An Interactive Tool Implementing a Matching Approach to Confer Semantics over Tabular Data Based on Knowledge Graphs

This article introduces Kepler-aSI, a novel matching approach designed to address potential semantic gaps in tabular data by utilising a Knowledge Graph. The task poses significant challenges for machines, necessitating additional cognitive capabilities incorporated into the matching methods. Our primary objective is to devise a rapid and effective technique for annotating tabular data with relevant features extracted from a Knowledge Graph. This approach combines search and filter services with advanced text pre-processing techniques. We conducted experimental assessments within the SemTab 2021 and SemTab 2022 challenges to evaluate its performance. The obtained results demonstrate promising performance and commendable rankings, signifying the effectiveness of our proposed method.