GKG-LLM: A Unified Framework for Generalized Knowledge Graph
  Construction
Authors: name: Jian Zhang, name: Bifan Wei, name: Shihao Qi, name: haiping Zhu, name: Jun Liu, name: Qika Lin

The construction of Generalized Knowledge Graph (GKG), including knowledge
graph, event knowledge graph and commonsense knowledge graph, is fundamental
for various natural language processing tasks. Current studies typically
construct these types of graph separately, overlooking holistic insights and
potential unification that could be beneficial in computing resources and usage
perspectives. However, a key challenge in developing a unified framework for
GKG is obstacles arising from task-specific differences. In this study, we
propose a unified framework for constructing generalized knowledge graphs to
address this challenge. First, we collect data from 15 sub-tasks in 29 datasets
across the three types of graphs, categorizing them into in-sample,
counter-task, and out-of-distribution (OOD) data. Then, we propose a
three-stage curriculum learning fine-tuning framework, by iteratively injecting
knowledge from the three types of graphs into the Large Language Models.
Extensive experiments show that our proposed model improves the construction of
all three graph types across in-domain, OOD and counter-task data.