A framework for efficient representative summarization of RDF graphs

Šejla Čebirić; François Goasdoué; Ioana Manolescu

A framework for efficient representative summarization of RDF graphs

Šejla Čebirić
, François Goasdoué
, Ioana Manolescu

Research output: Contribution to journal › Conference article › peer-review

Abstract

RDF is the data model of choice for SemanticWeb applications. RDF graphs are often large and have heterogeneous, complex structure. Graph summaries are compact structures computed from the input graph; they are typically used to simplify users' experience and to speed up graph processing. We introduce a formal RDF summarization framework, based on graph quotients and RDF node equivalence; our framework can be instantiated with many such equivalence relations. We show that our summaries represent the structure and semantics of the input graph, and establish a sufficient condition on the RDF equivalence relation which ensures that a graph can be summarized more efficiently, without materializing its implicit triples.

Original language	English
Journal	CEUR Workshop Proceedings
Volume	1963
Publication status	Published - 1 Jan 2017
Event	2017 ISWC Posters and Demonstrations and Industry Tracks, ISWC-P and D-Industry 2017 - Vienna, Austria Duration: 23 Oct 2017 → 25 Oct 2017

Cite this

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title = "A framework for efficient representative summarization of RDF graphs",

abstract = "RDF is the data model of choice for SemanticWeb applications. RDF graphs are often large and have heterogeneous, complex structure. Graph summaries are compact structures computed from the input graph; they are typically used to simplify users' experience and to speed up graph processing. We introduce a formal RDF summarization framework, based on graph quotients and RDF node equivalence; our framework can be instantiated with many such equivalence relations. We show that our summaries represent the structure and semantics of the input graph, and establish a sufficient condition on the RDF equivalence relation which ensures that a graph can be summarized more efficiently, without materializing its implicit triples.",

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AU - Čebirić, Šejla

AU - Goasdoué, François

AU - Manolescu, Ioana

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N2 - RDF is the data model of choice for SemanticWeb applications. RDF graphs are often large and have heterogeneous, complex structure. Graph summaries are compact structures computed from the input graph; they are typically used to simplify users' experience and to speed up graph processing. We introduce a formal RDF summarization framework, based on graph quotients and RDF node equivalence; our framework can be instantiated with many such equivalence relations. We show that our summaries represent the structure and semantics of the input graph, and establish a sufficient condition on the RDF equivalence relation which ensures that a graph can be summarized more efficiently, without materializing its implicit triples.

AB - RDF is the data model of choice for SemanticWeb applications. RDF graphs are often large and have heterogeneous, complex structure. Graph summaries are compact structures computed from the input graph; they are typically used to simplify users' experience and to speed up graph processing. We introduce a formal RDF summarization framework, based on graph quotients and RDF node equivalence; our framework can be instantiated with many such equivalence relations. We show that our summaries represent the structure and semantics of the input graph, and establish a sufficient condition on the RDF equivalence relation which ensures that a graph can be summarized more efficiently, without materializing its implicit triples.

UR - https://www.scopus.com/pages/publications/85033498857

M3 - Conference article

AN - SCOPUS:85033498857

SN - 1613-0073

VL - 1963

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

T2 - 2017 ISWC Posters and Demonstrations and Industry Tracks, ISWC-P and D-Industry 2017

Y2 - 23 October 2017 through 25 October 2017

ER -

A framework for efficient representative summarization of RDF graphs

Abstract

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