@inproceedings{yuen-2026-http,
title = "{HTTP} for Meaning: Towards Linguistically Complete Knowledge Representations with Knowledge State Protocol",
author = "Yuen, Ian",
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-1.10/",
pages = "128--138",
ISBN = "979-8-89176-464-4",
abstract = "The choice of knowledge representation (KR) determines what a knowledge base (KB) can store and how that knowledge can be retrieved and reasoned over. Recent knowledge-graph (KG) retrieval-augmented generation (RAG) systems show that *explicit symbolic structure* can complement the implicit knowledge encoded in large language models (LLMs). However, KGs capture only part of a text{'}s linguistic meaning and often leave **conceptual** and **temporal** context implicit, leading to recurrent reasoning failures. Existing approaches address these gaps through task-specific extensions but do not provide a unified framework for coordinating conceptual, instance-level, and temporal structure. We envision the **Knowledge State Protocol** (KSP), which coordinates canonical KRs to make known dimensions of linguistic meaning explicit. By standardizing interoperability among KRs, KSP provides a common, extensible, and open foundation for *structured* agent memory and neuro-symbolic training across modalities and languages."
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<abstract>The choice of knowledge representation (KR) determines what a knowledge base (KB) can store and how that knowledge can be retrieved and reasoned over. Recent knowledge-graph (KG) retrieval-augmented generation (RAG) systems show that *explicit symbolic structure* can complement the implicit knowledge encoded in large language models (LLMs). However, KGs capture only part of a text’s linguistic meaning and often leave **conceptual** and **temporal** context implicit, leading to recurrent reasoning failures. Existing approaches address these gaps through task-specific extensions but do not provide a unified framework for coordinating conceptual, instance-level, and temporal structure. We envision the **Knowledge State Protocol** (KSP), which coordinates canonical KRs to make known dimensions of linguistic meaning explicit. By standardizing interoperability among KRs, KSP provides a common, extensible, and open foundation for *structured* agent memory and neuro-symbolic training across modalities and languages.</abstract>
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%0 Conference Proceedings
%T HTTP for Meaning: Towards Linguistically Complete Knowledge Representations with Knowledge State Protocol
%A Yuen, Ian
%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 yuen-2026-http
%X The choice of knowledge representation (KR) determines what a knowledge base (KB) can store and how that knowledge can be retrieved and reasoned over. Recent knowledge-graph (KG) retrieval-augmented generation (RAG) systems show that *explicit symbolic structure* can complement the implicit knowledge encoded in large language models (LLMs). However, KGs capture only part of a text’s linguistic meaning and often leave **conceptual** and **temporal** context implicit, leading to recurrent reasoning failures. Existing approaches address these gaps through task-specific extensions but do not provide a unified framework for coordinating conceptual, instance-level, and temporal structure. We envision the **Knowledge State Protocol** (KSP), which coordinates canonical KRs to make known dimensions of linguistic meaning explicit. By standardizing interoperability among KRs, KSP provides a common, extensible, and open foundation for *structured* agent memory and neuro-symbolic training across modalities and languages.
%U https://aclanthology.org/2026.akbc-1.10/
%P 128-138
Markdown (Informal)
[HTTP for Meaning: Towards Linguistically Complete Knowledge Representations with Knowledge State Protocol](https://aclanthology.org/2026.akbc-1.10/) (Yuen, AKBC 2026)
ACL