  Detecting semantic concept of columns in tabular data is of particular
interest to many applications ranging from data integration, cleaning, search
to feature engineering and model building in machine learning. Recently,
several works have proposed supervised learning-based or heuristic
pattern-based approaches to semantic type annotation. Both have shortcomings
that prevent them from generalizing over a large number of concepts or
examples. Many neural network based methods also present scalability issues.
Additionally, none of the known methods works well for numerical data. We
propose $C^2$, a column to concept mapper that is based on a maximum likelihood
estimation approach through ensembles. It is able to effectively utilize vast
amounts of, albeit somewhat noisy, openly available table corpora in addition
to two popular knowledge graphs to perform effective and efficient concept
prediction for structured data. We demonstrate the effectiveness of $C^2$ over
available techniques on 9 datasets, the most comprehensive comparison on this
topic so far.
