The cost of reasoning with RDF updates

Sana Al Azwari; John N. Wilson

The cost of reasoning with RDF updates

Sana Al Azwari, John N. Wilson

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

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154 Downloads (Pure)

Abstract

Many real world RDF collections are large compared with other real world data structures. Such large RDF collections evolve in a distributed environment.
Therefore, these changes between RDF versions need to be detected and computed in order to synchronize these changes to the other users. To cope with the evolving nature of the semantic web, it is important to understand the costs and benefits of the different change detection techniques. In this paper, we experimentally provide a detailed analysis of the overall process of RDF change detection techniques namely: explicit change detection, forward-inference change detection, backward-inference change detection and backward-inference and pruning change detection. The results show that pruning is relatively expensive by comparison with inferencing.

Original language	English
Title of host publication	Proceedings of the 9th IEEE International Conference on Semantic Computing (IEEE ICSC 2015)
Place of Publication	Piscataway, New Jersey, United States
Publisher	IEEE
Pages	328-331
Number of pages	4
ISBN (Print)	9781479979356
Publication status	Published - 6 Feb 2015

Keywords

Resource Description Framework (RDF)
reasoning
RDF data model
inference methods
distributed systems
RDF graphs

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AlAzwari-Wilson-ICSC2015-the-cost-of-reasoning-with-RDF-triplesAccepted author manuscript, 1.26 MB

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title = "The cost of reasoning with RDF updates",

abstract = "Many real world RDF collections are large compared with other real world data structures. Such large RDF collections evolve in a distributed environment. Therefore, these changes between RDF versions need to be detected and computed in order to synchronize these changes to the other users. To cope with the evolving nature of the semantic web, it is important to understand the costs and benefits of the different change detection techniques. In this paper, we experimentally provide a detailed analysis of the overall process of RDF change detection techniques namely: explicit change detection, forward-inference change detection, backward-inference change detection and backward-inference and pruning change detection. The results show that pruning is relatively expensive by comparison with inferencing.",

keywords = "Resource Description Framework (RDF), reasoning, RDF data model, inference methods, distributed systems, RDF graphs",

author = "\{Al Azwari\}, Sana and Wilson, \{John N.\}",

note = "{\textcopyright} 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.",

year = "2015",

month = feb,

day = "6",

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isbn = "9781479979356",

pages = "328--331",

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T1 - The cost of reasoning with RDF updates

AU - Al Azwari, Sana

AU - Wilson, John N.

N1 - © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2015/2/6

Y1 - 2015/2/6

N2 - Many real world RDF collections are large compared with other real world data structures. Such large RDF collections evolve in a distributed environment. Therefore, these changes between RDF versions need to be detected and computed in order to synchronize these changes to the other users. To cope with the evolving nature of the semantic web, it is important to understand the costs and benefits of the different change detection techniques. In this paper, we experimentally provide a detailed analysis of the overall process of RDF change detection techniques namely: explicit change detection, forward-inference change detection, backward-inference change detection and backward-inference and pruning change detection. The results show that pruning is relatively expensive by comparison with inferencing.

AB - Many real world RDF collections are large compared with other real world data structures. Such large RDF collections evolve in a distributed environment. Therefore, these changes between RDF versions need to be detected and computed in order to synchronize these changes to the other users. To cope with the evolving nature of the semantic web, it is important to understand the costs and benefits of the different change detection techniques. In this paper, we experimentally provide a detailed analysis of the overall process of RDF change detection techniques namely: explicit change detection, forward-inference change detection, backward-inference change detection and backward-inference and pruning change detection. The results show that pruning is relatively expensive by comparison with inferencing.

KW - Resource Description Framework (RDF)

KW - reasoning

KW - RDF data model

KW - inference methods

KW - distributed systems

KW - RDF graphs

UR - http://ieee-icsc.org/

M3 - Conference contribution book

SN - 9781479979356

SP - 328

EP - 331

BT - Proceedings of the 9th IEEE International Conference on Semantic Computing (IEEE ICSC 2015)

PB - IEEE

CY - Piscataway, New Jersey, United States

ER -

The cost of reasoning with RDF updates

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Keywords

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