Applying Semantic Correlation Rules in Technical Communication for Generating Logic- Driven Cross-References in Static Publications

Abstract

Authors in the field of technical communication are responsible for producing and enriching content with cross-references to create highquality static publications. However, manual cross-reference generation can lead to inefficiencies, inconsistencies, and errors. This research explores the automation of cross-reference generation in the publication process by utilizing semantic correlation rules (SCR) to logically link content. The study tested a novel approach that separates cross-references from content and enriches them during publication. The method involves defining use cases, extracting structure, and enriching cross-references based on metadata. This is implemented using Python and semantic technology. The study highlights benefits such as enhanced consistency and functionality. Future work involves refining the methodology and exploring extensions to SCR, which offers broader applications beyond cross-reference generation.