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BLINKG is a benchmark designed to evaluate the mapping capabilities of Large Language Models (LLMs) in constructing Knowledge Graphs from heterogeneous data sources. It provides a standardized framework to assess how effectively LLMs establish correspondences between data schemas and ontology concepts.
This extended paper revisits Semantic Web Services insights for Knowledge Graphs, proposing a four-dimensional formal framework and an Agentic Affordance Profile (AAP) to enable principled KG selection, composition, and failure diagnosis at agent planning time.
arXiv reports on its ongoing HTML Papers project, highlighting improved conversion fidelity, corpus-scale HTML conversion reaching 75% error-free rate, initial MathML 4 Intent annotations for accessible speech, and a Rust port of LaTeXML to reduce costs.
This thesis proposes a modular framework for scalable uncertainty reasoning in knowledge graphs, addressing imprecise attribute values, probabilistic triple existence, and incomplete schema through tailored algebraic, logical, and geometric techniques.