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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Notes
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FAIR stands for Findability, Accessibility, Interoperability, and Reuse.
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All the official experimental values obtained and presented within the framework of this study (and challenge) are available and searchable via this link: https://www.aicrowd.com/challenges/semtab-2021. Please refer to the first author profile for a clear and detailed overview of all metrics. Note that there are 3 Rounds.
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Baazouzi, W., Kachroudi, M., Faiz, S. (2023). A Matching Approach to Confer Semantics over Tabular Data Based on Knowledge Graphs. In: Fournier-Viger, P., Hassan, A., Bellatreche, L. (eds) Model and Data Engineering. MEDI 2022. Lecture Notes in Computer Science, vol 13761. Springer, Cham. https://doi.org/10.1007/978-3-031-21595-7_17
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