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Open world database management systems assume tuples not in the database still exist and are becoming an increas- ingly important area of research. We present Themis, the first open world database that automatically rebalances ar- bitrarily biased samples to approximately answer queries as if they were issued over the entire population. We lever- age apriori population aggregate information to develop and combine two different approaches for automatic debiasing: sample reweighting and Bayesian network probabilistic mod- eling. We build a prototype of Themis and demonstrate that Themis achieves higher query accuracy than the default AQP approach, an alternative sample reweighting technique, and a variety of Bayesian network models while maintaining in- teractive query response times. We also show that Themis is robust to differences in the support between the sample and population, a key use case when using social media samples.