# A Metamodel and Framework for Artificial General Intelligence From Theory to Practice

arXiv: https://arxiv.org/abs/2102.06112 · HTML: https://mlsanity.com/paper/2102.06112/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 68 references that are in the map.
- First posted on arXiv 11 Feb 2021.

## Builds on (most cited first, 15 of 15)

- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://mlsanity.com/paper/1905.00537/) (2019)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017)
- [A Survey on Network Embedding](https://mlsanity.com/paper/1711.08752/) (2017)
- [Blessing of dimensionality: mathematical foundations of the statistical physics of data](https://mlsanity.com/paper/1801.03421/) (2018)
- [Improving Generative Visual Dialog by Answering Diverse Questions](https://mlsanity.com/paper/1909.10470/) (2019)
- [Describing Unseen Videos via Multi-Modal Cooperative Dialog Agents](https://mlsanity.com/paper/2008.07935/) (2020)
- [Conceptual Spaces for Cognitive Architectures: A Lingua Franca for Different Levels of Representation](https://mlsanity.com/paper/1701.00464/) (2017)
- [Product recognition in store shelves as a sub-graph isomorphism problem](https://mlsanity.com/paper/1707.08378/) (2017)

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

- [Neuro-Symbolic Learning: Principles and Applications in Ophthalmology](https://mlsanity.com/paper/2208.00374/) (2022)
- [Neurosymbolic Systems of Perception & Cognition: The Role of Attention](https://mlsanity.com/paper/2112.01603/) (2021)
- [HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning](https://mlsanity.com/paper/2403.05763/) (2024)
- [Ethosight: A Reasoning-Guided Iterative Learning System for Nuanced Perception based on Joint-Embedding & Contextual Label Affinity](https://mlsanity.com/paper/2307.10577/) (2023)
- [Artificial Intelligence and Innovation Ecosystem: Evolutionary Developments, Challenges, and Future Directions](https://mlsanity.com/paper/2607.24589/) (2026)

## Abstract (arXiv)

This paper introduces a new metamodel-based knowledge representation that significantly improves autonomous learning and adaptation. While interest in hybrid machine learning / symbolic AI systems leveraging, for example, reasoning and knowledge graphs, is gaining popularity, we find there remains a need for both a clear definition of knowledge and a metamodel to guide the creation and manipulation of knowledge. Some of the benefits of the metamodel we introduce in this paper include a solution to the symbol grounding problem, cumulative learning, and federated learning. We have applied the metamodel to problems ranging from time series analysis, computer vision, and natural language understanding and have found that the metamodel enables a wide variety of learning mechanisms ranging from machine learning, to graph network analysis and learning by reasoning engines to interoperate in a highly synergistic way. Our metamodel-based projects have consistently exhibited unprecedented accuracy, performance, and ability to generalize. This paper is inspired by the state-of-the-art approaches to AGI, recent AGI-aspiring work, the granular computing community, as well as Alfred Korzybski's general semantics. One surprising consequence of the metamodel is that it not only enables a new level of autonomous learning and optimal functioning for machine intelligences, but may also shed light on a path to better understanding how to improve human cognition.