A distantly supervised approach for enriching product graphs with user opinions

Product Graphs (PGs) are knowledge graphs that structure the relationship of products and their characteristics. They have become very popular lately due to their potential to enable AI-related tasks in e-commerce. With the rise of social media, many dynamic and subjective information on products and their characteristics became widely available, creating an opportunity to aggregate such information to PGs. In this paper, we propose a method called PGOpi (Product Graph enriched with Opinions), whose goal is to enrich existing PGs with subjective information extracted from reviews written by customers. PGOpi uses a deep learning model to map opinions extracted from user reviews to nodes in the PG corresponding to targets of these opinions. To alleviate manual labor dependency for training the model, we devise a distant supervision strategy based on word embeddings. We have performed an extensive experimental evaluation on five product categories of two representative real-world datasets. The proposed unsupervised approach achieves superior micro F1 score over more complex unsupervised models. It also presents comparable results to a fully-supervised model.