RoBERTa-GCN: A New Method for Relation Extraction in Automobile Accessory Domain

Abstract

The automotive industry's rapid expansion has sparked increasing interest in the realm of automotive accessories. Navigating vast information landscapes to find accurate matches has become paramount. Leveraging cutting-edge information technologies, such as knowledge graphs and graph database-based question-answering systems, offers a crucial avenue for enhancing search efficiency. Addressing challenges posed by the domain's specialized terminology and intricate relationships, this paper introduces an innovative approach that combines a pre-trained model (RoBERTa) with graph convolutional networks (GCN). Initially, the text undergoes processing through the pre-trained model, yielding semantic feature vectors that enhance comprehension of industry-specific terminology. Subsequently, a graph convolutional network (GCN) is employed to process these semantic vectors, capturing a broader scope of neighboring vector node information. This approach not only strengthens the relationships between semantic information but also captures the intricate interconnections among entities. Ultimately, an automotive accessory query knowledge graph question-answering system is constructed using extracted entity relationship triplets. Experimental results demonstrate that the proposed RoBERTa-GCN model outperforms other baseline models, achieving an impressive F1 score of 83.93%. This research significantly enhances query capabilities and exhibits versatility in handling natural language inputs from diverse users.