Users often need to gather information about “entities” of interest. Recent efforts try to automate this task by leveraging the vast corpus of HTML tables; this is referred to as “entity augmentation”. The accuracy of entity augmentation critically depends on semantic relationships between web tables as well as semantic labels of those tables. Current techniques work well for string-valued and static attributes but perform poorly for numeric and time-varying attributes. In this paper, we first build a semantic graph that (i) labels columns with unit, scale and timestamp information and (ii) computes semantic matches between columns even when the same numeric attribute is expressed in different units or scales. Second, we develop a novel entity augmentation API suited for numeric and time-varying attributes that leverages the semantic graph. Building the graph is challenging as such label information is often missing from the column headers. Our key insight is to leverage the wealth of tables on the web and infer label information from semantically matching columns of other web tables; this complements “local” extraction from column headers. However, this creates an interdependence between labels and semantic matches; we address this challenge by representing the task as a probabilistic graphical model that jointly discovers labels and semantic matches over all columns. Our experiments on real-life datasets show that (i) our semantic graph contains higher quality labels and semantic matches and (ii) entity augmentation based on the above graph has significantly higher precision and recall compared with the state-of-the-art.