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.
A novel framework for combining symbolic and sub-symbolic knowledge is proposed based on the notion of Hybrid Knowledge Base (HKB), a formalism that integrates ontologies with Machine Learning (ML) classifiers.
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.
This paper looks at the results of testing deontic axioms and systems with example problems using Kripke semantics and possible worlds over accessibility relations in the context of a large ontology, and considers the notion of a sanction in a Contrary To Duty scenario.
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