Ontology-guided Knowledge Graph Construction from Maintenance Short Texts
Zeno Cauter, Nikolay Yakovets · 2024
Large-scale knowledge graph construction remains infeasible since it requires significant human-expert involvement.Further complications arise when building graphs from domainspecific data due to their unique vocabularies and associated contexts.In this work, we demonstrate the ability of open-source large language models (LLMs), such as Llama-2 and Llama-3, to extract facts from domain-specific Maintenance Short Texts (MSTs).We employ an approach which combines ontologyguided triplet extraction and in-context learning.By using only 20 semantically similar examples with the Llama-3-70B-Instruct model, we achieve performance comparable to previous methods that relied on fine-tuning techniques like SpERT and REBEL.This indicates that domain-specific fact extraction can be accomplished through inference alone, requiring minimal labeled data.This opens up possibilities for effective and efficient semiautomated knowledge graph construction for domain-specific data.