title: Harvesting Textual and Structured Data from the HAL Publication
    Repository
summary: HAL (Hyper Articles en Ligne) is the French national publication repository,
  used by most higher education and research organizations for their open science
  policy. As a digital library, it is a rich repository of scholarly documents,
  but its potential for advanced research has been underutilized. We present
  HALvest, a unique dataset that bridges the gap between citation networks and
  the full text of papers submitted on HAL. We craft our dataset by filtering HAL
  for scholarly publications, resulting in approximately 700,000 documents,
  spanning 34 languages across 13 identified domains, suitable for language model
  training, and yielding approximately 16.5 billion tokens (with 8 billion in
  French and 7 billion in English, the most represented languages). We transform
  the metadata of each paper into a citation network, producing a directed
  heterogeneous graph. This graph includes uniquely identified authors on HAL, as
  well as all open submitted papers, and their citations. We provide a baseline
  for authorship attribution using the dataset, implement a range of
  state-of-the-art models in graph representation learning for link prediction,
  and discuss the usefulness of our generated knowledge graph structure.
paper_authors._id: 66a9c622d9b96f8886143205
paper_authors.name: Francis Kulumba
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-31T12:49:33.440Z
paper_authors._id: 622a058138f0b01c1c2b33c9
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/622a058138f0b01c1c2b33c9/fZ2T_BJU9gbXGuxgbZ_OI.jpeg
paper_authors.fullname: Francis Kulumba
paper_authors.type: user
paper_authors.user: Madjakul
paper_authors._id: 66a9c622d9b96f8886143206
paper_authors.name: Wissam Antoun
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-31T08:32:32.035Z
paper_authors._id: 605350699008a64d7b43b78d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1616431735085-605350699008a64d7b43b78d.jpeg
paper_authors.fullname: Wissam Antoun
paper_authors.type: user
paper_authors.user: wissamantoun
paper_authors._id: 66a9c622d9b96f8886143207
paper_authors.name: Guillaume Vimont
paper_authors._id: 66a9c622d9b96f8886143208
paper_authors.name: Laurent Romary
paper_title: Harvesting Textual and Structured Data from the HAL Publication
    Repository
paper_summary: HAL (Hyper Articles en Ligne) is the French national publication repository,
  used by most higher education and research organizations for their open science
  policy. As a digital library, it is a rich repository of scholarly documents,
  but its potential for advanced research has been underutilized. We present
  HALvest, a unique dataset that bridges the gap between citation networks and
  the full text of papers submitted on HAL. We craft our dataset by filtering HAL
  for scholarly publications, resulting in approximately 700,000 documents,
  spanning 34 languages across 13 identified domains, suitable for language model
  training, and yielding approximately 16.5 billion tokens (with 8 billion in
  French and 7 billion in English, the most represented languages). We transform
  the metadata of each paper into a citation network, producing a directed
  heterogeneous graph. This graph includes uniquely identified authors on HAL, as
  well as all open submitted papers, and their citations. We provide a baseline
  for authorship attribution using the dataset, implement a range of
  state-of-the-art models in graph representation learning for link prediction,
  and discuss the usefulness of our generated knowledge graph structure.
paper_ai_keywords: citation network directed heterogeneous graph graph representation learning link prediction knowledge graph structure authorship attribution