# Neurosymbolic Graph Enrichment for Grounded World Models

arXiv: https://arxiv.org/abs/2411.12671 · HTML: https://mlsanity.com/paper/2411.12671/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in World models for language agents, one of 11 directions in World models.
- Builds on 18 of its 75 references that are in the map.
- First posted on arXiv 19 Nov 2024.

## Placed in

- World models / [World models for language agents](https://mlsanity.com/wm/world-models-for-language-agents/)

## Builds on (most cited first, 18 of 18)

- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [KG-BERT: BERT for Knowledge Graph Completion](https://mlsanity.com/paper/1909.03193/) (2019)
- [Neurosymbolic AI: The 3rd Wave](https://mlsanity.com/paper/2012.05876/) (2020)
- [Neural-Symbolic Learning and Reasoning: A Survey and Interpretation](https://mlsanity.com/paper/1711.03902/) (2017)
- [LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities](https://mlsanity.com/paper/2305.13168/) (2023)
- [Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective](https://mlsanity.com/paper/2003.00330/) (2020)
- [Neuro-Symbolic Artificial Intelligence: Current Trends](https://mlsanity.com/paper/2105.05330/) (2021)
- [LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT](https://mlsanity.com/paper/2307.06917/) (2023)
- [A Survey on Neural-symbolic Learning Systems](https://mlsanity.com/paper/2111.08164/) (2021)
- [Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review](https://mlsanity.com/paper/2202.12205/) (2022)
- [Symbolic Logic meets Machine Learning: A Brief Survey in Infinite Domains](https://mlsanity.com/paper/2006.08480/) (2020)
- [Amnestic Forgery: an Ontology of Conceptual Metaphors](https://mlsanity.com/paper/1805.12115/) (2018)
- [On the Unexpected Abilities of Large Language Models](https://mlsanity.com/paper/2308.09720/) (2023)
- [Neurosymbolic Methods for Dynamic Knowledge Graphs](https://mlsanity.com/paper/2409.04572/) (2024)
- [Language Model as an Annotator: Unsupervised Context-aware Quality Phrase Generation](https://mlsanity.com/paper/2312.17349/) (2023)
- [ChainNet: Structured Metaphor and Metonymy in WordNet](https://mlsanity.com/paper/2403.20308/) (2024)

## Built on by (most cited first, 6 of 6)

- [Graph World Models: Concepts, Taxonomy, and Future Directions](https://mlsanity.com/paper/2604.27895/) (2026)
- [Predicate Renaming via Large Language Models](https://mlsanity.com/paper/2510.25517/) (2025)
- [Enhancing multimodal analogical reasoning with Logic Augmented Generation](https://mlsanity.com/paper/2504.11190/) (2025)
- [The Medical Metaphors Corpus (MCC)](https://mlsanity.com/paper/2508.07993/) (2025)
- [Walking on the DARKSIDE](https://mlsanity.com/paper/2608.23370/) (2026)
- [An ontology for cross-sectoral crisis management: core and public health modules](https://mlsanity.com/paper/2610.01326/) (2026)

## Abstract (arXiv)

The development of artificial intelligence systems capable of understanding and reasoning about complex real-world scenarios is a significant challenge. In this work we present a novel approach to enhance and exploit LLM reactive capability to address complex problems and interpret deeply contextual real-world meaning. We introduce a method and a tool for creating a multimodal, knowledge-augmented formal representation of meaning that combines the strengths of large language models with structured semantic representations. Our method begins with an image input, utilizing state-of-the-art large language models to generate a natural language description. This description is then transformed into an Abstract Meaning Representation (AMR) graph, which is formalized and enriched with logical design patterns, and layered semantics derived from linguistic and factual knowledge bases. The resulting graph is then fed back into the LLM to be extended with implicit knowledge activated by complex heuristic learning, including semantic implicatures, moral values, embodied cognition, and metaphorical representations. By bridging the gap between unstructured language models and formal semantic structures, our method opens new avenues for tackling intricate problems in natural language understanding and reasoning.