Despite the vast amount of information encoded in Knowledge Graphs (KGs), information about the class affiliation of entities remains often incomplete. Graph Convolutional Networks (GCNs) have been shown to be effective predictors of complete information about the class affiliation of entities in KGs. However, these models do not learn the class affiliation of entities in KGs incorporating the complexity of the task, which negatively affects the models prediction capabilities. To address this problem, we introduce a Markov process-based architecture into well-known GCN architectures. This end-to-end network learns the prediction of class affiliation of entities in KGs within a Markov process. The number of computational steps is learned during training using a geometric distribution. At the same time, the loss function combines insights from the field of evidential learning. The experiments show a performance improvement over existing models in several studied architectures and datasets. Based on the chosen hyperparameters for the geometric distribution, the expected number of computation steps can be adjusted to improve efficiency and accuracy during training.

翻译：尽管知识图谱（KGs）中编码了大量信息，但关于实体类别归属的信息往往仍不完整。图卷积网络（GCNs）已被证明是预测知识图谱中实体类别完整信息的有效工具。然而，这些模型在学习实体类别归属时未能充分考虑任务的复杂性，从而影响了模型的预测能力。为解决这一问题，我们在经典GCN架构中引入了基于马尔可夫过程的架构。该端到端网络通过马尔可夫过程学习知识图谱中实体类别归属的预测。计算步骤的数量在训练过程中通过几何分布进行学习。同时，损失函数融合了证据学习领域的洞见。实验表明，在多个研究架构和数据集上，该模型性能优于现有模型。通过调整几何分布的选定超参数，可以调节预期计算步骤数，从而在训练过程中提升效率与准确性。