Harvesting Textual and Structured Data from the HAL Publication
  Repository
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.
66a9c622d9b96f8886143205
Francis Kulumba
claimed_verified
2024-07-31T12:49:33.440Z
622a058138f0b01c1c2b33c9
https://cdn-avatars.huggingface.co/v1/production/uploads/622a058138f0b01c1c2b33c9/fZ2T_BJU9gbXGuxgbZ_OI.jpeg
Francis Kulumba
user
Madjakul
66a9c622d9b96f8886143206
Wissam Antoun
admin_assigned
2024-07-31T08:32:32.035Z
605350699008a64d7b43b78d
https://cdn-avatars.huggingface.co/v1/production/uploads/1616431735085-605350699008a64d7b43b78d.jpeg
Wissam Antoun
user
wissamantoun
66a9c622d9b96f8886143207
Guillaume Vimont
66a9c622d9b96f8886143208
Laurent Romary
Harvesting Textual and Structured Data from the HAL Publication
  Repository
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.
HALvest bridges citation networks with HAL documents, creating a multilingual dataset for training language models, authorship attribution, and graph representation learning.
citation networks language model training authorship attribution graph representation learning link prediction knowledge graph