ORKG-Leaderboards: a systematic workflow for mining leaderboards as a knowledge graph Academic Article uri icon

abstract

  • AbstractThe purpose of this work is to describe theorkg-Leaderboard software designed to extractleaderboardsdefined astask–dataset–metrictuples automatically from large collections of empirical research papers in artificial intelligence (AI). The software can support both the main workflows of scholarly publishing, viz. as LaTeX files or as PDF files. Furthermore, the system is integrated with the open research knowledge graph (ORKG) platform, which fosters the machine-actionable publishing of scholarly findings. Thus, the systemsss output, when integrated within the ORKG’s supported Semantic Web infrastructure of representing machine-actionable ‘resources’ on the Web, enables: (1) broadly, the integration of empirical results of researchers across the world, thus enabling transparency in empirical research with the potential to also being complete contingent on the underlying data source(s) of publications; and (2) specifically, enables researchers to track the progress in AI with an overview of the state-of-the-art across the most common AI tasks and their corresponding datasets via dynamic ORKG frontend views leveraging tables and visualization charts over the machine-actionable data. Our best model achieves performances above 90% F1 on theleaderboardextraction task, thus provingorkg-Leaderboards a practically viable tool for real-world usage. Going forward, in a sense,orkg-Leaderboards transforms theleaderboardextraction task to an automated digitalization task, which has been, for a long time in the community, a crowdsourced endeavor.

publication date

  • 2024

number of pages

  • 13

start page

  • 41

end page

  • 54

volume

  • 25

issue

  • 1