Coreference resolution aims to identify and link mentions that refer to a same entity in a text. However, Arabic coreference resolution remains under-explored due to data scarcity and linguistic complexity. Previous work has primarily relied on pre-trained language models, multilingual transformers, and cross-lingual transfer learning. Yet, these approaches often overlook syntactic and semantic structures, which are essential for capturing richer contextual representations. In this work, we focus on considering syntactic and semantic information to improve mentions linking for the Arabic language. We propose a heterogeneous graph-based model that jointly leverages these features to enhance Arabic coreference resolution. The suggested model encodes syntactic structure via dependency trees, using the Arabic parser, Camel-Parser 2.0. This model refines contextual representations by incorporating semantic role labeling (SRL), leading to a deeper semantic understanding. We conduct an analysis of our approach, comparing its performance with BERT-based methods and sequence-to-sequence models. Experiments on Arabic CoNLL-2012 shared task show that the suggested model outperforms BERT-based approaches in terms of F1 score and achieves competitive results against state-of-the-art sequence-to-sequence models.