Identifying Web Tables - Supporting a Neglected Type of Content on the Web
This work addresses the challenge of making unstructured web tables accessible as linked open data, which is incremental as it builds on existing extraction methods to improve data publishing workflows.
The paper tackles the problem of extracting semantics from unstructured web tables, which are a common but neglected data format, by developing a framework that processes and converts HTML tables into linked RDF data. It evaluates machine learning techniques and string similarity functions to enhance table recognition and extraction.
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.