Automatic Annotation of Metadata in Power System Databases Based on Correlation Feature Selection and Natural Language Processing

The task of annotating metadata in power system databases has become more challenging due to the exponential growth of data in these databases. In order to tackle this challenge, we propose a new approach for automating the annotation of metadata. We begin by using correlation feature selection to identify the most relevant features for annotation. We then apply natural language processing techniques to extract semantic information from these selected features. The results of our experiments show that our method achieves a high level of accuracy in metadata annotation and also reduces the time required for annotation. In conclusion, our approach provides a streamlined and efficient solution for automatically annotating metadata in power system databases.

I. Introduction

Initially, correlation feature selection is employed to identify the most relevant features for metadata annotation. By analyzing the correlations between different features, the ones that exhibit the strongest relationship with the metadata are identified. This selective approach allows for a focus on the most informative features, thereby enhancing the overall accuracy of the annotation process.