[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83670-en":3,"doc-seo-83670-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83670,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Knowledge-Centric Information Systems","For decades, data engineering has established principles for integrating, governing, validating, cataloging, and serving organizational data. Large language models do not remove these concerns; they generalize them as organizations shift from information repositories to executable infrastructure. The paper argues for an enterprise AI architectural discipline—knowledge architecture—for representing, maintaining, governing, and operationally delivering organizational knowledge, redefining ETL/CDC/lineage/catalogs into knowledge-oriented equivalents and extending patterns toward LLM Wiki and Open Knowledge Format.","arXiv :2607 .02609v 1 [ cs . SE] 1 Jul 2026  \nKnowledge-Centric Information Systems  \nGeneralizing Data Engineering Principles for Executable Organizational Knowledge  \n Mariano Garralda-Barrio∗  \nIndependent Researcher  \nLleida, Spain  \n[mariano.garralda.r@gmail.com](mariano.garralda.r@gmail.com)  \nJuly 7, 2026  \nAbstract  \nFor decades, data engineering has developed mature architectural principles for integrating, governing, validating, cataloging, and serving organizational data. The rise of large language models does not eliminate these concerns; it exposes a broader version of them. Organizational knowledge is becoming executable infrastructure: systems increasingly retrieve it, assemble it, reason over it, and act on it. This paper argues that enterprise artificial intelligence (AI) systems suggest a transition toward an architectural discipline for representing, maintaining, governing, and operationally delivering organizational knowledge. We refer to this discipline as knowledge architecture. We offer a conceptual model and taxonomy showing how classical data-engineering guarantees must be redefined when the managed unit shifts from records to knowledge artifacts: extract, transform, and load (ETL) becomes knowledge ingestion, changedata capture (CDC) becomes knowledge change detection, lineage becomes provenance, catalogs become knowledge catalogs, materialized views become knowledge views, and medallion architectures become raw–curated–operational knowledge layers. Emerging formats such as large language model (LLM) Wiki and the Open Knowledge Format (OKF) are treated as early evidence of this transition, not as its endpoint. The central claim is that knowledge architecture becomes useful when organizational knowledge ceases to be a passive information resource and becomes an operational asset used by humans, agents, workflows, and models to execute work.  \nKeywords knowledge architecture · data architecture · data engineering · large language models · knowledge engineering · interoperability · governance · provenance · Open Knowledge Format  \n1 Introduction  \nModern enterprises have spent the last several decades learning how to manage data as an architectural asset. The resulting discipline includes ingestion pipelines, warehouses, lakes, lakehouses, change-data capture, metadata catalogs, governance processes, lineage systems, quality controls, event streams, and consumption interfaces [1–4] . These patterns emerged because organizations had too much data, in too many systems, with too many downstream consumers.  \nA similar transition is now occurring for organizational knowledge. Large language models (LLMs), retrievalaugmented generation (RAG), semantic search, agentic workflows, and tool-calling systems make it possible to consume information that previously remained outside classical data pipelines [5–7] . Enterprise knowledge is no longer limited to records in databases. It includes manuals, design documents, images, slide decks, spreadsheets, tickets, contracts, source code, application programming interface (API) specifications, metrics, chat histories, decisions, runbooks, playbooks, and tacit assumptions embedded in workflows.  \n∗ Independent Researcher / Investigador Independiente.  \nThe architectural problem is changing. The question is no longer only how to process data, but how to represent, maintain, govern, synchronize, validate, and serve heterogeneous knowledge so that humans, agents, workflows, and models can use it reliably. We use architecture in the established sense of organizing system structures, stakeholder concerns, viewpoints, and design decisions for complex software-intensive and information systems [8–11] . We call the resulting discipline knowledge architecture: an architectural response to the shift from knowledge as a passive resource that humans consult to knowledge as executable infrastructure that systems use to decide, assemble context, call tools, and perform work.  \nDefinitio","cbCaiuVUckkPugs5","https://ap.wps.com/l/cbCaiuVUckkPugs5","pdf",464552,3,1,10,"English","en",105,"# Introduction\n## Executable organizational knowledge\n## Knowledge architecture\n## From data-centric to knowledge-centric systems","[{\"question\":\"What problem does knowledge architecture address?\",\"answer\":\"It addresses how to represent, maintain, govern, synchronize, validate, and serve heterogeneous organizational knowledge so humans, agents, workflows, and models can use it reliably.\"},{\"question\":\"How does the paper define “executable organizational knowledge”?\",\"answer\":\"Executable organizational knowledge is organizational knowledge directly consumable by humans, models, agents, workflows, or applications to influence or perform operational behavior, extending beyond the narrow notion of program code.\"},{\"question\":\"How do classical data-engineering concepts change under knowledge architecture?\",\"answer\":\"The managed unit shifts from records to knowledge artifacts, turning ETL into knowledge ingestion, CDC into knowledge change detection, lineage into provenance, catalogs into knowledge catalogs, and materialized views into knowledge 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problem does knowledge architecture address?","Question",{"text":75,"@type":76},"It addresses how to represent, maintain, govern, synchronize, validate, and serve heterogeneous organizational knowledge so humans, agents, workflows, and models can use it reliably.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper define “executable organizational knowledge”?",{"text":80,"@type":76},"Executable organizational knowledge is organizational knowledge directly consumable by humans, models, agents, workflows, or applications to influence or perform operational behavior, extending beyond the narrow notion of program code.",{"name":82,"@type":73,"acceptedAnswer":83},"How do classical data-engineering concepts change under knowledge architecture?",{"text":84,"@type":76},"The managed unit shifts from records to knowledge artifacts, turning ETL into knowledge ingestion, CDC into knowledge change detection, lineage into provenance, catalogs into knowledge catalogs, and 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