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Combining human and computation intelligence: the case of data interlinking tools

Combining human and computation intelligence: the case of data interlinking tools
Combining human and computation intelligence: the case of data interlinking tools
Interlinking is without doubt one of the most active and mature areas of research and development in semantic technologies. Over the last decade or more a multitude of approaches to match, merge and integrate ontologies, both at the schema and instance levels have been proposed and successfully applied to resolve heterogeneity issues and, more recently, to interlink RDF data sets exposed over the Web as part of the Linked Open Data Cloud. The strengths and weaknesses of existing interlinking solutions, as well as their natural limitations and principled combinations have been intensively studied, not least through community projects such as the Ontology Alignment Evaluation Initiative. Human input remains a key ingredient of the process, either as a source of domain knowledge used to train matching algorithms and to build the underlying knowledge base, or to validate automatically computed results. In this paper we describe how such human input could be acquired and used to enhance the results of existing data interlinking technology via crowdsourcing. In a survey of data interlinking tools we identify several aspects of the interlinking process that crucially rely on human contributions and explain how these aspects could be subject to a semantically enabled human computation architecture that can be set-up by extending interlinking platforms such as Silk with direct interfaces to popular microtask platforms such as Amazon's Mechanical Turk.
human computation, linked data management, data interlinking, tools, survey, semantics, ontologies, crowdsourcing, computational intelligence, microtasking
1744-2621
77-92
Simperl, Elena
40261ae4-c58c-48e4-b78b-5187b10e4f67
Wölger, Stephan
ecf08ed8-ab7c-44df-8401-3c70efa729e2
Thaler, Stefan
c2ce4356-3d1b-480b-a957-3ae82265a81c
Norton, Barry
ed2682b8-b2a5-47b6-931f-b74ab216b563
Bϋrger, Tobias
c39912b2-e9e8-4d1e-beee-c35ce730f088
Simperl, Elena
40261ae4-c58c-48e4-b78b-5187b10e4f67
Wölger, Stephan
ecf08ed8-ab7c-44df-8401-3c70efa729e2
Thaler, Stefan
c2ce4356-3d1b-480b-a957-3ae82265a81c
Norton, Barry
ed2682b8-b2a5-47b6-931f-b74ab216b563
Bϋrger, Tobias
c39912b2-e9e8-4d1e-beee-c35ce730f088

Simperl, Elena, Wölger, Stephan, Thaler, Stefan, Norton, Barry and Bϋrger, Tobias (2012) Combining human and computation intelligence: the case of data interlinking tools International Journal of Semantics, Metadata and Ontologies, 7, (2), pp. 77-92. (doi:10.1504/IJMSO.2012.050018).

Record type: Article

Abstract

Interlinking is without doubt one of the most active and mature areas of research and development in semantic technologies. Over the last decade or more a multitude of approaches to match, merge and integrate ontologies, both at the schema and instance levels have been proposed and successfully applied to resolve heterogeneity issues and, more recently, to interlink RDF data sets exposed over the Web as part of the Linked Open Data Cloud. The strengths and weaknesses of existing interlinking solutions, as well as their natural limitations and principled combinations have been intensively studied, not least through community projects such as the Ontology Alignment Evaluation Initiative. Human input remains a key ingredient of the process, either as a source of domain knowledge used to train matching algorithms and to build the underlying knowledge base, or to validate automatically computed results. In this paper we describe how such human input could be acquired and used to enhance the results of existing data interlinking technology via crowdsourcing. In a survey of data interlinking tools we identify several aspects of the interlinking process that crucially rely on human contributions and explain how these aspects could be subject to a semantically enabled human computation architecture that can be set-up by extending interlinking platforms such as Silk with direct interfaces to popular microtask platforms such as Amazon's Mechanical Turk.

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More information

Published date: 2012
Keywords: human computation, linked data management, data interlinking, tools, survey, semantics, ontologies, crowdsourcing, computational intelligence, microtasking
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 351591
URI: http://eprints.soton.ac.uk/id/eprint/351591
ISSN: 1744-2621
PURE UUID: ee8053de-3ce2-43f6-b534-c89b0ef6d8d0
ORCID for Elena Simperl: ORCID iD orcid.org/0000-0003-1722-947X

Catalogue record

Date deposited: 23 Apr 2013 12:54
Last modified: 18 Jul 2017 04:24

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Contributors

Author: Elena Simperl ORCID iD
Author: Stephan Wölger
Author: Stefan Thaler
Author: Barry Norton
Author: Tobias Bϋrger

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