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Tabular data plays an essential role in many data analytics and machinelearning tasks. Typically, tabular data does not possess any machine-readablesemantics. In this context, semantic table interpretation is crucial for makingdata analytics workflows more robust and explainable. This article proposesTab2KG - a novel method that targets at the interpretation of tables withpreviously unseen data and automatically infers their semantics to transformthem into semantic data graphs. We introduce original lightweight semanticprofiles that enrich a domain ontology's concepts and relations and representdomain and table characteristics. We propose a one-shot learning approach thatrelies on these profiles to map a tabular dataset containing previously unseeninstances to a domain ontology. In contrast to the existing semantic tableinterpretation approaches, Tab2KG relies on the semantic profiles only and doesnot require any instance lookup. This property makes Tab2KG particularlysuitable in the data analytics context, in which data tables typically containnew instances. Our experimental evaluation on several real-world datasets fromdifferent application domains demonstrates that Tab2KG outperformsstate-of-the-art semantic table interpretation baselines.

Tab2KG: Semantic Table Interpretation with Lightweight Semantic Profiles