# Describing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG

arXiv: https://arxiv.org/abs/2303.15113 · HTML: https://mlsanity.com/paper/2303.15113/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 27 Mar 2023.

## Builds on (most cited first, 5 of 5)

- [Neural-Symbolic Learning and Reasoning: A Survey and Interpretation](https://mlsanity.com/paper/1711.03902/) (2017)
- [Thinking Fast and Slow in AI](https://mlsanity.com/paper/2010.06002/) (2020)
- [Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use cases](https://mlsanity.com/paper/2102.11965/) (2021)
- [A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems](https://mlsanity.com/paper/1905.12389/) (2019)
- [Context-Aware Embeddings for Automatic Art Analysis](https://mlsanity.com/paper/1904.04985/) (2019)

## Built on by (most cited first, 5 of 5)

- [LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT](https://mlsanity.com/paper/2307.06917/) (2023)
- [Knowledge Engineering using Large Language Models](https://mlsanity.com/paper/2310.00637/) (2023)
- [Prompt-Time Ontology-Driven Symbolic Knowledge Capture with Large Language Models](https://mlsanity.com/paper/2405.14012/) (2024)
- [Standardizing Knowledge Engineering Practices with a Reference Architecture](https://mlsanity.com/paper/2404.03624/) (2024)
- [Evaluating Class Membership Relations in Knowledge Graphs using Large Language Models](https://mlsanity.com/paper/2404.17000/) (2024)

## Abstract (arXiv)

In line with the general trend in artificial intelligence research to create intelligent systems that combine learning and symbolic components, a new sub-area has emerged that focuses on combining machine learning (ML) components with techniques developed by the Semantic Web (SW) community - Semantic Web Machine Learning (SWeML for short). Due to its rapid growth and impact on several communities in the last two decades, there is a need to better understand the space of these SWeML Systems, their characteristics, and trends. Yet, surveys that adopt principled and unbiased approaches are missing. To fill this gap, we performed a systematic study and analyzed nearly 500 papers published in the last decade in this area, where we focused on evaluating architectural, and application-specific features. Our analysis identified a rapidly growing interest in SWeML Systems, with a high impact on several application domains and tasks. Catalysts for this rapid growth are the increased application of deep learning and knowledge graph technologies. By leveraging the in-depth understanding of this area acquired through this study, a further key contribution of this paper is a classification system for SWeML Systems which we publish as ontology.