TL;DR
This paper provides its computational complexity analysis for ontologies specified in (the Description Logic counterpart of) RDFS, and binary Multi-Layer Perceptrons, and binary Multi-Layer Perceptrons.
OaK is presented, an ontology-as-a-kernel framework that dynamically constructs and refines task-oriented ontologies for LLM agents and shows that OaK improves standard LLM agents, strengthens evidence grounding, and boosts the reliability of multi-step reasoning.
This work proposes a set of metrics designed to evaluate the ability of LRMs to generate formal knowledge in order to solve domain-specific problems, and curates – based on a collection of LRM-generated Prolog programs – common issues in generated KBs, and derive metrics that can be used to assess such issues.
This work proposes GPTKB 2.0, a methodology for constructing disambiguated KBs directly from large language models (LLMs) that incorporates on-the-fly disambiguation of entities, relations and classes, and is meticulously designed to satisfy both scalability and disambiguation accuracy.
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