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The purpose of this work is to describe the Orkg-Leaderboard softwaredesigned to extract leaderboards defined as Task-Dataset-Metric tuplesautomatically from large collections of empirical research papers in ArtificialIntelligence (AI). The software can support both the main workflows ofscholarly publishing, viz. as LaTeX files or as PDF files. Furthermore, thesystem is integrated with the Open Research Knowledge Graph (ORKG) platform,which fosters the machine-actionable publishing of scholarly findings. Thus thesystem output, when integrated within the ORKG's supported Semantic Webinfrastructure of representing machine-actionable 'resources' on the Web,enables: 1) broadly, the integration of empirical results of researchers acrossthe world, thus enabling transparency in empirical research with the potentialto also being complete contingent on the underlying data source(s) ofpublications; and 2) specifically, enables researchers to track the progress inAI with an overview of the state-of-the-art (SOTA) across the most common AItasks and their corresponding datasets via dynamic ORKG frontend viewsleveraging tables and visualization charts over the machine-actionable data.Our best model achieves performances above 90% F1 on the \textit{leaderboard}extraction task, thus proving Orkg-Leaderboards a practically viable tool forreal-world usage. Going forward, in a sense, Orkg-Leaderboards transforms theleaderboard extraction task to an automated digitalization task, which hasbeen, for a long time in the community, a crowdsourced endeavor.

ORKG-Leaderboards: A Systematic Workflow for Mining Leaderboards as a Knowledge Graph