Knowledge-Embedded Narrative Construction from Open Source Intelligence
Abstract
Storytelling is an innate part of language-based communication. Today, current events are reported via Open Source Intelligence (OSINT) sources like news websites, blogs, and discussion forums. Scattered and fragmented sources such as these can be better understood when organized as chains of event plot points, or narratives, that have the ability to communicate end-end stories. Though search engines can retrieve aggregated event information, they lack the ability to sequence relevant events together to form narratives about different topics. I propose an AI system inspired by Gustav Freytag’s narrative theory called the Plot Element Pyramid and use knowledge graphs to represent, chain, and reason over narratives from disparately sourced event details to better comprehend convoluted, noisy information about critical events during intelligence analysis.
BibTeX
@article{Ranade_2024, title={Knowledge-Embedded Narrative Construction from Open Source Intelligence}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/26926}, DOI={10.1609/aaai.v37i13.26926}, abstractNote={Storytelling is an innate part of language-based communication. Today, current events are reported via Open Source Intelligence (OSINT) sources like news websites, blogs, and discussion forums. Scattered and fragmented sources such as these can be better understood when organized as chains of event plot points, or narratives, that have the ability to communicate end-end stories. Though search engines can retrieve aggregated event information, they lack the ability to sequence relevant events together to form narratives about different topics. I propose an AI system inspired by Gustav Freytag’s narrative theory called the Plot Element Pyramid and use knowledge graphs to represent, chain, and reason over narratives from disparately sourced event details to better comprehend convoluted, noisy information about critical events during intelligence analysis.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Ranade, Priyanka}, year={2024}, month={Jul.}, pages={16131-16132} }