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Erschienen in:
Foundations in Human Computation Kapitel lesen Erstes Kapitel lesen

2013 | OriginalPaper | Buchkapitel

Knowledge Engineering via Human Computation

verfasst von : Elena Simperl, Maribel Acosta, Fabian Flöck

Erschienen in: Handbook of Human Computation

Verlag: Springer New York

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Abstract

In this chapter, we will analyze a number of essential knowledge engineering activities that, for technical or principled reasons, can hardly be optimally executed through automatic processing approaches, thus remaining heavily reliant on human intervention. Human computation methods can be applied to this field in order to overcome these limitations in terms of accuracy, while still being able to fully take advantage of the scalability and performance of machine-driven capabilities. For each activity, we will explain how this symbiosis can be achieved by giving a short overview of the state of the art and several examples of systems and applications such as games-with-a-purpose, microtask crowdsourcing projects, and community-driven collaborative initiatives that showcase the benefits of the general idea.

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Vorheriges Kapitel The Virtuous Circle of the Quantified Self: A Human Computational Approach to Improved Health Outcomes
Nächstes Kapitel Human Computation in Citizen Science
Fußnoten
1
Especially in tasks like data annotation or data quality assessment, which involve defining and encoding the meaning of the resources published on the Web or resolving semantic conflicts such as data ambiguity or inconsistency.
 
2
A similar process model applies to the creation, management and use of instance data. Management and pre-development activities cover the entire scope of the knowledge-engineering exercise. Development, post-development and support activities are equally relevant to both schema and data, though there might be differences in their actual realization. For example, instance data is typically lifted from existing sources into the newly created ontological schema, while a greater share of activities at the ontology level are carried out manually.
 
3
Exceptions include highly contextualized systems, which require extensive training and/or background knowledge. In these cases, the manual efforts shifts from the creation and maintenance of the knowledge base to the generation of training data sets and background corpora.
 
15
Amazon Mechanical Turk: http://​mturk.​com, CrowdFlower: http://​crowdflower.​com
 
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Metadaten
Titel
Knowledge Engineering via Human Computation
verfasst von
Elena Simperl
Maribel Acosta
Fabian Flöck
Copyright-Jahr
2013
Verlag
Springer New York
Buch
Handbook of Human Computation

Print ISBN: 978-1-4614-8805-7

Electronic ISBN: 978-1-4614-8806-4

Copyright-Jahr: 2013

DOI
https://doi.org/10.1007/978-1-4614-8806-4_13