GKG-LLM: A Unified Framework for Generalized Knowledge Graph
  Construction
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.
67da533bb443470b7908a048
Jian Zhang
claimed_verified
2025-03-19T09:44:13.546Z
658be7fe135580745c510323
/avatars/830e5cec4565efdc23226a86a0fcef0e.svg
Jian Zhang
user
VentureZJ
67da533bb443470b7908a049
Bifan Wei
67da533bb443470b7908a04a
Shihao Qi
67da533bb443470b7908a04b
haiping Zhu
67da533bb443470b7908a04c
Jun Liu
67da533bb443470b7908a04d
Qika Lin
admin_assigned
2025-03-20T13:28:09.851Z
66ac77011cfb12c087605acb
/avatars/54c06bd1c4c9d491470ed4162c2301ae.svg
Lin
user
Qika
GKG-LLM: A Unified Framework for Generalized Knowledge Graph
  Construction
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.
A unified framework for constructing generalized knowledge graphs improves performance across different knowledge graph types by leveraging a three-stage curriculum learning fine-tuning approach.
Generalized Knowledge Graph (GKG) knowledge graph event knowledge graph commonsense knowledge graph curriculum learning fine-tuning Large Language Models in-sample counter-task out-of-distribution (OOD) data