  Tabular data plays an essential role in many data analytics and machine
learning tasks. Typically, tabular data does not possess any machine-readable
semantics. In this context, semantic table interpretation is crucial for making
data analytics workflows more robust and explainable. This article proposes
Tab2KG - a novel method that targets at the interpretation of tables with
previously unseen data and automatically infers their semantics to transform
them into semantic data graphs. We introduce original lightweight semantic
profiles that enrich a domain ontology's concepts and relations and represent
domain and table characteristics. We propose a one-shot learning approach that
relies on these profiles to map a tabular dataset containing previously unseen
instances to a domain ontology. In contrast to the existing semantic table
interpretation approaches, Tab2KG relies on the semantic profiles only and does
not require any instance lookup. This property makes Tab2KG particularly
suitable in the data analytics context, in which data tables typically contain
new instances. Our experimental evaluation on several real-world datasets from
different application domains demonstrates that Tab2KG outperforms
state-of-the-art semantic table interpretation baselines.
