In this paper, we examine the possibility of using data collected from millions of tables on the Web to extend an ontology with new attributes. There are two major challenges in using such a large number of potentially noisy tables for this task. First, table columns need to be matched to create groups of columns that represent a new (or existing) attribute for a particular class in the ontology. Second, the column groups need to be ranked according to their "usefulness" in augmenting the ontology. We show several approaches to addressing these challenges and report on the results of our extensive experiments using Web Tables from the Web Data Commons corpus, and using the DBpedia Ontology as our target ontology.