The book employs various computational models and algorithms to analyze and process knowledge graphs, focusing on enhancing their efficiency and accuracy. It explores parallel reasoning algorithms and high-performance computing architectures like neural networks to improve reasoning efficiency. The book also investigates the parallel tractability of knowledge graph reasoning, identifying datalog programs with reasoning complexity bounded by NC complexity, which is parallelly tractable. This research has implications for real-world applications, such as enhancing the performance of knowledge graph reasoning systems, enabling the creation of parallel tractable knowledge graphs, and constructing high-performance computing architectures like logic neural networks, ultimately leading to more efficient and effective knowledge graph applications.