Implicit discourse relation recognition (IDRR) addresses the classification of discourse relations between text segments without explicit connectives. Existing prompt-based methods for IDRR often rely heavily on predicting surface connectives as an indicator for the discourse relation, which is inherently limited by the capacity of pre-trained language models. Meanwhile, standard attention mechanisms in these models are easily distracted by task-irrelevant tokens. This paper proposes a Role-Focused Prompt Framework that addresses these limitations by introducing a role-centric perspective to IDRR. Our approach is built on two core innovations: (1) the incorporation of linguistically grounded semantic roles (e.g., Cause/Effect for Contingency relation) into IDRR, which directly captures the underlying argument structure that determines discourse relations, reducing reliance on connectives; (2) a focused prompt structure that condenses the input to its core semantic concepts (argument summaries, connective, and semantic roles), creating a high signal-to-noise environment for attention-based reasoning. Extensive experiments on Penn Discourse TreeBank 2.0 (PDTB 2.0) demonstrate that our framework achieves competitive results, providing complementary direction for IDRR research. Ablation studies validate that both innovations are essential to the framework. Our work demonstrates that incorporating linguistically grounded semantic roles and focusing on task-relevant concepts can effectively specialize pre-trained models for IDRR.