The extraction and maintenance of Linked Data datasets is a cumbersome, time-consuming and resource-intensive activity. The cost for producing Linked Data can be reduced by a workflow management system, which describes plans to systematically support the lifecycle of RDF datasets. We present the LODFlow Linked Data Workflow Management System, which provides an environment for planning, executing, reusing, and documenting Linked Data workflows.The LODFlow approach is based on a comprehensive knowledge model for describing the workflows and a workflow execution engine supporting systematic workflow execution, reporting, and exception handling. The environment was evaluated in a large-scale real-world use case. As result, LODFlow supports Linked Data engineers to systematically plan, execute and assess Linked Data production and maintenance workflows, thus improving efficiency, ease-of-use, reproducibility, reuseability and provenance.
National Chung Hsing University, Taiwan, University of Bonn & Fraunhofer IAIS
www.cs.uni-bonn.de
Professor and director
Leibniz Information Center for Science & Technology
https://www.tib.eu/en/
Following stations at the universities of Dresden, Ekaterinburg, Leipzig, Pennsylvania, Bonn and the Fraunhofer Society, Prof. Auer was appointed Professor of Data Science and Digital Libraries at Leibniz Universität Hannover and Director of the TIB in 2017. Prof. Auer has made important contributions to semantic technologies, knowledge engineering and information systems.
