Research Thesis Introduces Modular Framework for Uncertainty in Knowledge Graphs

A research thesis published on arXiv.org on May 26, 2026, introduces a modular framework for handling uncertainty in knowledge graphs through probabilistic literals, SPARQL provenance compilation, and statistical schema reasoning. The work addresses three levels of uncertainty in real-world data modeling: imprecise attribute values, probabilistic triple existence, and incomplete schema knowledge.

Current Semantic Web standards lack native support for reasoning over such uncertainty, and existing extensions often lead to computational intractability, according to the study’s abstract. The framework introduces scalable methods to maintain semantic integrity while processing uncertain data, potentially influencing AI standards development in the U.S. tech sector.

Knowledge graphs—structured repositories of semantic data—serve as critical infrastructure for enterprise data integration and AI systems. The research, hosted on the U.S.-based preprint platform arXiv, offers technical solutions to improve reasoning capabilities in systems handling incomplete or probabilistic information.