DBpedia provides a complementary service to Wikipedia by exposing knowledge (from 130 Wikimedia projects, in particular the English Wikipedia, Commons, Wikidata and over 100 Wikipedia language editions) in a quality-controlled form compatible with tools covering ad-hoc structured data querying, business intelligence & analytics, entity extraction, natural language processing, reasoning & inference, machine learning services, and artificial intelligence in general. Data is published strictly in line with “Linked Data” principles using open standards (e.g., URIs, HTTP, HTML, RDF, and SPARQL) and open data licensing.
By providing the critical kernel around which the Linked Open Data Cloud blossomed, DBpedia enabled that cloud to become today’s massive reference database that allows anyone to look up the description of an entity based on either its hyperlink-based identifier or its literal label.
DBpedia has been, and continues to operate as a key part in the public information infrastructure and a major focal point for research and expertise related to artificial intelligence (machine learning, natural language processing, and knowledge management).
Since 2008, DBpedia has been the precursor of today’s knowledge graphs, driving prototyping, proof-of-concepts and innovation. Many enterprises, such as Apple (via Siri), Google (via Freebase and Google Knowledge Graph), and IBM (via Watson), and particularly their respective high-visibility projects associated with artificial intelligence, have adopted the idea of data extraction from Wikipedia (and the web in general) and now build their own knowledge bases (KBs) for their major applications (such as the Google ranking algorithm). In order to keep up, DBpedia is a public KB that — if properly invested in — can live up to and even surpass its closed counterparts.
In a European context, DBpedia has proven to be one of the few Open Data sources where useful information for libraries, small to medium enterprises (SME) and large, multilingual and multi-national European organisations is ready to be exploited for their business cases such as the creation of internal data spaces from text and other sources, thesaurus management (from taxonomies to knowledge graphs) as well as data quality assurance and benchmarking.
DBpedia also remains a staple for academic pursuits in the areas of Information Architecture, Ontology Design, Artificial Intelligence, Machine Learning, Natural Language Processing, and more. It has provided fodder for experts driving projects in these areas at Google, Microsoft, Facebook, Oracle, IBM, Apple, and many others.
How can you support DBpedia?
… consider a donation to the DBpedia Association: Donation page
… join the DBpedia community for improving the quality or extending the knowledge base: Get Involved
… if you would like to invest in a particular feature of DBpedia or need customized data, we can mediate professional services
If you have any questions, please contact us.
What will we achieve with the money?
Continued support and innovation behind this most powerful public reference database. Every contribution you make adds to a pool of contributions from others that collectively validate the purpose and utility of this project.
building a better DBpedia for its users
systematically develop DBpedia’s public data, software and services
continously merge community contributions into the core assets of DBpedia, i.e. ease barriers for upstream committing and connect with community members to integrate their solutions and extensions
Web Services (SPARQL & Lookup API) and Linked Data
increase availability and performance scalability of the SPARQL Query & Linked Data Deployment services, i.e. higher uptime and more concurrent users for our public web services around the world
host more data
implement new features, that are requested by the community
Extract, Transform, Load
adapt our extractors to match the changes and the growth in Wikipedias content
include more data sources to fuse into the DBpedia core data set
focus on data in Wikipedia that is harder to extract with good quality such as article text, tables, references, etc.
provide all extracted data for download as dumpfile
- improve data quality assurance
- our release manager provides fresh releases twice a year: http://downloads.dbpedia.org
- we have implemented quality checks, which we are constantly improving
- better documentation making the releases more business-ready, i.e. detect identifier changes
better publicity and exploitation (community events, booths, flyers, add use cases to projects)
community building, networking and coordination of community actions
support the Language Chapters of DBpedia to achieve better data quality from extraction of non-English Wikipedia versions
support for member and community issues, e.g. query and linked data service availability, bugs, travel grants, etc.
Thank you for supporting DBpedia!