A clustering approach to extract data from HTML tables. Issue 6 (November 2021) - Jiménez, Patricia; Roldán, Juan C.; Corchuelo, Rafael
Abstract: HTML tables have become pervasive on the Web. Extracting their data automatically is difficult because finding the relationships between their cells is not trivial due to the many different layouts, encodings, and formats available. In this article, we introduce Melva, which is an unsupervised domain-agnostic proposal to extract data from HTML tables without requiring any external knowledge bases. It relies on a clustering approach that helps make label cells apart from value cells and establish their relationships. We compared Melva to four competitors on more than 3 000 HTML tables from the Wikipedia and the Dresden Web Table Corpus. The conclusion is that our proposal is 21.70% better than the best unsupervised competitor and equals the best supervised competitor regarding effectiveness, but it is 99.14% better regarding efficiency. Highlights: User-friendly HTML tables are a popular means to publish data. It is difficult for software agents to leverage them automatically. We present a new method to extract their data automatically. Its approach is totally unsupervised and builds on genetic clustering. It is as effective as the best supervised proposal, but far more efficient.