Distributed human computation framework for linked data co-reference resolution

Abstract

Distributed Human Computation (DHC) is a technique used to solve computational problems by incorporating the collaborative effort of a large number of humans. It is also a solution to AI-complete problems such as natural language processing. The Semantic Web with its root in AI is envisioned to be a decentralised world-wide information space for sharing machine-readable data with minimal integration costs. There are many research problems in the Semantic Web that are considered as AI-complete problems. An example is co-reference resolution, which involves determining whether different URIs refer to the same entity. This is considered to be a significant hurdle to overcome in the realisation of large-scale Semantic Web applications. In this paper, we propose a framework for building a DHC system on top of the Linked Data Cloud to solve various computational problems. To demonstrate the concept, we are focusing on handling the co-reference resolution in the Semantic Web when integrating distributed datasets. The traditional way to solve this problem is to design machine-learning algorithms. However, they are often computationally expensive, error-prone and do not scale. We designed a DHC system named iamResearcher, which solves the scientific publication author identity co-reference problem when integrating distributed bibliographic datasets. In our system, we aggregated 6 million bibliographic data from various publication repositories. Users can sign up to the system to audit and align their own publications, thus solving the co-reference problem in a distributed manner. The aggregated results are published to the Linked Data Cloud.

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Identifiers

ORCID for Nicholas Gibbins: orcid.org/0000-0002-6140-9956

ORCID for Wendy Hall: orcid.org/0000-0003-4327-7811

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