Ontology for Meaning-Driven AI: Grounding, Interpretability, and Trust

Assessing Knowledge Provenance Quality for CEO Decision Support: An OWL-DL Ontological Framework

The ontology demonstrates approaches to enhancing the precision of knowledge provenance and outlines the systematic engineering of a knowledge provenance ontology designed to improve its reliability for executive decision-making. Utilizing architectural principles, we propose a formal model that evaluates knowledge pro...

AI semantics for biomedical data integration

Researchers increasingly need to explore hypotheses that span multimodal data across different scales, organisms, and domains. In practice, this requires connecting knowledge across fragmented databases with incompatible APIs and heterogeneous annotation practices. Large language model (LLM) agents can automate this da...

From Knowledge Retrieval to Execution: Designing Executable Knowledge Systems

The paper contributes to KM research by reframing KM as a system design challenge for AI-enabled execution and by positioning governance, validation and feedback control as central mechanisms for reliable organisational knowledge use.

Toward Effective and Reliable LLM Agents via Dynamic Ontology

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.

Motivations for Participating in Biomedical Ontology Communities within Human-AI Collaboration

Knowledge Retrieval Architectures for AI-Assisted Software Development: From Static Context to Autonomous Development Agents

The integration of Large Language Models (LLMs) into software development workflows has fundamentally transformed how developers interact with knowledge repositories, codebases, and documentation. However, traditional knowledge retrieval mechanisms face significant challenges when applied to the dynamic, multi-modal, a...