Understanding a large corpus of Web Tables through matching with knowledge bases - An empirical study

Abstract

Extracting and analyzing the vast amount of structured tabular data available on the Web is a challenging task and has received a significant attention in the past few years. In this paper, we present the results of our analysis of the contents of a large corpus of over 90 million Web Tables through matching table contents with instances from a public cross-domain ontology such as DBpedia. The goal of this study is twofold. First, we examine how a large-scale matching of all table contents with a knowledge base can help us gain a better understanding of the corpus beyond what we gain from simple statistical measures such as distribution of table sizes and values. Second, we show how the results of our analysis are affected by the choice of the ontology and knowledge base. The ontologies studied include DBpedia Ontology, Schema.org, YAGO, Wikidata, and Freebase. Our results can provide a guideline for practitioners relying on these knowledge bases for data analysis.