SSAKG 2.0 is an open-source package for building Structural Sequential Associative Knowledge Graphs, where objects are vertices and ordered sequences form sparse connection patterns. It functions as an associative memory, allowing complete sequences to be reconstructed from partial or unordered context. Version 2.0 introduces new algorithms that exploit individual bits in computer memory to efficiently search graph connections, implemented in a hybrid Python and C architecture for performance.
- Enables sequence reconstruction from partial, unordered context using sparse graph structures.
- Version 2.0 adds bit-level memory operations for faster graph connection searches.
- Hybrid Python/C implementation balances flexibility with high-performance core operations.
- Open-source package allows practitioners to integrate structural associative memory into AI systems.