title: Benchmarking Recommendation, Classification, and Tracing Based on
    Hugging Face Knowledge Graph
summary: The rapid growth of open source machine learning (ML) resources, such as
  models and datasets, has accelerated IR research. However, existing platforms
  like Hugging Face do not explicitly utilize structured representations,
  limiting advanced queries and analyses such as tracing model evolution and
  recommending relevant datasets. To fill the gap, we construct HuggingKG, the
  first large-scale knowledge graph built from the Hugging Face community for ML
  resource management. With 2.6 million nodes and 6.2 million edges, HuggingKG
  captures domain-specific relations and rich textual attributes. It enables us
  to further present HuggingBench, a multi-task benchmark with three novel test
  collections for IR tasks including resource recommendation, classification, and
  tracing. Our experiments reveal unique characteristics of HuggingKG and the
  derived tasks. Both resources are publicly available, expected to advance
  research in open source resource sharing and management.
paper_authors._id: 6833f24ed5c438959f7decf9
paper_authors.name: Qiaosheng Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T09:01:00.198Z
paper_authors._id: 619ef3f253061ce00477b09e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/619ef3f253061ce00477b09e/FknZhgQhV2_3aTqIKVsTo.jpeg
paper_authors.fullname: Qiaosheng Chen
paper_authors.type: user
paper_authors.user: cqsss
paper_authors._id: 6833f24ed5c438959f7decfa
paper_authors.name: Kaijia Huang
paper_authors._id: 6833f24ed5c438959f7decfb
paper_authors.name: Xiao Zhou
paper_authors._id: 6833f24ed5c438959f7decfc
paper_authors.name: Weiqing Luo
paper_authors._id: 6833f24ed5c438959f7decfd
paper_authors.name: Yuanning Cui
paper_authors._id: 6833f24ed5c438959f7decfe
paper_authors.name: Gong Cheng
paper_title: Benchmarking Recommendation, Classification, and Tracing Based on
    Hugging Face Knowledge Graph
paper_summary: The rapid growth of open source machine learning (ML) resources, such as
  models and datasets, has accelerated IR research. However, existing platforms
  like Hugging Face do not explicitly utilize structured representations,
  limiting advanced queries and analyses such as tracing model evolution and
  recommending relevant datasets. To fill the gap, we construct HuggingKG, the
  first large-scale knowledge graph built from the Hugging Face community for ML
  resource management. With 2.6 million nodes and 6.2 million edges, HuggingKG
  captures domain-specific relations and rich textual attributes. It enables us
  to further present HuggingBench, a multi-task benchmark with three novel test
  collections for IR tasks including resource recommendation, classification, and
  tracing. Our experiments reveal unique characteristics of HuggingKG and the
  derived tasks. Both resources are publicly available, expected to advance
  research in open source resource sharing and management.
paper_ai_summary: HuggingKG, a large-scale knowledge graph, enhances open source ML resource management by enabling advanced queries and analyses via HuggingBench.
paper_ai_keywords: knowledge graph resource recommendation classification tracing multi-task benchmark