Abstract
The automated construction of knowledge graphs from procedural documents is a challenging research area. Here, the lack of annotated data, as well as raw text repositories describing real-world procedural documents, make it extremely difficult to adopt deep learning approaches. Pre-trained language models have shown promising results concerning the knowledge extraction tasks from the models themselves. Although several works explored this strategy to build knowledge graph, the viability of knowledge base construction by using prompt-based learning strategy from such language models has not yet been investigated deeply. In this work, we present a prompt-based in-context learning strategy to extract, from natural language process descriptions, conceptual information that can be converted into their equivalent knowledge graphs. Such a strategy is performed in a multi-turn dialog fashion. We validate the accuracy of the proposed approach from both quantitative and qualitative perspectives. The results highlight the feasibility of the proposed approach within low-resource scenarios.