Use Cases

This page lists use cases for the DBpedia knowledge base together with references to ongoing work into these directions.

1. Revolutionize Wikipedia Search

Wikipedia currently only supports keyword-based search and does not allow more expressive queries like “Give me all cities in New Jersey with more than 10 000 inhabitants” or “Give me all Italian musicians from the 18th century.” This lowers the overall utility of Wikipedia.

One major application domain for the DBpedia data set is to enable sophisticated queries against Wikipedia, which could revolutionize the access to this valuable knowledge source.

Here are three prototypical search interfaces using different approaches to improve Wikipedia search:

• The DBpedia Query Builder provides a easy-to-use interface for formulating queries against DBpedia as a set of matching patterns.
• The OpenLink QBE visual query builder provides a graphical interface for formulating SPARQL queries against DBpedia.

Examples of different queries that can be asked against DBpedia are found on the OnlineAccess and Datasets pages.

2. Include DBpedia Data in Your Web Page

One nice thing about Wikipedia is that is kept up-to-date by a large community. Therefore, if you need a table on your Web page with, say, German cities, African musicians, Amiga computer games from the 90s, or whatever, you could generate this table with a SPARQL query against the DBpedia endpoint, and your table will stay up-to-date as Wikipedia changes.

Such tables can be implemented either by using Java Script on the client, or with a scripting language like PHP on the server. The second option also allows you to cache query results.

Other 3rd party applications integrating DBpedia into their websites include:

• UK football clubs with maps of the birthplaces of their players
• Rock and Roll Groups and their discography as a SIMILE timeline

3. Mobile and Geographic Applications

DBpedia contains information about 300,000 geographic locations and is interlinked with other geo-related data sources such as Geonames, the US Census, Euro Stat, and the CIA world fact book. The data set contains geo-coordinates for many geographic locations which enable location-based SPARQL searches.

This makes DBpedia a valuable data source for location-based applications. DBpedia contains short abstracts about places which display nicely on mobile phones and PDAs, in up to 12 languages.

As current generation mobiles and PDAs start having GPS receivers, it is possible to implement nice location-based information services for them based on DBpedia data and the DBpedia SPARQL endpoint.

One example of such an application is DBpedia Mobile which demonstrates how DBpedia can be used as entry point into the geo-spacial Semantic Web.

4. Document Classification, Annotation and Social Bookmarking

Terms from DBpedia can be used to annotate Web content. Compared to other subject hierarchies, like the classic ones used within libraries, DBpedia has the advantage that each subject is backed by a rich description including abstracts in 14 languages. Another advantage compared to static hierarchies is that DBpedia evolves as Wikipedia changes.

One application that uses DBpedia terms for the annotation of Web content is Faviki. Faviki is a social bookmarking tool which allows you to tag Web pages you want to remember with Wikipedia terms. This means that everybody uses the same names for tags from the world's largest collection of knowledge.

5. Multi-Domain Ontology

DBpedia is one of the largest multi-domain ontologies that currently exist. Compared to other ontologies which usually only cover specific domains, are created by relatively small groups of knowledge engineers, and are very cost intensive to keep up-to-date as domains change, DBpedia has the advantage that

1. it covers many domains and contains lots of instances.
2. it represents real community agreement.
3. it (automatically) evolves as Wikipedia changes.

The disadvantages of DBpedia compared to hand-crafted ontologies like SUMO, Open Cyc, or Wordnet are that

1. DBpedia is less formally structured.
2. The data quality is lower and there are inconsistencies within DBpedia.

An approach to combine the advantages of both worlds is to interlink DBpedia with hand-crafted ontologies such as Open Cyc, SUMO, or Word Net, which enables applications to use the formal knowledge from these ontologies together with the instance data from DBpedia.

DBpedia already contains

• 42.000 RDF links into OpenCyc,
• 318,000 RDF links into WordNet,
• 2,4 million RDF links to Freebase,
• RDF links to UMBEL.

Interlinking DBpedia with these ontologies could further extend query capabilities. For instance, knowing that “cities are geographic places” and “mountains are geographic places”, a query engine could return cities as well as mountains for a query about geographic places.

6. Nucleus for the Web of Data

The Web is currently changing from a medium to publish and share text documents into a medium to publish and share data.
This transition is facilitated by ideas from the Semantic Web community and initiatives like the W3C Linking Open Data project.

The DBpedia data set is served as Linked Data ([1], [2]) which allows the data set to be interlinked with other data sets and ontologies.

As DBpedia covers many domains and provides data-backed identifiers for 2.49 million concepts, it is developing into an interlinking-hub for other data sets.

Please see Interlinking for an overview about the data sets that are currently interlinked with DBpedia and W3C Linking Open Data for a list of other data sets and ontologies that are published on the Web as Linked Data.

7. Support Wikipedia Authors with Editing Suggestions

One strength of Wikipedia, and a central factor for its growth, is that it does not restrict contributors. On the other hand, this leads to many inconsistencies within Wikipedia, especially between the 251 different language versions.

Extracting structured data from all 251 versions of DBpedia and interlinking this data with background knowledge from ontologies like Open Cyc, SUMO, or Word Net, allows different types of consistency checks. For instance:

1. Population of Berlin within infoboxes in different languages.
2. Classification of a person in the category German cities.

Therefore, one promising direction for future work is to use DBpedia knowledge for consistency checks and to develop tools that support Wikipedia authors by offering editing/correction suggestions.