  Tables in scientific papers contain a wealth of valuable knowledge for the
scientific enterprise. To help the many of us who frequently consult this type
of knowledge, we present Tab2Know, a new end-to-end system to build a Knowledge
Base (KB) from tables in scientific papers. Tab2Know addresses the challenge of
automatically interpreting the tables in papers and of disambiguating the
entities that they contain. To solve these problems, we propose a pipeline that
employs both statistical-based classifiers and logic-based reasoning. First,
our pipeline applies weakly supervised classifiers to recognize the type of
tables and columns, with the help of a data labeling system and an ontology
specifically designed for our purpose. Then, logic-based reasoning is used to
link equivalent entities (via sameAs links) in different tables. An empirical
evaluation of our approach using a corpus of papers in the Computer Science
domain has returned satisfactory performance. This suggests that ours is a
promising step to create a large-scale KB of scientific knowledge.
