Exploring Naive Bayes Classifiers for Tabular Data to Knowledge Graph Matching

Abstract

The present research investigates the use of Naive Bayes classifiers to match knowledge graphs and tabular data, with particular emphasis on Column Type Annotation, Cell Entity Annotation, Column Property Annotation and Table Topic Detection. Using feature extraction techniques such as number of co-occurrences and term frequency, the study evaluates the effectiveness and performance of Naive Bayes classifiers on a variety of datasets. The proposed method is straightforward and generic, making a contribution to the field of knowledge graph matching and demonstrating the potential of Naive Bayes classifiers for the integration and interoperability of tabular data and knowledge graphs.