Semantic segmentation of 3D point clouds is a fundamental problem in computer vision, particularly relevant to the digital preservation and automated analysis of cultural heritage. Recent deep learning models, such as Point Transformer~v3, have achieved state-of-the-art performance by leveraging attention mechanisms to capture both local geometry and long-range spatial relationships. However, purely data-driven approaches still struggle to generalize across geometrically complex or underrepresented classes and offer limited interpretability. This thesis explores a neuro-symbolic framework that integrates logical reasoning into the learning process of a transformer-based point cloud segmentation model. The proposed method combines the representational capacity of deep neural networks with the interpretability and domain awareness of Logic Tensor Networks (LTN), implemented through the LTNTorch library.