<p>Argument relation classification (ARC) between argument components (ACs) has made significant progress in recent years. However, many existing approaches either rely heavily on external knowledge or on linguistic information encoded in Pre-trained Language Models (PLMs) or large language models, often neglecting the extraction of fine-grained, semantic information within ACs. This information is essential for developing strategies tailored to the specific challenges of ARC tasks. To address this, we propose leveraging Frame Semantic Parsing (FSP), an open-source transformer, to extract semantic frames. These frames, consisting of triggers and arguments along with their roles, represent the semantic relationships within ACs. We then design two types of prompt templates: one for triggers and arguments, and another for frames and roles, to generate conceptual information that facilitates ARC. Finally, we utilize the RoBERTa PLM model, training it with the two types of prompt templates using a Siamese network architecture, which encodes two inputs separately, with multi-head attention. Extensive experiments across six domain-specific argument mining datasets demonstrate that our FSP–ARC approach yields competitive results compared to four state-of-the-art baselines in terms of accuracy, precision, recall, and macro Macro F1s.</p>