# Associative Knowledge Graphs for Efficient Sequence Storage and Retrieval

arXiv: https://arxiv.org/abs/2411.14480 · HTML: https://mlsanity.com/paper/2411.14480/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 19 Nov 2024.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Hopfield Networks is All You Need](https://mlsanity.com/paper/2008.02217/) (2020)
- [Dense Associative Memory for Pattern Recognition](https://mlsanity.com/paper/1606.01164/) (2016)
- [Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex](https://mlsanity.com/paper/1511.00083/) (2015)
- [How Can We Be So Dense? The Benefits of Using Highly Sparse Representations](https://mlsanity.com/paper/1903.11257/) (2019)
- [Associative Long Short-Term Memory](https://mlsanity.com/paper/1602.03032/) (2016)
- [Stopwords in Technical Language Processing](https://mlsanity.com/paper/2006.02633/) (2020)

## Built on by (most cited first, 1 of 1)

- [SSAKG 2.0: An Open-Source Package for Structural Associative Sequence Memory and Context-Based Retrieval](https://mlsanity.com/paper/2609.01849/) (2026)

## Abstract (arXiv)

The paper addresses challenges in storing and retrieving sequences in contexts like anomaly detection, behavior prediction, and genetic information analysis. Associative Knowledge Graphs (AKGs) offer a promising approach by leveraging sparse graph structures to encode sequences. The objective was to develop a method for sequence storage and retrieval using AKGs that maintain high memory capacity and context-based retrieval accuracy while introducing algorithms for efficient element ordering. The study utilized Sequential Structural Associative Knowledge Graphs (SSAKGs). These graphs encode sequences as transitive tournaments with nodes representing objects and edges defining the order. Four ordering algorithms were developed and tested: Simple Sort, Node Ordering, Enhanced Node Ordering, and Weighted Edges Node Ordering. The evaluation was conducted on synthetic datasets consisting of random sequences of varying lengths and distributions, and real-world datasets, including sentence-based sequences from the NLTK library and miRNA sequences mapped symbolically with a window-based approach. Metrics such as precision, sensitivity, and specificity were employed to assess performance. SSAKGs exhibited quadratic growth in memory capacity relative to graph size. This study introduces a novel structural approach for sequence storage and retrieval. Key advantages include no training requirements, flexible context-based reconstruction, and high efficiency in sparse memory graphs. With broad applications in computational neuroscience and bioinformatics, the approach offers scalable solutions for sequence-based memory tasks.