Crowdsourcing Semantics for Big Data in Geoscience Applications
The interleaving of human, machine, and semantics have the potential to overcome some of the issues currently surrounding Big Data. Semantic technologies, in particular, have been shown to adequately address data integration when dealing with data size, variety, and complexity of data sources – the very definition of Big Data. Yet, for some tasks, semantic algorithms do not reach a level of accuracy that many production environments require. In this position paper, we argue that augmenting such algorithms with crowdsourcing is a viable solution. In particular, we examine Big Data within the geosciences and describe outstanding questions regarding the merger of crowdsourcing and semantics. We present our ongoing work in this area and discuss directions for future research.
& Hitzler, P.
(2013). Crowdsourcing Semantics for Big Data in Geoscience Applications. Semantics for Big Data: Papers from the AAAI Symposium.
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