An Explainable Natural Language Framework for Identifying and Notifying Target Audiences In Enterprise Communication
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866913978918109184 |
|---|---|
| author | Lourenço, Vítor N. Dubey, Mohnish Bai, Yunfei Depeige, Audrey Jain, Vivek |
| author_facet | Lourenço, Vítor N. Dubey, Mohnish Bai, Yunfei Depeige, Audrey Jain, Vivek |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05267 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Explainable Natural Language Framework for Identifying and Notifying Target Audiences In Enterprise Communication Lourenço, Vítor N. Dubey, Mohnish Bai, Yunfei Depeige, Audrey Jain, Vivek Artificial Intelligence 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. |
| title | An Explainable Natural Language Framework for Identifying and Notifying Target Audiences In Enterprise Communication |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2508.05267 |