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.Table of Contents:
Semantic Data Management: A Human-driven Process Fundamentals of Motivation and Incentives
Case Study: Motivating Employees to Annotate Content Case Study: Building a Community of
Practice Around Web Service Management and Annotation Case Study: Games with a Purpose for
Semantic Content Creation Conclusions
