  Modern data lakes are deeply heterogeneous in the vocabulary that is used to
describe data. We study a problem of disambiguation in data lakes: how can we
determine if a data value occurring more than once in the lake has different
meanings and is therefore a homograph? While word and entity disambiguation
have been well studied in computational linguistics, data management and data
science, we show that data lakes provide a new opportunity for disambiguation
of data values since they represent a massive network of interconnected values.
We investigate to what extent this network can be used to disambiguate values.
DomainNet uses network-centrality measures on a bipartite graph whose nodes
represent values and attributes to determine, without supervision, if a value
is a homograph. A thorough experimental evaluation demonstrates that
state-of-the-art techniques in domain discovery cannot be re-purposed to
compete with our method. Specifically, using a domain discovery method to
identify homographs has a precision and a recall of 38% versus 69% with our
method on a synthetic benchmark. By applying a network-centrality measure to
our graph representation, DomainNet achieves a good separation between
homographs and data values with a unique meaning. On a real data lake our
top-200 precision is 89%.
