Identifying Web Tables - Supporting a Neglected Type of Content on the Web

TL;DR

This paper presents a framework for extracting, processing, and linking web tables to semantic data formats like RDF, utilizing machine learning techniques to recognize tables and derive meaningful linked open data from unstructured web sources.

Contribution

It introduces a novel approach and software framework for automatic web table recognition and conversion into linked open data, enhancing data accessibility and semantic understanding.

Findings

Machine learning techniques improve table recognition accuracy.

The framework successfully exports tables to RDF format.

Evaluation shows effective extraction from diverse web sources.

Abstract

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.