  Generating schema labels automatically for column values of data tables has
many data science applications such as schema matching, and data discovery and
linking. For example, automatically extracted tables with missing headers can
be filled by the predicted schema labels which significantly minimizes human
effort. Furthermore, the predicted labels can reduce the impact of inconsistent
names across multiple data tables. Understanding the connection between column
values and contextual information is an important yet neglected aspect as
previously proposed methods treat each column independently. In this paper, we
propose a context-aware semantic labeling method using both the column values
and context. Our new method is based on a new setting for semantic labeling,
where we sequentially predict labels for an input table with missing headers.
We incorporate both the values and context of each data column using the
pre-trained contextualized language model, BERT, that has achieved significant
improvements in multiple natural language processing tasks. To our knowledge,
we are the first to successfully apply BERT to solve the semantic labeling
task. We evaluate our approach using two real-world datasets from different
domains, and we demonstrate substantial improvements in terms of evaluation
metrics over state-of-the-art feature-based methods.
