title: GKG-LLM: A Unified Framework for Generalized Knowledge Graph
    Construction
summary: 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.
paper_authors._id: 67da533bb443470b7908a048
paper_authors.name: Jian Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-19T09:44:13.546Z
paper_authors._id: 658be7fe135580745c510323
paper_authors.avatarUrl: /avatars/830e5cec4565efdc23226a86a0fcef0e.svg
paper_authors.fullname: Jian Zhang
paper_authors.type: user
paper_authors.user: VentureZJ
paper_authors._id: 67da533bb443470b7908a049
paper_authors.name: Bifan Wei
paper_authors._id: 67da533bb443470b7908a04a
paper_authors.name: Shihao Qi
paper_authors._id: 67da533bb443470b7908a04b
paper_authors.name: haiping Zhu
paper_authors._id: 67da533bb443470b7908a04c
paper_authors.name: Jun Liu
paper_authors._id: 67da533bb443470b7908a04d
paper_authors.name: Qika Lin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-20T13:28:09.851Z
paper_authors._id: 66ac77011cfb12c087605acb
paper_authors.avatarUrl: /avatars/54c06bd1c4c9d491470ed4162c2301ae.svg
paper_authors.fullname: Lin
paper_authors.type: user
paper_authors.user: Qika
paper_title: GKG-LLM: A Unified Framework for Generalized Knowledge Graph
    Construction
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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