# Learning Semantic Association Rules from Internet of Things Data

arXiv: https://arxiv.org/abs/2412.03417 · HTML: https://mlsanity.com/paper/2412.03417/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 58 references that are in the map.
- First posted on arXiv 4 Dec 2024.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Ontologies in Digital Twins: A Systematic Literature Review](https://mlsanity.com/paper/2308.15168/) (2023)
- [Numerical Association Rule Mining: A Systematic Literature Review](https://mlsanity.com/paper/2307.00662/) (2023)
- [Association Rules Mining with Auto-Encoders](https://mlsanity.com/paper/2304.13717/) (2023)
- [The Impact of Discretization Method on the Detection of Six Types of Anomalies in Datasets](https://mlsanity.com/paper/2008.12330/) (2020)
- [Semantic Association Rule Learning from Time Series Data and Knowledge Graphs](https://mlsanity.com/paper/2310.07348/) (2023)
- [Knowledge Graphs in the Digital Twin: A Systematic Literature Review About the Combination of Semantic Technologies and Simulation in Industrial Automation](https://mlsanity.com/paper/2406.09042/) (2024)
- [Time series numerical association rule mining variants in smart agriculture](https://mlsanity.com/paper/2212.03669/) (2022)

## Built on by (most cited first, 2 of 2)

- [Neurosymbolic Association Rule Mining from Tabular Data](https://mlsanity.com/paper/2504.19354/) (2025)
- [Discovering Association Rules in High-Dimensional Small Tabular Data](https://mlsanity.com/paper/2509.20113/) (2025)

## Abstract (arXiv)

Association Rule Mining (ARM) is the task of discovering commonalities in data in the form of logical implications. ARM is used in the Internet of Things (IoT) for different tasks including monitoring and decision-making. However, existing methods give limited consideration to IoT-specific requirements such as heterogeneity and volume. Furthermore, they do not utilize important static domain-specific description data about IoT systems, which is increasingly represented as knowledge graphs. In this paper, we propose a novel ARM pipeline for IoT data that utilizes both dynamic sensor data and static IoT system metadata. Furthermore, we propose an Autoencoder-based Neurosymbolic ARM method (Aerial) as part of the pipeline to address the high volume of IoT data and reduce the total number of rules that are resource-intensive to process. Aerial learns a neural representation of a given data and extracts association rules from this representation by exploiting the reconstruction (decoding) mechanism of an autoencoder. Extensive evaluations on 3 IoT datasets from 2 domains show that ARM on both static and dynamic IoT data results in more generically applicable rules while Aerial can learn a more concise set of high-quality association rules than the state-of-the-art with full coverage over the datasets.