Edge Knowledge Bases for Sustainable and Explainable AI

Aparna S. Varde; Emil Njor; Francesco Daghero; Mahyar Tourchi Moghaddam

Edge Knowledge Bases for Sustainable and Explainable AI

Aparna S. Varde, Emil Njor, Francesco Daghero, Mahyar Tourchi Moghaddam

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Abstract

We propose a vision of EdgeKBs(Edge Knowledge Bases) integrating KBs into Edge AI systems. EdgeKBs can infuse commonsense and domain knowledge within Edge devices to guide them while learning from training data. By doing so, we not only bring explainability and performance benefits of KBs to the Edge, but also enable more efficient, sustainable Edge AI systems. We envisage three approaches to develop EdgeKBs: Slicing Dicing; Tailoring to Fit; and Dynamic EdgeKBs. These EdgeKBs will bring Knowledge-Guided Machine Learning (KGML) to the Edge to support next-generation applications, e.g., Physical AI, cobots, and local natural language interfaces.

Anthology ID:: 2026.akbc-1.3
Volume:: Proceedings of the 11th Workshop on Automated Knowledge Base Construction (AKBC 2026)
Month:: October
Year:: 2026
Address:: Budapest, Hungary
Editors:: Jan-Christoph Kalo, Ndapa Nakashole, Simon Razniewski, Fabian M. Suchanek, Andreas Vlachos, Andrew McCallum
Venues:: AKBC | WS
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 19–25
Language:
URL:: https://aclanthology.org/2026.akbc-1.3/
DOI:
Bibkey:
Cite (ACL):: Aparna S. Varde, Emil Njor, Francesco Daghero, and Mahyar Tourchi Moghaddam. 2026. Edge Knowledge Bases for Sustainable and Explainable AI. In Proceedings of the 11th Workshop on Automated Knowledge Base Construction (AKBC 2026), pages 19–25, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):: Edge Knowledge Bases for Sustainable and Explainable AI (Varde et al., AKBC 2026)
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PDF:: https://aclanthology.org/2026.akbc-1.3.pdf

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@inproceedings{varde-etal-2026-edge,
    title = "Edge Knowledge Bases for Sustainable and Explainable {AI}",
    author = "Varde, Aparna S.  and
      Njor, Emil  and
      Daghero, Francesco  and
      Moghaddam, Mahyar Tourchi",
    editor = "Kalo, Jan-Christoph  and
      Nakashole, Ndapa  and
      Razniewski, Simon  and
      Suchanek, Fabian M.  and
      Vlachos, Andreas  and
      McCallum, Andrew",
    booktitle = "Proceedings of the 11th Workshop on Automated Knowledge Base Construction ({AKBC} 2026)",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.akbc-1.3/",
    pages = "19--25",
    ISBN = "979-8-89176-464-4",
    abstract = "We propose a vision of EdgeKBs(Edge Knowledge Bases) integrating KBs into Edge AI systems. EdgeKBs can infuse commonsense and domain knowledge within Edge devices to guide them while learning from training data. By doing so, we not only bring explainability and performance benefits of KBs to the Edge, but also enable more efficient, sustainable Edge AI systems. We envisage three approaches to develop EdgeKBs: Slicing Dicing; Tailoring to Fit; and Dynamic EdgeKBs. These EdgeKBs will bring Knowledge-Guided Machine Learning (KGML) to the Edge to support next-generation applications, e.g., Physical AI, cobots, and local natural language interfaces."
}

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%0 Conference Proceedings
%T Edge Knowledge Bases for Sustainable and Explainable AI
%A Varde, Aparna S.
%A Njor, Emil
%A Daghero, Francesco
%A Moghaddam, Mahyar Tourchi
%Y Kalo, Jan-Christoph
%Y Nakashole, Ndapa
%Y Razniewski, Simon
%Y Suchanek, Fabian M.
%Y Vlachos, Andreas
%Y McCallum, Andrew
%S Proceedings of the 11th Workshop on Automated Knowledge Base Construction (AKBC 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-464-4
%F varde-etal-2026-edge
%X We propose a vision of EdgeKBs(Edge Knowledge Bases) integrating KBs into Edge AI systems. EdgeKBs can infuse commonsense and domain knowledge within Edge devices to guide them while learning from training data. By doing so, we not only bring explainability and performance benefits of KBs to the Edge, but also enable more efficient, sustainable Edge AI systems. We envisage three approaches to develop EdgeKBs: Slicing Dicing; Tailoring to Fit; and Dynamic EdgeKBs. These EdgeKBs will bring Knowledge-Guided Machine Learning (KGML) to the Edge to support next-generation applications, e.g., Physical AI, cobots, and local natural language interfaces.
%U https://aclanthology.org/2026.akbc-1.3/
%P 19-25

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Markdown (Informal)

[Edge Knowledge Bases for Sustainable and Explainable AI](https://aclanthology.org/2026.akbc-1.3/) (Varde et al., AKBC 2026)

Edge Knowledge Bases for Sustainable and Explainable AI (Varde et al., AKBC 2026)

ACL

Aparna S. Varde, Emil Njor, Francesco Daghero, and Mahyar Tourchi Moghaddam. 2026. Edge Knowledge Bases for Sustainable and Explainable AI. In Proceedings of the 11th Workshop on Automated Knowledge Base Construction (AKBC 2026), pages 19–25, Budapest, Hungary. Association for Computational Linguistics.