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

Abstract

Abstract: 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 Question

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?

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