发表机构
Institute of Management and Information Technology; University of Information Technology and Management; Silesian University of Technology（管理与信息技术学院; 信息技术与管理大学; 西里西亚工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文介绍了开源软件包SSAKG 2.0，它可构建结构序列关联知识图，利用新算法高效检索序列，经多类序列实验验证效果，可通过GitHub和PyPI获取。
AI 中文摘要
本文介绍了SSAKG 2.0，这是一个用于构建和操作结构序列关联知识图（Structural Sequential Associative Knowledge Graphs，SSAKGs）的开源软件包。SSAKG将对象表示为图顶点，将有序序列表示为图连接的结构模式。所得的稀疏图被用作关联记忆，其中完整序列可从部分无序的上下文中重建。2.0版本引入了利用计算机内存的单个位来高效搜索图连接的新算法。该软件包用Python实现，而性能关键的图操作用C实现并通过Python接口暴露。这种混合实现提供了灵活的高级编程环境，同时降低了与大型稀疏图相关的内存和计算开销。算法使用随机生成的数值序列、来自NLTK语料库句子的序列以及mRNA序列进行评估。实验证明了该软件包从部分上下文中存储和重建序列的能力，并为评估图密度、序列长度和内存大小对检索性能的影响提供了基础。SSAKG 2.0根据Apache 2.0开源许可证分发，包含文档和可复现示例，可通过GitHub和Python软件包索引（PyPI）公开获取。
英文摘要
This article presents SSAKG 2.0, an open-source software package for constructing and operating Structural Sequential Associative Knowledge Graphs (SSAKGs). An SSAKG represents objects as graph vertices and ordered sequences as structural patterns of graph connections. The resulting sparse graph is used as an associative memory in which complete sequences can be reconstructed from a partial, unordered context. Version 2.0 introduces new algorithms that exploit individual bits of computer memory to efficiently search graph connections. The package is implemented in Python, while performance-critical graph operations are implemented in C and exposed through a Python interface. This hybrid implementation provides a flexible high-level programming environment while reducing the memory and computational overhead associated with large sparse graphs. The algorithms were evaluated using randomly generated numerical sequences, sequences derived from sentences in the NLTK corpus, and mRNA sequences. The experiments demonstrate the ability of the package to store and reconstruct sequences from partial contexts and provide a basis for evaluating the effects of graph density, sequence length, and memory size on retrieval performance. SSAKG 2.0 is distributed under the Apache 2.0 open-source license. The package includes documentation and reproducible examples and is publicly available through GitHub and the Python Package Index (PyPI).
Comments15 pages, 5 figures
