Adaptive Low-level Storage of Very Large Knowledge Graphs

Jacopo Urbani; Ceriel Jacobs

doi:10.1145/3366423.3380246

Adaptive Low-level Storage of Very Large Knowledge Graphs

Jacopo Urbani
, Ceriel Jacobs

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

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 language	English
Title of host publication	WWW '20
Subtitle of host publication	Proceedings of The Web Conference 2020
Editors	Yennun Huang, Irwin King, Tie-Yan Liu, Maarten Steen, van
Place of Publication	Taipei, Taiwan
Publisher	Association for Computing Machinery, Inc
Pages	1761-1772
Number of pages	12
ISBN (Electronic)	9781450370233
DOIs	https://doi.org/10.1145/3366423.3380246
Publication status	Published - 2020
Event	29th International World Wide Web Conference, WWW 2020 - Taipei, Taiwan, Province of China Duration: 20 Apr 2020 → 24 Apr 2020

Conference

Conference	29th International World Wide Web Conference, WWW 2020
Country/Territory	Taiwan, Province of China
City	Taipei
Period	20/04/20 → 24/04/20

Funding

Acknowledgments. We would like to thank (in alphabetical order) Peter Boncz, Martin Kersten, Stefan Manegold, and Gerhard Weikum for discussing and providing comments to improve this work. This project was partly funded by the NWO research programme 400.17.605 (VWData) and NWO VENI project 639.021.335. Some experiments presented in this paper were performed on the clusters DAS5 and SCILENS funded by NWO grants.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

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10.1145/3366423.3380246

Adaptive LowLevel Storage of Very Large Knowledge GraphsFinal published version, 1.07 MBLicence: Article 25fa Dutch Copyright Act

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Cite this

@inproceedings{46b4c7246a694c49943a6d4e67d01fb2,

title = "Adaptive Low-level Storage of Very Large Knowledge Graphs",

abstract = "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.",

author = "Jacopo Urbani and Ceriel Jacobs",

year = "2020",

doi = "10.1145/3366423.3380246",

language = "English",

pages = "1761--1772",

editor = "Yennun Huang and Irwin King and Tie-Yan Liu and \{Steen, van\}, Maarten",

booktitle = "WWW '20",

publisher = "Association for Computing Machinery, Inc",

note = "29th International World Wide Web Conference, WWW 2020 ; Conference date: 20-04-2020 Through 24-04-2020",

}

Urbani, J & Jacobs, C 2020, Adaptive Low-level Storage of Very Large Knowledge Graphs. in Y Huang, I King, T-Y Liu & M Steen, van (eds), WWW '20: Proceedings of The Web Conference 2020. Association for Computing Machinery, Inc, Taipei, Taiwan, pp. 1761-1772, 29th International World Wide Web Conference, WWW 2020, Taipei, Taiwan, Province of China, 20/04/20. https://doi.org/10.1145/3366423.3380246

Adaptive Low-level Storage of Very Large Knowledge Graphs. / Urbani, Jacopo; Jacobs, Ceriel.
WWW '20: Proceedings of The Web Conference 2020. ed. / Yennun Huang; Irwin King; Tie-Yan Liu; Maarten Steen, van. Taipei, Taiwan: Association for Computing Machinery, Inc, 2020. p. 1761-1772.

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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AB - 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.

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Adaptive Low-level Storage of Very Large Knowledge Graphs

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Conference

Funding

UN SDGs

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