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Triag, a framework based on triangles of RDF triples

Published: 14 June 2020 Publication History

Abstract

The success of RDF-based enterprise Knowledge Graphs partly depends on the efficiency to serve SPARQL queries over large datasets. This usually requires the optimization of a large number of joins between a query's triple patterns. A common solution to this problem is to index triples in several orders and to provide adapted query processing optimizations. In this paper, we extend this approach by proposing a framework that tackles a frequently encountered basic graph pattern: triangles. We present appropriate data structures to store these triangles, provide distributed algorithms to discover and materialize them (including inferred triangles), and detail query optimization techniques. Experimental results conducted over an Apache Spark implementation on two real-world RDF datasets emphasize the performance boost obtained with our approach.

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  • (2023)RDF(S) Store in Object-Relational DatabasesJournal of Database Management10.4018/JDM.33471035:1(1-32)Online publication date: 11-Dec-2023

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cover image ACM Conferences
SBD '20: Proceedings of The International Workshop on Semantic Big Data
June 2020
62 pages
ISBN:9781450379748
DOI:10.1145/3391274
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 14 June 2020

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Author Tags

  1. RDF triangles
  2. SPARQL
  3. indexing
  4. inference
  5. optimization

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SIGMOD/PODS '20
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SBD '20 Paper Acceptance Rate 9 of 15 submissions, 60%;
Overall Acceptance Rate 30 of 54 submissions, 56%

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  • (2023)RDF(S) Store in Object-Relational DatabasesJournal of Database Management10.4018/JDM.33471035:1(1-32)Online publication date: 11-Dec-2023

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