NELL2RDF: Reading the Web, Tracking the Provenance, and Publishing it as Linked Data

Using data mining on linked open data for analyzing e-procurement information - a ma- chine learning approach to the linked data mining challenge 2013. Jan Michelfeit and Tom´ aˇ

Motivated by GetThere, a passenger information sys- tem that crowdsources transport information from users (including per- sonal data, such as their location), four scenarios

Recently, museums embraced the Seman- tic Web as a means to make this data available to the world, but the experience so far shows that publishing museum data to the Linked Data

Through a use case of world health organization data, this project shows that current linked open data technologies have a potential to represent heterogonous

The two chosen vocabularies are the OPMV (Open Provenance Model Vocabulary) [12], a lightweight imple- mentation of the community Open Provenance Model [8], and the

belong to the data space, while visual abstraction stage, view stage and visual mapping transformation belong to the visual space.. These two main blocks are connected by

In this paper, we de- scribe a light-weight provenance extension for the voiD vocabulary that allows data publishers to add provenance metadata to their datasets.. These

Most of the Linked Data is generated auto- matically by converting existing structured data sources (typically relational databases) into RDF, using an ontology that closely matches