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      "@value" : "Simple issues like syntax errors or duplicates can be easily identified and repaired in a fully automatic fash- ion. However, data quality issues in LD are more challenging to detect. Current approaches to tackle these problems still require expert human intervention, e.g., for specifying rules [14] or test cases [21], or fail due to the context-specific nature of quality assessment, which does not lend itself well to general workflows and rules that could be executed by a computer pro- gram. In this paper, we explore an alternative data cu- ration strategy, which is based on crowdsourcing."
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