is tied
directly to an English-language Wikipedia article. This yields certain beneficial
properties to DBpedia identifiers:– They cover a wide range of encyclopedic topics,– They are defined by community consensus,

– There are clear policies in place for their management,– And an extensive textual definition of the concept is available at a wellknown web location (the Wikipedia page).

4

Accessing the DBpedia Dataset on the Web

We provide three access mechanisms to the DBpedia dataset: Linked Data, theSPARQL protocol, and downloadable RDF dumps. Royalty-free access to theseinterfaces is granted under the terms of the GNU Free Documentation License.
Linked Data. Linked Data is a method of publishing RDF data on the Web
that relies on http:// URIs as resource identifiers and the HTTP protocol toretrieve resource descriptions [4,5]. The URIs are configured to return meaningful information about the resource—typically, an RDF description containing everything that is known about it. Such a description usually mentions related resources by URI, which in turn can be accessed to yield their descriptions. This forms a dense mesh of web-accessible resource descriptions that can
span server and organization boundaries. DBpedia resource identifiers, such as
http://dbpedia.org/resource/Busan, are set up to return RDF descriptions
when accessed by Semantic Web agents, and a simple HTML view of the same information to traditional web browsers (see Figure 3). HTTP content negotiation
is used to deliver the appropriate format.
Web agents that can access Linked Data include: 1. Semantic Web browsers
like Disco7 , Tabulator[17] (see Figure 3), or the OpenLink Data Web Browser8 ;2. Semantic Web crawlers like SWSE9 and Swoogle10 ; 3. Semantic Web query
agents like the Semantic Web Client Library11 and the SemWeb client for SWI
prolog12 .
SPARQL Endpoint. We provide a SPARQL endpoint for querying the DBpedia
dataset. Client applications can send queries over the SPARQL protocol to thisendpoint at http://dbpedia.org/sparql. This interface is appropriate when
the client application developer knows in advance exactly what information isneeded. In addition to standard SPARQL, the endpoint supports several extensions of the query language that have proved useful for developing user interfaces:
full text search over selected RDF predicates, and aggregate functions, notably
COUNT. To protect the service from overload, limits on query cost and result size
are in place. For example, a query that asks for the store’s entire contents isrejected as too costly. SELECT results are truncated at 1000 rows. The SPARQL
endpoint is hosted using Virtuoso Universal Server13 .
7

http://sites.wiwiss.fu-berlin.de/suhl/bizer/ng4j/disco/
http://demo.openlinksw.com/DAV/JS/rdfbrowser/index.html
9
http://swse.org
10
http://swoogle.umbc.edu/
11
http://sites.wiwiss.fu-berlin.de/suhl/bizer/ng4j/semwebclient/12
http://moustaki.org/swic/
13
http://virtuoso.openlinksw.com
8

Figure 3. http://dbpedia.org/resource/Busan viewed in a web browser(left) and in Tabulator (right).
RDF Dumps. N-Triple serializations of the datasets are available for download
at the DBpedia website and can be used by sites that are interested in larger
parts of the dataset.

5Interlinking DBpedia with other Open Datasets

In order to enable DBpedia users to discover further information, the DBpedia
dataset is interlinked with various other data sources on the Web using RDF
links. RDF links enable web surfers to navigate from data within one data source
to related data within other sources using a Semantic Web browser. RDF links
can also be followed by the crawlers of Semantic Web search engines, which may
provide sophisticated search and query capabilities over crawled data.
The DBpedia interlinking effort is part of the Linking Open Data community
project14 of the W3C Semantic Web Education and Outreach (SWEO) interest group. This community project is committed to make massive datasets andontologies, such as the US Census, Geonames, MusicBrainz, the DBLP bibliography, WordNet, Cyc and many others, interoperable on the Semantic Web. DBpedia, with its broad topic coverage, intersects with practically all these datasetsand therefore makes an excellent “linking hub” for such efforts.
Figure 4 gives an overview about the datasets that are currently interlinked
with DBpedia. Altogether this Web-of-Data amounts to approximately 2 billion
RDF triples. Using these RDF links, surfers can for instance navigate from acomputer scientist in DBpedia to her publications in the DBLP database, froma DBpedia book to reviews and sales offers for this book provided by the RDF
Book Mashup, or from a band in DBpedia to a list of their songs provided byMusicbrainz or dbtune.
The example RDF link shown below connects the DBpedia URI identifying
Busan with further data about the city provided by Geonames:<http://dbpedia.org/resource/Busan>14

http://esw.w3.org/topic/SweoIG/TaskForces/CommunityProjects/
LinkingOpenData

Figure 4. Datsets that are interlinked with DBpedia.

owl:sameAs <http://sws.geonames.org/1838524/> .
Agents can follow this link, retrieve RDF from the Geonames URI, andthereby get hold of additional information about Busan as published by the
Geonames server, which again contains further links deeper into the Geonames
data. DBpedia URIs can also be used to express personal interests, places ofresidence, and similar facts within personal FOAF profiles:<http://richard.cyganiak.de/foaf.rdf#cygri>
foaf:topic_interest <http://dbpedia.org/resource/Semantic_Web> ;foaf:based_near <http://dbpedia.org/resource/Berlin> .
Another use case is categorization of blog posts, news stories and other documents. The advantage of this approach is that all DBpedia URIs are backed
with data and thus allow clients to retrieve more information about a topic:<http://news.cnn.com/item1143>
dc:subject <http://dbpedia.org/resource/Iraq_War> .

6

User Interfaces

User interfaces for DBpedia can range from a simple table within a classic web
page, over browsing interfaces to different types of query interfaces. This section
gives an overview about the different user interfaces that have been implemented
so far.

6.1

Simple Integration of DBpedia Data into Web Pages

DBpedia is a valuable source of general-purpose data that can be used within
web pages. Therefore, if you want a table containing German state capitals,
African musicians, Amiga computer games or whatever on your website, you
can generate this table using a SPARQL query against the DBpedia endpoint.
Wikipedia is kept up-to-date by a large community and a nice feature of suchtables is that they will also stay up-to-date as Wikipedia, and thus also DBpedia,
changes. Such tables can either be implemented using Javascript on the client
or with a scripting language like PHP on the server. Two examples of Javascript
generated tables are found on the DBpedia website15 .
6.2

Search DBpedia.org

Search DBpedia.org is a sample application that allows users to explore theDBpedia dataset together with information from interlinked datasets such as
Geonames, the RDF Book Mashup or the DBLP bibliography. In contrast to
the keyword-based full-text search commonly found on the Web, search overstructured data offers the opportunity to make productive use of the relations in
the data, enabling stepwise narrowing of search results in different dimensions.
This adds a browsing component to the search task and may reduce the common“keyword-hit-or-not-hit” problem.
A Search DBpedia.org session starts with a keyword search. A first set ofresults is computed by direct keyword matches. Related matches are added,
using the relations between entities up to a depth of two nodes. Thus, a search
for the keyword “Scorsese” will include the director Martin Scorsese, as well as
all of his films, and the actors of these films.
The next step is result ranking. Our experiments showed that important
articles receive more incoming page links from other articles. We use a combination of incoming link count, relevance of the link’s source, and relation depth
to calculate a relevance ranking.
After entering a search term, the user is presented with a list of ranked
results, and with a tag cloud built from the classes found in the results, using
a combination of the DBpedia and YAGO [16] classifications. Each class weightis calculated from the sum of associated result weights and the frequency ofoccurrence. The tag cloud enables the user to narrow the results to a specific
type of entities, such as “Actor”, even though a simple keyword search may nothave brought up any actors.
When a resource from the results is selected, the user is presented with adetailed view of all data that is known about the resource. Label, image and description are shown on top. Single-valued and multi-valued properties are shown
separately. Data from interlinked datasets is automatically retrieved by following RDF links within the dataset and retrieved data from interlinked datasets
is shown together with the DBpedia data.
15

http://dbpedia.org

Figure 5. Search results and details view for Busan.

6.3

Querying DBpedia Data

Compared to most of the other Semantic Web knowledge bases currently available, for the RDF extracted from Wikipedia we have to deal with a different
type of knowledge structure – we have a very large information schema and aconsiderable amount of data adhering to this schema. Existing tools unfortunately mostly focus on either one of both parts of a knowledge base being large,
schema or data.
If we have a large data set and large data schema, elaborated RDF storeswith integrated query engines alone are not very helpful. Due to the large data
schema, users can hardly know which properties and identifiers are used in theknowledge base and hence can be used for querying. Consequently, users have to
be guided when building queries and reasonable alternatives should be suggested.
We specifically developed a graph pattern builder for querying the extracted
Wikipedia content. Users query the knowledge base by means of a graph patternconsisting of multiple triple patterns. For each triple pattern three form fields
capture variables, identifiers or filters for subject, predicate and object of a triple.
While users type identifier names into one of the form fields, a look-ahead search
proposes suitable options. These are obtained not just by looking for matchingidentifiers but by executing the currently built query using a variable for thecurrently edited identifier and filtering the results returned for this variable formatches starting with the search string the user supplied. This method ensures,that the identifier proposed is really used in conjunction with the graph patternunder construction and that the query actually returns results. In addition, theidentifier search results are ordered by usage number, showing commonly used
identifiers first. All this is executed in the background, using the Web 2.0 AJAX
technology and hence completely transparent for the user. Figure 6 shows ascreenshot of the graph pattern builder.

Figure 6. Form based query builder.

6.4

Third Party User Interfaces

The DBpedia project aims at providing a hotbed for applications and mashups
based on information from Wikipedia. Although DBpedia was just recently
launched, there is already a number of third party applications using the dataset.
Examples include:– A SemanticMediaWiki [14,18] installation run by the University of Karlsruhe, which has imported the DBpedia dataset together with the English
edition of Wikipedia.
– WikiStory (see Figure 7) which enables users to browse Wikipedia articles
about people on a large timeline.
– The Objectsheet JavaScript visual data environment,which allows spreadsheet calculations based on DBpedia data16 .

7

Related WorkA second project that also works on extracting structured information from
Wikipedia is the YAGO project [16]. YAGO extracts only 14 relationship types,
such as subClassOf, type, familyNameOf, locatedIn from different sources of information in Wikipedia. One source is the Wikipedia category system (for subClassOf, locatedIn, diedInYear, bornInYear ), and another one are Wikipedia
redirects. YAGO does not perform an infobox extraction as in our approach. For
determining (sub-)class relationships, YAGO does not use the full Wikipedia
category hierarchy, but links leaf categories to the WordNet hierarchy.
16

http://richk.net/objectsheet/osc.html?file=sparql_query1.os

Figure 7. WikiStory allows timeline browsing of biographies in Wikipedia.

The Semantic MediaWiki project [14,18] also aims at enabling the reuse of
information within Wikis as well as at enhancing search and browse facilities.
Semantic MediaWiki is an extension of the MediaWiki software, which allows
you to add structured data into Wikis using a specific syntax. Ultimately, theDBpedia and Semantic MediaWiki have similar goals. Both want to deliver the
benefits of structured information in Wikipedia to the users, but use different
approaches to achieve this aim. Semantic MediaWiki requires authors to deal
with a new syntax and covering all structured information within Wikipedia
would require to convert all information into this syntax. DBpedia exploits the
structure that already exists within Wikipedia and hence does not require deep
technical or methodological changes. However, DBpedia is not as tightly integrated into Wikipedia as is planned for Semantic MediaWiki and thus is limitedin constraining Wikipedia authors towards syntactical and structural consistency
and homogeneity.
Another interesting approach is followed by Freebase17 . The project aims
at building a huge online database which users can edit in a similar fashion as
they edit Wikipedia articles today. The DBpedia community cooperates with
Metaweb and we will interlink data from both sources once Freebase is public.

8

Future Work and Conclusions

As future work, we will first concentrate on improving the quality of the DBpedia dataset. We will further automate the data extraction process in order toincrease the currency of the DBpedia dataset and synchronize it with changes
in Wikipedia. In parallel, we will keep on exploring different types of user interfaces and use cases for the DBpedia datasets. Within the W3C Linking Open17

http://www.freebase.com

Data community project18 , we will interlink the DBpedia dataset with further
datasets as they get published as Linked Data on the Web. We also plan to
exploit synergies between Wikipedia versions in different languages in order tofurther increase DBpedia coverage and provide quality assurance tools to the
Wikipedia community. Such a tool could for instance notify a Wikipedia author
about contradictions between the content of infoboxes contained in the different language versions of an article. Interlinking DBpedia with other knowledge
bases such as Cyc (and their use as back-ground knowledge) could lead to further
methods for (semi-) automatic consistency checks for Wikipedia content.
DBpedia is a major source of open, royalty-free data on the Web. We hopethat by interlinking DBpedia with further data sources, it could serve as a nucleus
for the emerging Web of Data.

Acknowledgments
We are grateful to the members of the growing DBpedia community, who are
actively contributing to the project. In particular we would like to thank Jörg
Schüppel and the OpenLink team around Kingsley Idehen and Orri Erling.

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