Users often need to gather information about "entities" of

interest. Recent efforts try to automate this task by lever-

aging the vast corpus of HTML tables; this is referred to

as "entity augmentation". The accuracy of entity augmen-

tation 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 fifirst build a semantic graph that (i) la-

bels 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 augmen-

tation 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 se-

mantically matching columns of other web tables; this com-

plements "local" extraction from column headers. However,

this creates an interdependence between labels and seman-

tic matches; we address this challenge by representing the

task as a probabilistic graphical model that jointly discov-

ers labels and semantic matches over all columns. Our ex-

periments 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.

Publisher  ACM SIGMOD

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