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Optimising linked data queries in the presence of co-reference

Optimising linked data queries in the presence of co-reference
Optimising linked data queries in the presence of co-reference
Due to the distributed nature of Linked Data, many resources are referred to by more than one URI. This phenomenon, known as co-reference, increases the probability of leaving out implicit semantically related results when querying Linked Data. The probability of co-reference increases further when considering distributed SPARQL queries over a larger set of distributed datasets. Addressing co-reference in Linked Data queries, on one hand, increases complexity of query processing. On the other hand, it requires changes in how statistics of datasets are taken into consideration. We investigate these two challenges of addressing co-reference in distributed SPARQL queries, and propose two methods to improve query efficiency: 1) a model named Virtual Graph, that transforms a query with co-reference into a normal query with pre-existing bindings; 2) an algorithm named $\Psi$, that intensively exploits parallelism, and dynamically optimises queries using runtime statistics. We deploy both methods in an distributed engine called LHD-d. To evaluate LHD-d, we investigate the distribution of co-reference in the real world, based on which we simulate an experimental RDF network. In this environment we demonstrate the advantages of LHD-d for distributed SPARQL queries in environments with co-reference
Wang, Xin
735297cd-af6a-430e-bf68-8550d1a2f60b
Tiropanis, Thanassis
d06654bd-5513-407b-9acd-6f9b9c5009d8
Davis, Hugh C.
1608a3c8-0920-4a0c-82b3-ee29a52e7c1b
Wang, Xin
735297cd-af6a-430e-bf68-8550d1a2f60b
Tiropanis, Thanassis
d06654bd-5513-407b-9acd-6f9b9c5009d8
Davis, Hugh C.
1608a3c8-0920-4a0c-82b3-ee29a52e7c1b

Wang, Xin, Tiropanis, Thanassis and Davis, Hugh C. (2014) Optimising linked data queries in the presence of co-reference At 11th Extended Semantic Web Conference 2014 (ESWC 2014), Greece. 25 - 29 May 2014. 15 pp.

Record type: Conference or Workshop Item (Paper)

Abstract

Due to the distributed nature of Linked Data, many resources are referred to by more than one URI. This phenomenon, known as co-reference, increases the probability of leaving out implicit semantically related results when querying Linked Data. The probability of co-reference increases further when considering distributed SPARQL queries over a larger set of distributed datasets. Addressing co-reference in Linked Data queries, on one hand, increases complexity of query processing. On the other hand, it requires changes in how statistics of datasets are taken into consideration. We investigate these two challenges of addressing co-reference in distributed SPARQL queries, and propose two methods to improve query efficiency: 1) a model named Virtual Graph, that transforms a query with co-reference into a normal query with pre-existing bindings; 2) an algorithm named $\Psi$, that intensively exploits parallelism, and dynamically optimises queries using runtime statistics. We deploy both methods in an distributed engine called LHD-d. To evaluate LHD-d, we investigate the distribution of co-reference in the real world, based on which we simulate an experimental RDF network. In this environment we demonstrate the advantages of LHD-d for distributed SPARQL queries in environments with co-reference

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More information

Submitted date: 13 January 2014
e-pub ahead of print date: 26 February 2014
Venue - Dates: 11th Extended Semantic Web Conference 2014 (ESWC 2014), Greece, 2014-05-25 - 2014-05-29
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 363026
URI: http://eprints.soton.ac.uk/id/eprint/363026
PURE UUID: 4b036c0f-c90c-4950-8a27-2688b1e10f8e
ORCID for Thanassis Tiropanis: ORCID iD orcid.org/0000-0002-6195-2852
ORCID for Hugh C. Davis: ORCID iD orcid.org/0000-0002-1182-1459

Catalogue record

Date deposited: 20 Mar 2014 11:31
Last modified: 18 Jul 2017 02:44

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Contributors

Author: Xin Wang
Author: Hugh C. Davis ORCID iD

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