This article presents a neurosymbolic approach to knowledge graph enrichment, leveraging the strengths of large language models (LLMs) and structured semantic representations. The method utilizes LLMs to generate a natural language description from an image input, which is then transformed into an Abstract Meaning Representation (AMR) graph and further formalized as an ontology-based knowledge graph. This graph is then iteratively extended with implicit knowledge, such as presuppositions, conversational implicatures, and moral values, by applying a series of heuristics. By bridging the gap between unstructured language models and formal semantic structures, the proposed method opens new avenues for tackling intricate problems in natural language understanding and reasoning.

https://arxiv.org/pdf/2411.12671