  Automatically identifying data types of web structured data is a key step in
the process of web data integration. Web structured data is usually associated
with entities or objects in a particular domain. In this paper, we aim to map
attributes of an entity in a given domain to pre-specified classes of
attributes in the same domain based on their values. To perform this task, we
propose a hybrid deep learning network that relies on the format of the
attributes' values. It does so without any pre-processing or using pre-defined
hand-crafted features. The hybrid network combines sequence-based neural
networks, namely convolutional neural networks (CNN) and recurrent neural
networks (RNN), to learn the sequence structure of attributes' values. The CNN
captures short-distance dependencies in these sequences through a sliding
window approach, and the RNN captures long-distance dependencies by storing
information of previous characters. These networks create different vector
representations of the input sequence which are combined using a pooling layer.
This layer applies a specific operation on these vectors in order to capture
their most useful patterns for the task. Finally, on top of the pooling layer,
a softmax function predicts the label of a given attribute value. We evaluate
our strategy in four different web domains. The results show that the pooling
network outperforms previous approaches, which use some kind of input
pre-processing, in all domains.
