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Research publications are the primary vehicle for sharing scientific progressin the form of new discoveries, methods, techniques, and insights.Unfortunately, the lack of a large-scale, comprehensive, and easy-to-useresource capturing the myriad relationships between publications, theirauthors, and venues presents a barrier to applications for gaining a deeperunderstanding of science. In this paper, we present PubGraph, a new resourcefor studying scientific progress that takes the form of a large-scale knowledgegraph (KG) with more than 385M entities, 13B main edges, and 1.5B qualifieredges. PubGraph is comprehensive and unifies data from various sources,including Wikidata, OpenAlex, and Semantic Scholar, using the Wikidataontology. Beyond the metadata available from these sources, PubGraph includesoutputs from auxiliary community detection algorithms and large languagemodels. To further support studies on reasoning over scientific networks, wecreate several large-scale benchmarks extracted from PubGraph for the core taskof knowledge graph completion (KGC). These benchmarks present many challengesfor knowledge graph embedding models, including an adversarial community-basedKGC 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 quarterlyto accommodate the release of new publications.

PubGraph: A Large-Scale Scientific Knowledge Graph