Metadata Model Construction and Annotation Framework to Build Product Data Repository for Cloud Manufacturing

This paper presents a framework for automatically building a product data repository, overcoming the limitations of machine understanding and the time-consuming, costly nature of manual human annotation. In this study, we focus on Personal Protective Equipment (PPE), specifically respirator mask product categories, as a case study. First, we extract product specifications from the details and sub-details sections of product pages and build < attribute, attribute-values > metadata in dictionary form. Second, to process the data, we represent numerical data with a special token [NUM] and use hierarchical clustering based on Term Frequency-Inverse Document Frequency (TF-IDF) and cosine similarity for categorical data. Third, we propose an algorithm-based method to annotate product attributes and attribute values in product descriptions using the metadata, while minimizing human efforts. Additionally, we employ BIO tagging to extract location information for attribute values, streamlining the overall annotation process. This paper contributes to the field by presenting a method for automatically generating product-related datasets through an algorithm that enables the construction of metadata without any human intervention. This approach is a crucial step toward implementing a knowledge repository in a Cloud Manufacturing (CM) environment. Particularly, by addressing time and effort concerns in the dataset construction process, this method can significantly contribute to the creation of large-scale datasets.