DBpedia is a crowd-sourced community effort to extract structured information from Wikipedia and make this information available on the Web. DBpedia allows you to ask sophisticated queries against Wikipedia, and to link the different data sets on the Web to Wikipedia data. We hope that this work will make it easier for the huge amount of information in Wikipedia to be used in some new interesting ways. Furthermore, it might inspire new mechanisms for navigating, linking, and improving the encyclopedia itself. Upcoming Events
Are you interested in how things are related with each other? The RelFinder helps to get an overview: It extracts and visualizes relationships between given objects in RDF data and makes these relationships interactively explorable. Highlighting and filtering features support visual analysis both on a global and detailed level. RelFinder - Interactive Relationship Discovery in RDF Datasets
For the last 150 years, The New York Times has maintained one of the most authoritative news vocabularies ever developed. In 2009, we began to publish this vocabulary as linked open data. The Data As of 13 January 2010, The New York Times has published approximately ,10,000 subject headings as linked open data under a CC BY license.
Now that we’ve published nearly 10,000 of our tags as Linked Open Data, you’re probably wondering what kind of cool applications you can build with this data. To help you get started (and since linked data applications are a little different from your average Web application), we thought we’d provide a sample application and detailed information about how we built it. Our sample application, “Who Went Where,” lets you explore recent Times coverage of the alumni of a specified college or university. The Who Went Where application (click for larger image)
Virtuoso SPARQL Query Form
Generating RDF from data.gov - Data-gov Wiki From Data-gov Wiki Overview Many of the datasets in data.gov are available as tables (spreadsheets). This makes it easy to translate the datasets into RDF by generating a triple for each table cell where the row id is the subject, the column name is the predicate, and the cell content is the object. Our work adopted the following principles: