Qiuru Fu¹, Shumao Zhang¹, Shuang Zhou¹, Jie Xu^1,*, Changming Zhao², Shanchao Li³, Du Xu^1,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.070493 - 09 December 2025

Abstract Knowledge graphs often suffer from sparsity and incompleteness. Knowledge graph reasoning is an effective way to address these issues. Unlike static knowledge graph reasoning, which is invariant over time, dynamic knowledge graph reasoning is more challenging due to its temporal nature. In essence, within each time step in a dynamic knowledge graph, there exists structural dependencies among entities and relations, whereas between adjacent time steps, there exists temporal continuity. Based on these structural and temporal characteristics, we propose a model named “DKGR-DR” to learn distributed representations of entities and relations by combining recurrent neural networks More >

Xiaotong Han^1,2, Yunqi Jiang^2,3, Haitao Wang^1,2, Yuan Tian^1,2,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1951-1971, 2025, DOI:10.32604/cmc.2025.060134 - 16 April 2025

Abstract Knowledge graphs (KGs), which organize real-world knowledge in triples, often suffer from issues of incompleteness. To address this, multi-hop knowledge graph reasoning (KGR) methods have been proposed for interpretable knowledge graph completion. The primary approaches to KGR can be broadly classified into two categories: reinforcement learning (RL)-based methods and sequence-to-sequence (seq2seq)-based methods. While each method has its own distinct advantages, they also come with inherent limitations. To leverage the strengths of each method while addressing their weaknesses, we propose a cyclical training method that alternates for several loops between the seq2seq training phase and the… More >

Shiming Lin^1,2,3, Ling Ye², Yijie Zhuang¹, Lingyun Lu^2,*, Shaoqiu Zheng^2,*, Chenxi Huang¹, Ng Yin Kwee⁴

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 299-317, 2024, DOI:10.32604/cmc.2024.051379 - 18 July 2024

Abstract In recent years, with the continuous development of deep learning and knowledge graph reasoning methods, more and more researchers have shown great interest in improving knowledge graph reasoning methods by inferring missing facts through reasoning. By searching paths on the knowledge graph and making fact and link predictions based on these paths, deep learning-based Reinforcement Learning (RL) agents can demonstrate good performance and interpretability. Therefore, deep reinforcement learning-based knowledge reasoning methods have rapidly emerged in recent years and have become a hot research topic. However, even in a small and fixed knowledge graph reasoning action… More >

Dezheng Zhang^1,2, Qi Jia^1,2, Shibing Yang^1,2, Xinliang Han², Cong Xu³, Xin Liu^1,4, Yonghong Xie^1,2,*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 159-170, 2022, DOI:10.32604/cmc.2022.017295 - 03 November 2021

Abstract Syndrome differentiation is the core diagnosis method of Traditional Chinese Medicine (TCM). We propose a method that simulates syndrome differentiation through deductive reasoning on a knowledge graph to achieve automated diagnosis in TCM. We analyze the reasoning path patterns from symptom to syndromes on the knowledge graph. There are two kinds of path patterns in the knowledge graph: one-hop and two-hop. The one-hop path pattern maps the symptom to syndromes immediately. The two-hop path pattern maps the symptom to syndromes through the nature of disease, etiology, and pathomechanism to support the diagnostic reasoning. Considering the… More >