  Research publications are the primary vehicle for sharing scientific progress
in the form of new discoveries, methods, techniques, and insights.
Unfortunately, the lack of a large-scale, comprehensive, and easy-to-use
resource capturing the myriad relationships between publications, their
authors, and venues presents a barrier to applications for gaining a deeper
understanding of science. In this paper, we present PubGraph, a new resource
for studying scientific progress that takes the form of a large-scale knowledge
graph (KG) with more than 385M entities, 13B main edges, and 1.5B qualifier
edges. PubGraph is comprehensive and unifies data from various sources,
including Wikidata, OpenAlex, and Semantic Scholar, using the Wikidata
ontology. Beyond the metadata available from these sources, PubGraph includes
outputs from auxiliary community detection algorithms and large language
models. To further support studies on reasoning over scientific networks, we
create several large-scale benchmarks extracted from PubGraph for the core task
of knowledge graph completion (KGC). These benchmarks present many challenges
for knowledge graph embedding models, including an adversarial community-based
KGC evaluation setting, zero-shot inductive learning, and large-scale learning.
All of the aforementioned resources are accessible at https://pubgraph.isi.edu/
and released under the CC-BY-SA license. We plan to update PubGraph quarterly
to accommodate the release of new publications.
