Abstract
In this article, we present Kepler-aSI, a matching approach to overcome possible semantic gaps in tabular data by referring to a Knowledge Graph. The task proves difficult for the machines, which requires extra effort to deploy the cognitive ability in the matching methods. Indeed, the ultimate goal of our new method is to implement a fast and efficient approach to annotate tabular data with features from a Knowledge Graph. The approach combines search and filter services combined with text pre-processing techniques. The experimental evaluation was conducted in the context of the SemTab 2021 challenge and yielded encouraging and promising results referring to its performance and ranks held.
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