Column-oriented datalog materialization for large knowledge graphs

Jacopo Urbani; Ceriel Jacobs; Markus Krötzsch

Column-oriented datalog materialization for large knowledge graphs

Jacopo Urbani, Ceriel Jacobs, Markus Krötzsch

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

Abstract

The evaluation of Datalog rules over large Knowledge Graphs (KGs) is essential for many applications. In this paper, we present a new method of materializing Datalog inferences, which combines a column-based memory layout with novel optimization methods that avoid redundant inferences at runtime. The pro-active caching of certain subqueries further increases efficiency. Our empirical evaluation shows that this approach can often match or even surpass the performance of state-of-the-art systems, especially under restricted resources.

Original language	English
Title of host publication	30th AAAI Conference on Artificial Intelligence, AAAI 2016
Publisher	AAAI Press
Pages	258-264
Number of pages	7
ISBN (Electronic)	9781577357605
Publication status	Published - 2016
Event	30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States Duration: 12 Feb 2016 → 17 Feb 2016

Conference

Conference	30th AAAI Conference on Artificial Intelligence, AAAI 2016
Country/Territory	United States
City	Phoenix
Period	12/02/16 → 17/02/16

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title = "Column-oriented datalog materialization for large knowledge graphs",

abstract = "The evaluation of Datalog rules over large Knowledge Graphs (KGs) is essential for many applications. In this paper, we present a new method of materializing Datalog inferences, which combines a column-based memory layout with novel optimization methods that avoid redundant inferences at runtime. The pro-active caching of certain subqueries further increases efficiency. Our empirical evaluation shows that this approach can often match or even surpass the performance of state-of-the-art systems, especially under restricted resources.",

author = "Jacopo Urbani and Ceriel Jacobs and Markus Kr{\"o}tzsch",

year = "2016",

language = "English",

pages = "258--264",

booktitle = "30th AAAI Conference on Artificial Intelligence, AAAI 2016",

publisher = "AAAI Press",

note = "30th AAAI Conference on Artificial Intelligence, AAAI 2016 ; Conference date: 12-02-2016 Through 17-02-2016",

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T1 - Column-oriented datalog materialization for large knowledge graphs

AU - Urbani, Jacopo

AU - Jacobs, Ceriel

AU - Krötzsch, Markus

PY - 2016

Y1 - 2016

N2 - The evaluation of Datalog rules over large Knowledge Graphs (KGs) is essential for many applications. In this paper, we present a new method of materializing Datalog inferences, which combines a column-based memory layout with novel optimization methods that avoid redundant inferences at runtime. The pro-active caching of certain subqueries further increases efficiency. Our empirical evaluation shows that this approach can often match or even surpass the performance of state-of-the-art systems, especially under restricted resources.

AB - The evaluation of Datalog rules over large Knowledge Graphs (KGs) is essential for many applications. In this paper, we present a new method of materializing Datalog inferences, which combines a column-based memory layout with novel optimization methods that avoid redundant inferences at runtime. The pro-active caching of certain subqueries further increases efficiency. Our empirical evaluation shows that this approach can often match or even surpass the performance of state-of-the-art systems, especially under restricted resources.

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UR - http://www.scopus.com/inward/citedby.url?scp=85007190432&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85007190432

SP - 258

EP - 264

BT - 30th AAAI Conference on Artificial Intelligence, AAAI 2016

PB - AAAI Press

T2 - 30th AAAI Conference on Artificial Intelligence, AAAI 2016

Y2 - 12 February 2016 through 17 February 2016

ER -

Column-oriented datalog materialization for large knowledge graphs

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Conference

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