Bridging Human Cognition and AI: Enhancing Transparency and Explainability with Hierarchical Conceptual Graphs and the Knowing Protocol

Klas Ehnemark
Reliable and Trustworthy Artificial Intelligence 2025, PMLR 310:19-23, 2025.

Abstract

The rapid deployment of large language models (LLMs) in critical domains demands greater transparency and explainability to build user trust and enable effective collaboration. Cur- rent AI-human interactions largely rely on unstructured text, often resulting in misunder- standings and limited insight into AI reasoning. We introduce a Hierarchical Conceptual Graph Model and the Knowing Communication Protocol to bridge the gap between sym- bolic human reasoning and sub-symbolic AI processing. Our model combines conceptual spaces, ontologies, and hierarchical structures to explicitly represent complex knowledge, while the Knowing Protocol, through the Knowing Markup Language (KML), facilitates structured, machine-readable interactions. This approach enhances transparency by align- ing AI-generated content with human cognitive structures, promoting clarity and collabo- rative knowledge building—ultimately addressing the limitations of traditional text-based AI tools and advancing trustworthy, explainable AI.

Cite this Paper

BibTeX
@InProceedings{pmlr-v310-ehnemark25a, title = {Bridging Human Cognition and AI: Enhancing Transparency and Explainability with Hierarchical Conceptual Graphs and the Knowing Protocol}, author = {Ehnemark, Klas}, booktitle = {Reliable and Trustworthy Artificial Intelligence 2025}, pages = {19--23}, year = {2025}, editor = {Nguyen, Hoang D. and Le, Duc-Trong and Björklund, Johanna and Vu, Xuan-Son}, volume = {310}, series = {Proceedings of Machine Learning Research}, month = {12 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v310/main/assets/ehnemark25a/ehnemark25a.pdf}, url = {https://proceedings.mlr.press/v310/ehnemark25a.html}, abstract = {The rapid deployment of large language models (LLMs) in critical domains demands greater transparency and explainability to build user trust and enable effective collaboration. Cur- rent AI-human interactions largely rely on unstructured text, often resulting in misunder- standings and limited insight into AI reasoning. We introduce a Hierarchical Conceptual Graph Model and the Knowing Communication Protocol to bridge the gap between sym- bolic human reasoning and sub-symbolic AI processing. Our model combines conceptual spaces, ontologies, and hierarchical structures to explicitly represent complex knowledge, while the Knowing Protocol, through the Knowing Markup Language (KML), facilitates structured, machine-readable interactions. This approach enhances transparency by align- ing AI-generated content with human cognitive structures, promoting clarity and collabo- rative knowledge building—ultimately addressing the limitations of traditional text-based AI tools and advancing trustworthy, explainable AI.} }
Endnote
%0 Conference Paper %T Bridging Human Cognition and AI: Enhancing Transparency and Explainability with Hierarchical Conceptual Graphs and the Knowing Protocol %A Klas Ehnemark %B Reliable and Trustworthy Artificial Intelligence 2025 %C Proceedings of Machine Learning Research %D 2025 %E Hoang D. Nguyen %E Duc-Trong Le %E Johanna Björklund %E Xuan-Son Vu %F pmlr-v310-ehnemark25a %I PMLR %P 19--23 %U https://proceedings.mlr.press/v310/ehnemark25a.html %V 310 %X The rapid deployment of large language models (LLMs) in critical domains demands greater transparency and explainability to build user trust and enable effective collaboration. Cur- rent AI-human interactions largely rely on unstructured text, often resulting in misunder- standings and limited insight into AI reasoning. We introduce a Hierarchical Conceptual Graph Model and the Knowing Communication Protocol to bridge the gap between sym- bolic human reasoning and sub-symbolic AI processing. Our model combines conceptual spaces, ontologies, and hierarchical structures to explicitly represent complex knowledge, while the Knowing Protocol, through the Knowing Markup Language (KML), facilitates structured, machine-readable interactions. This approach enhances transparency by align- ing AI-generated content with human cognitive structures, promoting clarity and collabo- rative knowledge building—ultimately addressing the limitations of traditional text-based AI tools and advancing trustworthy, explainable AI.
APA
Ehnemark, K.. (2025). Bridging Human Cognition and AI: Enhancing Transparency and Explainability with Hierarchical Conceptual Graphs and the Knowing Protocol. Reliable and Trustworthy Artificial Intelligence 2025, in Proceedings of Machine Learning Research 310:19-23 Available from https://proceedings.mlr.press/v310/ehnemark25a.html.

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