Incentive-Centric Semantic Web Application Engineering
Incentive-Centric Semantic Web Application Engineering
While many Web 2.0-inspired approaches to semantic content authoring do acknowledge motivation and incentives as the main drivers of user involvement, the amount of useful human contributions actually available will always remain a scarce resource. Complementarily, there are aspects of semantic content authoring in which automatic techniques have proven to perform reliably, and the added value of human (and collective) intelligence is often a question of cost and timing. The challenge that this book attempts to tackle is how these two approaches (machine- and human-driven computation) could be combined in order to improve the cost-performance ratio of creating, managing, and meaningfully using semantic content. To do so, we need to first understand how theories and practices from social sciences and economics about user behavior and incentives could be applied to semantic content authoring. We will introduce a methodology to help software designers to embed incentives-minded functionalities into semantic applications, as well as best practices and guidelines. We will present several examples of such applications, addressing tasks such as ontology management, media annotation, and information extraction, which have been built with these considerations in mind. These examples illustrate key design issues of incentivized Semantic Web applications that might have a significant effect on the success and sustainable development of the applications: the suitability of the task and knowledge domain to the intended audience, and the mechanisms set up to ensure high-quality contributions, and extensive user involvement.
978-1608459957
Simperl, E.
40261ae4-c58c-48e4-b78b-5187b10e4f67
Cuel, R.
37f2857d-0723-4866-946b-19341f800375
Stein, M.
5916ef47-010e-48a1-bbc5-bd4a65545bbe
30 January 2013
Simperl, E.
40261ae4-c58c-48e4-b78b-5187b10e4f67
Cuel, R.
37f2857d-0723-4866-946b-19341f800375
Stein, M.
5916ef47-010e-48a1-bbc5-bd4a65545bbe
Simperl, E., Cuel, R. and Stein, M.
(2013)
Incentive-Centric Semantic Web Application Engineering
(Synthesis Lectures on the Semantic Web: Theory and Technology, 4),
San Francisco, US.
Morgan & Claypool, 117pp.
Abstract
While many Web 2.0-inspired approaches to semantic content authoring do acknowledge motivation and incentives as the main drivers of user involvement, the amount of useful human contributions actually available will always remain a scarce resource. Complementarily, there are aspects of semantic content authoring in which automatic techniques have proven to perform reliably, and the added value of human (and collective) intelligence is often a question of cost and timing. The challenge that this book attempts to tackle is how these two approaches (machine- and human-driven computation) could be combined in order to improve the cost-performance ratio of creating, managing, and meaningfully using semantic content. To do so, we need to first understand how theories and practices from social sciences and economics about user behavior and incentives could be applied to semantic content authoring. We will introduce a methodology to help software designers to embed incentives-minded functionalities into semantic applications, as well as best practices and guidelines. We will present several examples of such applications, addressing tasks such as ontology management, media annotation, and information extraction, which have been built with these considerations in mind. These examples illustrate key design issues of incentivized Semantic Web applications that might have a significant effect on the success and sustainable development of the applications: the suitability of the task and knowledge domain to the intended audience, and the mechanisms set up to ensure high-quality contributions, and extensive user involvement.
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Published date: 30 January 2013
Organisations:
Web & Internet Science
Identifiers
Local EPrints ID: 351576
URI: http://eprints.soton.ac.uk/id/eprint/351576
ISBN: 978-1608459957
PURE UUID: 42641a31-24fd-4fed-b8b0-1f62db83e372
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Date deposited: 29 Apr 2013 10:54
Last modified: 14 Mar 2024 13:41
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Author:
R. Cuel
Author:
M. Stein
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