SemTUI: a Framework for the Interactive Semantic Enrichment of Tabular Data
This addresses the problem of data integration for users needing to prepare datasets for ML/AI analysis, though it is incremental as it builds on existing enrichment techniques with a new interface.
The authors tackled the manual and costly process of gathering and integrating tabular data from multiple sources by developing SemTUI, a framework that uses semantics to make enrichment flexible and effective, with user tests showing it achieves table enrichment with little effort and time.
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.