Materializing Knowledge Bases via Trigger Graphs

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Materializing Knowledge Bases via Trigger Graphs

Efthymia TsamouraEfthymia Tsamoura,  David CarralDavid Carral,  Enrico MaliziaEnrico Malizia,  Jacopo UrbaniJacopo Urbani
Efthymia Tsamoura, David Carral, Enrico Malizia, Jacopo Urbani
Materializing Knowledge Bases via Trigger Graphs
Proceedings of the 47th International Conference on Very Large Databases, VLDB 2021, to appear
  • KurzfassungAbstract
    The chase is a well-established family of algorithms used to materialize Knowledge Bases (KBs) for tasks like query answering under dependencies or data cleaning. A general problem of chase algorithms is that they might perform redundant computations. To counter this problem, we introduce the notion of Trigger Graphs (TGs), which guide the execution of the rules avoiding redundant computations. We present the results of an extensive theoretical and empirical study that seeks to answer when and how TGs can be computed and what are the benefits of TGs when applied over real-world KBs. Our results include introducing algorithms that compute (minimal) TGs. We implemented our approach in a new engine, called GLog, and our experiments show that it can be significantly more efficient than the chase enabling us to materialize Knowledge Graphs with 17B facts in less than 40 min using a single machine with commodity hardware.
  • Bemerkung: Note: An extended version of this paper containing all formal arguments is available at https://arxiv.org/abs/2102.02753
  • Projekt:Project: CPECDIAMONDScaDS.AI
  • Forschungsgruppe:Research Group: Wissensbasierte Systeme
@inproceedings{TCMU2021,
  author    = {Efthymia Tsamoura and David Carral and Enrico Malizia and Jacopo
               Urbani},
  title     = {Materializing Knowledge Bases via Trigger Graphs},
  booktitle = {Proceedings of the 47th International Conference on Very Large
               Databases, {VLDB} 2021},
  year      = {2021}
}