An Explainable Natural Language Framework for Identifying and Notifying Target Audiences In Enterprise Communication

TL;DR

This paper introduces an explainable NLP framework that uses RDF graphs and large language models to accurately identify target audiences in enterprise communication, enhancing transparency and efficiency.

Contribution

It presents a novel combination of RDF graph databases with LLMs for natural language query processing and explainable audience targeting in complex organizational settings.

Findings

Improved communication efficiency in large organizations.

Enhanced transparency through explainable reasoning.

Effective natural language query handling for audience identification.

Abstract

In large-scale maintenance organizations, identifying subject matter experts and managing communications across complex entities relationships poses significant challenges -- including information overload and longer response times -- that traditional communication approaches fail to address effectively. We propose a novel framework that combines RDF graph databases with LLMs to process natural language queries for precise audience targeting, while providing transparent reasoning through a planning-orchestration architecture. Our solution enables communication owners to formulate intuitive queries combining concepts such as equipment, manufacturers, maintenance engineers, and facilities, delivering explainable results that maintain trust in the system while improving communication efficiency across the organization.