DETEXA: Declarative Extensible Text Exploration and Analysis

Abstract

Metadata enrichment through text mining techniques is becoming one of the most significant tasks in digital libraries. Due to the pandemic increase of open access publications, several new challenges have emerged. Raw data are usually big, unstructured, and come from heterogeneous data sources. In this paper, we introduce a text analysis framework which is implemented in extended SQL and exploits the scalability characteristics of modern database management systems. The purpose of this framework is to provide the opportunity to build performant end-to-end text mining pipelines which includes data harvesting, cleaning, processing, and text analysis at once. SQL is selected due to its declarative nature which offers fast experimentation and the ability to build APIs, so that domain experts can edit text mining workflows via easy-to-use graphical interfaces. Our experimental analysis demonstrates that the proposed framework is very effective and achieves significant speedup in common use cases compared to other popular approaches.