Roomba: An Extensible Framework to Validate and Build Dataset Profiles
published: July 15, 2015, recorded: June 2015, views: 1770
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Linked Open Data (LOD) has emerged as one of the largest collections of interlinked datasets on the web. In order to beneffit from this mine of data, one needs to access to descriptive information about each dataset (or metadata). This information can be used to delay data entropy, enhance dataset discovery, exploration and reuse as well as helping data portal administrators in detecting and eliminating spam. However, such metadata information is currently very limited to a few data portals where they are usually provided manually, thus being often incomplete and inconsistent in terms of quality. To address these issues, we propose a scalable automatic approach for extracting, validating, correcting and generating descriptive linked dataset profiles. This approach applies several techniques in order to check the validity of the metadata provided and to generate descriptive and statistical information for a particular dataset or for an entire data portal.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !