  The abundance of the data in the Internet facilitates the improvement of
extraction and processing tools. The trend in the open data publishing
encourages the adoption of structured formats like CSV and RDF. However, there
is still a plethora of unstructured data on the Web which we assume contain
semantics. For this reason, we propose an approach to derive semantics from web
tables which are still the most popular publishing tool on the Web. The paper
also discusses methods and services of unstructured data extraction and
processing as well as machine learning techniques to enhance such a workflow.
The eventual result is a framework to process, publish and visualize linked
open data. The software enables tables extraction from various open data
sources in the HTML format and an automatic export to the RDF format making the
data linked. The paper also gives the evaluation of machine learning techniques
in conjunction with string similarity functions to be applied in a tables
recognition task.
