  Automatically annotating column types with knowledge base (KB) concepts is a
critical task to gain a basic understanding of web tables. Current methods rely
on either table metadata like column name or entity correspondences of cells in
the KB, and may fail to deal with growing web tables with incomplete meta
information. In this paper we propose a neural network based column type
annotation framework named ColNet which is able to integrate KB reasoning and
lookup with machine learning and can automatically train Convolutional Neural
Networks for prediction. The prediction model not only considers the contextual
semantics within a cell using word representation, but also embeds the
semantics of a column by learning locality features from multiple cells. The
method is evaluated with DBPedia and two different web table datasets, T2Dv2
from the general Web and Limaye from Wikipedia pages, and achieves higher
performance than the state-of-the-art approaches.
