🤖 AI Summary
Addressing the challenges of automated domain ontology mapping, high human dependency, and excessive costs in semantic modeling of structured data (CSV/JSON/XML), this paper proposes the Knowledge Prompt Chaining (KPC) framework. KPC serializes graph-structured domain knowledge and injects it into large language models (LLMs) to enable structure-aware, end-to-end semantic annotation and knowledge graph generation. By integrating a prompt chaining architecture, graph-knowledge serialization, and lightweight LLM fine-tuning, KPC substantially reduces reliance on large-scale input data. Experimental results demonstrate that KPC outperforms state-of-the-art methods in both semantic annotation accuracy and knowledge graph quality. It establishes an efficient, scalable paradigm for semantic enrichment of structured data—particularly suitable for low-resource settings—while preserving fidelity to domain semantics and structural constraints.
📝 Abstract
The task of building semantics for structured data such as CSV, JSON, and XML files is highly relevant in the knowledge representation field. Even though we have a vast of structured data on the internet, mapping them to domain ontologies to build semantics for them is still very challenging as it requires the construction model to understand and learn graph-structured knowledge. Otherwise, the task will require human beings' effort and cost. In this paper, we proposed a novel automatic semantic modeling framework: Knowledge Prompt Chaining. It can serialize the graph-structured knowledge and inject it into the LLMs properly in a Prompt Chaining architecture. Through this knowledge injection and prompting chaining, the model in our framework can learn the structure information and latent space of the graph and generate the semantic labels and semantic graphs following the chains' insturction naturally. Based on experimental results, our method achieves better performance than existing leading techniques, despite using reduced structured input data.