  Tables are a popular and efficient means of presenting structured
information. They are used extensively in various kinds of documents including
web pages. Tables display information as a two-dimensional matrix, the
semantics of which is conveyed by a mixture of structure (rows, columns),
headers, caption, and content. Recent research has started to consider tables
as first class objects, not just as an addendum to texts, yielding interesting
results for problems like table matching, table completion, or value
imputation. All of these problems inherently rely on an accurate measure for
the semantic similarity of two tables. We present TabSim, a novel method to
compute table similarity scores using deep neural networks. Conceptually,
TabSim represents a table as a learned concatenation of embeddings of its
caption, its content, and its structure. Given two tables in this
representation, a Siamese neural network is trained to compute a score
correlating with the tables' semantic similarity. To train and evaluate our
method, we created a gold standard corpus consisting of 1500 table pairs
extracted from biomedical articles and manually scored regarding their degree
of similarity, and adopted two other corpora originally developed for a
different yet similar task. Our evaluation shows that TabSim outperforms other
table similarity measures on average by app. 7% pp F1-score in a binary
similarity classification setting and by app. 1.5% pp in a ranking scenario.
