Skip to content

Home

Jump to bottom Edit New page
Irina Dragoste edited this page Jun 3, 2021 · 14 revisions

VLog is a 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. Compared to traditional row stores, column-based approaches have shown superior performance on analytical workloads, but are deployed mostly in relational DBMSs, see for instance MonetDB. For VLog, the vertical storage leads to high memory efficiency and competitive runtimes, but also requires specific implementation strategies and data structures.

Its main features are:

  • Small memory footprint, which allows the materialisation of large databases using commodity hardware
  • Interfaces to a variety of data sources, such as relational databases, CSV, SPARQL endpoints, etc.
  • Reasoning with existential rules (a.k.a. tuple-generating dependencies); more info
  • Java integration via the Rulewerk project (formerly: VLog4j), which also adds further import formats (RDF, OWL, Graal) and a convenient knowledge-base syntax
  • Experimental implementation of a parallel evaluation of the rules

Usage

We provide two small tutorials to illustrate how the system can be used. Click here for more info.

We also provide a detailed documentation of how various databases can be configured.

Publications

The main publication on VLog is:

The following publications cover specific topics:

  • (Existential-rule reasoning in VLog) Jacopo Urbani, Markus Krötzsch, Ceriel Jacobs, Irina Dragoste, David Carral: Efficient Model Construction for Horn Logic with VLog In Didier Galmiche, Stephan Schulz, Roberto Sebastiani, eds., Proceedings of the 8th International Joint Conference on Automated Reasoning (IJCAR 2018), volume 10900 of LNCS, 680--688, 2018. Springer. PDF + bibtex

  • (Java integration, SPARQL support) David Carral, Irina Dragoste, Larry González, Ceriel Jacobs, Markus Krötzsch, Jacopo Urbani: VLog: A Rule Engine for Knowledge Graphs In Chiara Ghidini, Olaf Hartig, Maria Maleshkova, Vojtěch Svátek, Isabel Cruz, Aidan Hogan, Jie Song, Maxime Lefrançois, Fabien Gandon, eds., Proceedings of the 18th International Semantic Web Conference (ISWC'19), volume 11779 of LNCS, 17--32, 2019. Springer. PDF + bibtex

Further publications:

  • 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 Jacobs, Markus Krötzsch: VLog: A Column-Oriented Datalog System for Large Knowledge Graphs In Takahiro Kawamura, Heiko Paulheim, eds., Proceedings of the 15th International Semantic Web Conf. (ISWC'16), Posters and Demos, volume 1690 of CEUR Workshop Proceedings, 2016. CEUR-WS.org.
  • 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.
Add a custom footer
Add a custom sidebar
Clone this wiki locally