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VLog is an experimental Datalog engine, which combines a column-based memory layout with novel optimization methods that avoid redundant inferences at runtime, resulting in high efficiency in terms of memory usage and speed. For further information see the VLog online documentation.
VLog can be downloaded from its github repository.
Below is a list of publications concerning VLog, in one way or another.
Varsha Ravichandra Mouli, Unmesh Joshi, Ceriel Jacobs, and Jacopo Urbani. Predicting the cost of online reasoning on knowledge graphs: Some heuristics. In 16th International Semantic Web Conference 2017 - Posters and Demos (ISWC2017P&D), Vienna, Austria, 2017.
Jacopo Urbani, Ceriel J. H. Jacobs, and Markus Krötzsch. Column-oriented Datalog materialization for large knowledge graphs. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA., pages 258--264, 2016, http.
Jacopo Urbani, Ceriel J. H. Jacobs, and Markus Krötzsch. VLog: A column-oriented Datalog reasoner. In The 39th German Conference on Artificial Intelligence (KI2016), Klagenfurt, Austria, 2016.
Jacopo Urbani, Ceriel J. H. Jacobs, and Markus Krötzsch. VLog: A column-oriented datalog system for large knowledge graphs. In 5th International Semantic Web Conference 2016 - Posters and Demos (ISWC2016P&D), Kobe, Japan, 2016.