Game Development Reference
In-Depth Information
it presents design and evaluation our own SAG-based methods. The part two of the
topic is oriented to the problem areas of designing the SAGs. It first reviews the field
of SAGs from this point of view, introducing a design aspect classification of SAGs.
Next, it projects our own SAGs through this perspective, pointing out several novel
design patterns and approaches.
Part one contains chapters as follows:
￿
Chapter 2 (State-of-the-art: semantics acquisition and crowdsourcing) reviews the
trends in semantics acquisition approaches, moving through expert and automated
approaches to crowdsourcing, which represents a broader domain of our research.
￿
Chapter 3 (State-of-the-art: semantics acquisition games) reviews the existing
crowdsourcing games and semantics acquisition games by type of the semantics
acquisition job they perform.
￿
Chapter 4 (Little Search Game: a method for lightweight domain modeling)
presents and evaluates two games that utilize principles of negative search to
assess term relationships.
￿
Chapter 5 (PexAce: a method for image metadata acquisition) presents and evalu-
ates a card game through which image tags are collected. We also present a game's
modification for acquisition of metadata for personal imagery.
￿
Chapter 6 (CityLights: amethod for musicmetadata validation) presents and evalu-
ates a question-based SAG, where player behavior indicates the validity of existing
music metadata.
The part two contains chapters as follows:
￿
Chapter 7 (State-of-the-art: design of the semantics acquisition games) reviews the
current trends and problems universal for SAG design, presents our SAG design
classification.
￿
Chapter 8 (Our SAGs: design aspects and improvements) looks at our own SAGs
presented in part one, but from the design perspectives. In particular, it points
out novel methods and “design patterns” for cold-start problem reduction, cheat-
ing detection and player expertise and confidence exploitation, introduced by our
games that can be generalized for use elsewhere.
We then conclude the topic and turn back to the crowdsourcing and reflect several
of our findings on this broader domain. We also points out future challenges for
crowdsourcing and semantics acquisition games.
References
1. Bizer, C., Heath, T., Berners-Lee, T.: Linked data—the story so far. Int. J. Semantic Web Inf.
Syst. 5 (3), 1-22 (2009)
2. Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hellmann, S.:
Dbpedia—a crystallization point for the web of data. Web Semant. 7 , 154-165 (2009)
 
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