Impact of Knowledge Graph Connection Density on Robustness of Multilingual Intent Classification Models Against Noisy Spoken

The rapid expansion of e-commerce platforms generates vast amounts of unstructured product data, creating significant challenges for information retrieval, recommendation systems, and data analytics. Knowledge Graphs (KGs) offer a structured, interpretable format to organize such data, yet constructing product-specific KGs remains a complex and manual process. This paper introduces a fully automated, AI agent-driven framework for constructing product knowledge graphs directly from unstructured product descriptions. Leveraging Large Language Models (LLMs), our method operates in three stages us

Research goal: What is the impact of varying knowledge graph connection density on the robustness of multilingual intent classification models against noisy spoken input in the MInDS-14 dataset?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.5/10.