Query Rewriting or Ontology Modification? Toward a Faster Approximate Reasoning on LOD Endpoints

Naoki YAMADA; Yuji YAMAGATA; Naoki FUKUTA

doi:10.1587/transinf.2016AGP0010

Query Rewriting or Ontology Modification? Toward a Faster Approximate Reasoning on LOD Endpoints

Naoki YAMADA, Yuji YAMAGATA, Naoki FUKUTA

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On an inference-enabled Linked Open Data (LOD) endpoint, usually a query execution takes longer than on an LOD endpoint without inference engine due to its processing of reasoning. Although there are two separate kind of approaches, query modification approaches, and ontology modifications have been investigated on the different contexts, there have been discussions about how they can be chosen or combined for various settings. In this paper, for reducing query execution time on an inference-enabled LOD endpoint, we compare these two promising methods: query rewriting and ontology modification, as well as trying to combine them into a cluster of such systems. We employ an evolutionary approach to make such rewriting and modification of queries and ontologies based on the past-processed queries and their results. We show how those two approaches work well on implementing an inference-enabled LOD endpoint by a cluster of SPARQL endpoints.

Publication: IEICE TRANSACTIONS on Information Vol.E100-D No.12 pp.2923-2930

Publication Date: 2017/12/01

Publicized: 2017/09/15

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2016AGP0010

Type of Manuscript: Special Section PAPER (Special Section on Frontiers in Agent-based Technology)

Category: Artificial Intelligence, Data Mining

Authors

Naoki YAMADA
  Shizuoka University
Yuji YAMAGATA
  Shizuoka University
Naoki FUKUTA
  Shizuoka University

Keyword

semantic web, SPARQL, inference

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Naoki YAMADA, Yuji YAMAGATA, Naoki FUKUTA, "Query Rewriting or Ontology Modification? Toward a Faster Approximate Reasoning on LOD Endpoints" in IEICE TRANSACTIONS on Information, vol. E100-D, no. 12, pp. 2923-2930, December 2017, doi: 10.1587/transinf.2016AGP0010.
Abstract: On an inference-enabled Linked Open Data (LOD) endpoint, usually a query execution takes longer than on an LOD endpoint without inference engine due to its processing of reasoning. Although there are two separate kind of approaches, query modification approaches, and ontology modifications have been investigated on the different contexts, there have been discussions about how they can be chosen or combined for various settings. In this paper, for reducing query execution time on an inference-enabled LOD endpoint, we compare these two promising methods: query rewriting and ontology modification, as well as trying to combine them into a cluster of such systems. We employ an evolutionary approach to make such rewriting and modification of queries and ontologies based on the past-processed queries and their results. We show how those two approaches work well on implementing an inference-enabled LOD endpoint by a cluster of SPARQL endpoints.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2016AGP0010/_p

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@ARTICLE{e100-d_12_2923,
author={Naoki YAMADA, Yuji YAMAGATA, Naoki FUKUTA, },
journal={IEICE TRANSACTIONS on Information},
title={Query Rewriting or Ontology Modification? Toward a Faster Approximate Reasoning on LOD Endpoints},
year={2017},
volume={E100-D},
number={12},
pages={2923-2930},
abstract={On an inference-enabled Linked Open Data (LOD) endpoint, usually a query execution takes longer than on an LOD endpoint without inference engine due to its processing of reasoning. Although there are two separate kind of approaches, query modification approaches, and ontology modifications have been investigated on the different contexts, there have been discussions about how they can be chosen or combined for various settings. In this paper, for reducing query execution time on an inference-enabled LOD endpoint, we compare these two promising methods: query rewriting and ontology modification, as well as trying to combine them into a cluster of such systems. We employ an evolutionary approach to make such rewriting and modification of queries and ontologies based on the past-processed queries and their results. We show how those two approaches work well on implementing an inference-enabled LOD endpoint by a cluster of SPARQL endpoints.},
keywords={},
doi={10.1587/transinf.2016AGP0010},
ISSN={1745-1361},
month={December},}

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TY - JOUR
TI - Query Rewriting or Ontology Modification? Toward a Faster Approximate Reasoning on LOD Endpoints
T2 - IEICE TRANSACTIONS on Information
SP - 2923
EP - 2930
AU - Naoki YAMADA
AU - Yuji YAMAGATA
AU - Naoki FUKUTA
PY - 2017
DO - 10.1587/transinf.2016AGP0010
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2017
AB - On an inference-enabled Linked Open Data (LOD) endpoint, usually a query execution takes longer than on an LOD endpoint without inference engine due to its processing of reasoning. Although there are two separate kind of approaches, query modification approaches, and ontology modifications have been investigated on the different contexts, there have been discussions about how they can be chosen or combined for various settings. In this paper, for reducing query execution time on an inference-enabled LOD endpoint, we compare these two promising methods: query rewriting and ontology modification, as well as trying to combine them into a cluster of such systems. We employ an evolutionary approach to make such rewriting and modification of queries and ontologies based on the past-processed queries and their results. We show how those two approaches work well on implementing an inference-enabled LOD endpoint by a cluster of SPARQL endpoints.
ER -