Abstract
Knowledge graphs are crucial assets for tasks like query answering or data integration. These tasks can be viewed as reasoning problems, which in turn require efficient reasoning systems to be implemented. To this end, we present VLog, a rule-based reasoner designed to satisfy the requirements of modern use cases, with a focus on performance and adaptability to different scenarios. We address the former with a novel vertical storage layout, and the latter by abstracting the access to data sources and providing a platform-independent Java API. Features of VLog include fast Datalog materialisation, support for reasoning with existential rules, stratified negation, and data integration from a variety of sources, such as high-performance RDF stores, relational databases, CSV files, OWL ontologies, and remote SPARQL endpoints.
Cite
CITATION STYLE
Carral, D., Dragoste, I., González, L., Jacobs, C., Krötzsch, M., & Urbani, J. (2019). VLog: A Rule Engine for Knowledge Graphs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11779 LNCS, pp. 19–35). Springer. https://doi.org/10.1007/978-3-030-30796-7_2
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