Adaptive Low-level Storage of Very Large Knowledge Graphs

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The increasing availability and usage of Knowledge Graphs (KGs) on the Web calls for scalable and general-purpose solutions to store this type of data structures. We propose Trident, a novel storage architecture for very large KGs on centralized systems. Trident uses several interlinked data structures to provide fast access to nodes and edges, with the physical storage changing depending on the topology of the graph to reduce the memory footprint. In contrast to single architectures designed for single tasks, our approach offers an interface with few low-level and general-purpose primitives that can be used to implement tasks like SPARQL query answering, reasoning, or graph analytics. Our experiments show that Trident can handle graphs with 1011 edges using inexpensive hardware, delivering competitive performance on multiple workloads.

Original languageEnglish
Title of host publicationWWW '20
Subtitle of host publicationProceedings of The Web Conference 2020
EditorsYennun Huang, Irwin King, Tie-Yan Liu, Maarten Steen, van
Place of PublicationTaipei, Taiwan
PublisherAssociation for Computing Machinery, Inc
Number of pages12
ISBN (Electronic)9781450370233
Publication statusPublished - 20 Apr 2020
Event29th International World Wide Web Conference, WWW 2020 - Taipei, Taiwan, Province of China
Duration: 20 Apr 202024 Apr 2020


Conference29th International World Wide Web Conference, WWW 2020
Country/TerritoryTaiwan, Province of China


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