Scalable Uncertainty Reasoning in Knowledge Graphs
A new paper titled "Scalable Uncertainty Reasoning in Knowledge Graphs" by Jingcheng Wu has been submitted to arXiv. The paper explores methods for handling uncertainty within knowledge graphs, utilizing various bibliographic and code-related tools for its development and dissemination.
A new research paper by Jingcheng Wu, "Scalable Uncertainty Reasoning in Knowledge Graphs," has been recently submitted to arXiv. The paper, currently awaiting DOI registration via DataCite, delves into advanced methods for incorporating and managing uncertainty within complex knowledge graph structures. Jingcheng Wu submitted the paper on May 15, 2026.
The submission leverages a variety of bibliographic and citation tools to enhance its academic rigor and discoverability. These include NASA ADS, Google Scholar, Semantic Scholar, and BibTeX export for streamlined referencing.
Furthermore, the research integrates with several platforms for code, data, and media, indicating a strong emphasis on reproducibility and open science. Tools like alphaXiv, CatalyzeX Code Finder, DagsHub, Hugging Face, and ScienceCast are utilized. Demos are also available through Replicate and Hugging Face Spaces.
The paper's context within computer science, specifically AI, positions it as a significant contribution to the field. arXivLabs, an experimental framework, supports community-driven features, aligning with values of openness and data privacy, which facilitate such research.
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