Extracting the attribute value of a product from the given product description is essential for ecommerce functions like product recommendations, search, and information retrieval. Therefore, understanding products in E-commerce. Greater accuracy certainly gives any retailer the edge. The burdensome aspect of this problem lies in the diversity of the products and their attributes and values. Existing solutions typically employ large language models or sequence-tagging approaches to capture the context of a given product description and extract attribute values. However, they do so with limited accuracy, which serves as the underlying motivation to explore a more comprehensive solution. Through this paper, we present a novel approach for attribute value extraction from product description leveraging graphs and graph neural networks. Our proposed method demonstrates improvements in attribute value extraction accuracy compared to the baseline sequence tagging approaches.