  The large availability of datasets fosters the use of \acrshort{ml} and
\acrshort{ai} technologies to gather insights, study trends, and predict unseen
behaviours out of the world of data. Today, gathering and integrating data from
different sources is mainly a manual activity that requires the knowledge of
expert users at an high cost in terms of both time and money. It is, therefore,
necessary to make the process of gathering and linking data from many different
sources affordable to make datasets ready to perform the desired analysis. In
this work, we propose the development of a comprehensive framework, named
SemTUI, to make the enrichment process flexible, complete, and effective
through the use of semantics. The approach is to promote fast integration of
external services to perform enrichment tasks such as reconciliation and
extension; and to provide users with a graphical interface to support
additional tasks, such as refinement to correct ambiguous results provided by
automatic enrichment algorithms. A task-driven user evaluation proved SemTUI to
be understandable, usable, and capable of achieving table enrichment with
little effort and time with user tests that involved people with different
skills and experiences.
