What Foundation Capital calls a context graph is, in the disciplinary vocabulary of information science, a procedural knowledge graph grounded in formal ontologies, with provenance, temporal validity, entity resolution and controlled vocabularies underneath. We already have the methods. PROV-O models provenance. SKOS handles vocabulary control. OWL provides the reasoning structures. The toolkit predates the marketing
Outsourcing and Process Knowledge Loss
What Foundation Capital calls a context graph is, in the disciplinary vocabulary of information science, a procedural knowledge graph grounded in formal ontologies, with provenance, temporal validity, entity resolution and controlled vocabularies underneath. We already have the methods. PROV-O models provenance. SKOS handles vocabulary control. OWL provides the reasoning structures. The toolkit predates the marketing. What is missing is not tooling but the recognition that this is a knowledge management problem, requiring knowledge engineers, information architects, ontologists and the cultural conditions that allow their work to be valued and sustained as part of the operational infrastructure. The Foundation Capital thesis is therefore best read not as a revelation but as a confession. The market is admitting, in the language it understands, that the operational knowledge required to run modern enterprises has gone missing. The 'context' being chased is the work that was sent away and outsourced in favor of products, features, solutions and marketing magic. The graph being built is an attempt to reconstruct, from telemetry and decision traces, what should have been captured natively through documentation, apprenticeship and disciplined knowledge engineering - and would have been, in an engineering state that had not transformed itself into a lawyerly society. That the industry needed a venture capital essay to name this problem tells us how far the disciplinary erosion has gone. There is a darker chapter in this story that must be told and merits a retrospective, in order to understand the domain of process and procedural knowledge. Why every organization is scrambling to wrap their arms around process knowledge, now known as context graphs, with the objective of recording, documenting and eliciting meaning from decision traces and execution traces. For the past four decades, American and Western companies have systematically outsourced not just manufacturing work, but the entire sociotechnical ecosystem that generates, maintains and transmits process knowledge. What began as a rational economic decision to reduce costs became a wholesale abandonment of the cultural and institutional practices that make process knowledge legible, valuable and actionable. We outsourced what we dismissively called 'the boring stuff' - the manufacturing, the execution, the grunt work - without understanding that we were outsourcing the very capacity to understand how things get built. This essay examines this history of outsourcing through the lens of process knowledge management. It argues that what was lost was not simply jobs or manufacturing capacity, but something more fundamental: the sociotechnical ethos that treated documentation, apprenticeship and the systematic capture of procedural knowledge as integral to the work of building things, not as an afterthought or administrative burden.
Organization associated with Arvind Jain and the context data platform reference.
Glean
Organization associated with the Data and AI Chief podcast reference.
ThoughtSpot
Jessica Talisman
Continuation link from the LinkedIn article to the longer Substack version.
The Context Gap on Intentional Arrangement
LinkedIn publishing embed for Jessica Talisman's Intentional Arrangement Substack reference.
Intentional Arrangement Substack embed
Intentional Arrangement | Jessica Talisman, MLS | Substack
The Context Gap RDF/HTML/Markdown/JSON-LD bundle
A critique of the enterprise AI context graph thesis as a rediscovery of process knowledge, procedural knowledge, institutional memory, and knowledge management disciplines weakened by outsourcing.
A New Name for an Old Wound In December 2025, Foundation Capital published an essay titled 'AI's Trillion-Dollar Opportunity: Context Graphs,' which has since become one of the most-discussed pieces in enterprise AI. The thesis holds that agents are hitting a wall that governance alone cannot solve, and the wall is not missing data - it is missing decision traces. Foundation Capital proposes the 'context graph' as the answer, characterized as a living record of decision traces stitched across entities and time, where precedent becomes searchable. Within a month, Dharmesh Shah of HubSpot was calling context graphs 'a system of record for decisions, not just data,' Aaron Levie of Box declared we had entered 'the era of context,' and Arvind Jain of Glean wrote that the concept 'finally has a name.' In other words, a rebranding of a discipline lost. Allow me a moment of professional indignation. The thing being so deftly branded is process knowledge, procedural knowledge and institutional memory, core tenants of library and information science and the knowledge management. And the organization of information and knowledge is exactly what American and Western companies systematically dismantled over decades of outsourcing. 2026 is the year venture capital rebranded the absence of knowledge management as a trillion-dollar opportunity. I want to be careful here as the Foundation Capital authors are not wrong about the problem. They have correctly diagnosed that enterprise AI agents fail in the gap between what happened and why it was allowed to happen - the gap between systems of record and the reasoning that connects inputs to outputs. They are also correct that 'the context that justified [a decision] isn't preserved,' that 'you can't replay the state of the world at decision time,' and that without that capacity, agents inherit their parent systems' blind spots. This is true. It is also exactly what knowledge engineers, ontologists and information architects have been saying for more than forty years. There is a deeper irony here. Foundation Capital observes that 'capturing decision traces requires being in the execution path at commit time, not bolting on governance after the fact.' Quite right. But who removed themselves from the execution path? Western companies did, deliberately, when they outsourced the execution. The 'decision traces' the industry is now scrambling to capture accounts for the traces that were never captured in the first place - because the work that would have generated them was sent to Shenzhen, Bangalore and Manila, and the sociotechnical ethos that would have documented them was dismantled along with the apprenticeship systems and communities of practice that sustained it. Context graphs are not a new invention. They are a market response to a self-inflicted wound. The thing being sold as the next platform shift is the recovery of process knowledge that was treated as boring stuff, sent away, and lost. Foundation Capital itself acknowledges the analog when they compare the opportunity to process mining, where companies like Celonis built businesses out of helping enterprises see workflows they had ceased to understand. The pattern is the same. We outsourced the work and lost the procedural knowledge. This is the convenience trap at organizational scale. Decisions to outsource seems locally rational - lower cost, cleaner balance sheets, focused 'core competencies.' The aggregate consequence emerges as the erosion of the knowledge infrastructures necessary to operate the systems we nominally own. Now we are told that purchasing context graph software from a verticalized startup will close the gap. Perhaps this is true. But software cannot reconstitute communities of practice or regenerate apprenticeship lineages. Software cannot retroactively document the forty years of operational decisions. What Foundation Capital calls a context graph is, in the disciplinary vocabulary of information science, a procedural knowledge graph grounded in formal ontologies, with provenance, temporal validity, entity resolution and controlled vocabularies underneath. We already have the methods. PROV-O models provenance. SKOS handles vocabulary control. OWL provides the reasoning structures. The toolkit predates the marketing. What is missing is not tooling but the recognition that this is a knowledge management problem, requiring knowledge engineers, information architects, ontologists and the cultural conditions that allow their work to be valued and sustained as part of the operational infrastructure. The Foundation Capital thesis is therefore best read not as a revelation but as a confession. The market is admitting, in the language it understands, that the operational knowledge required to run modern enterprises has gone missing. The 'context' being chased is the work that was sent away and outsourced in favor of products, features, solutions and marketing magic. The graph being built is an attempt to reconstruct, from telemetry and decision traces, what should have been captured natively through documentation, apprenticeship and disciplined knowledge engineering - and would have been, in an engineering state that had not transformed itself into a lawyerly society. That the industry needed a venture capital essay to name this problem tells us how far the disciplinary erosion has gone. There is a darker chapter in this story that must be told and merits a retrospective, in order to understand the domain of process and procedural knowledge. Why every organization is scrambling to wrap their arms around process knowledge, now known as context graphs, with the objective of recording, documenting and eliciting meaning from decision traces and execution traces. For the past four decades, American and Western companies have systematically outsourced not just manufacturing work, but the entire sociotechnical ecosystem that generates, maintains and transmits process knowledge. What began as a rational economic decision to reduce costs became a wholesale abandonment of the cultural and institutional practices that make process knowledge legible, valuable and actionable. We outsourced what we dismissively called 'the boring stuff' - the manufacturing, the execution, the grunt work - without understanding that we were outsourcing the very capacity to understand how things get built. This essay examines this history of outsourcing through the lens of process knowledge management. It argues that what was lost was not simply jobs or manufacturing capacity, but something more fundamental: the sociotechnical ethos that treated documentation, apprenticeship and the systematic capture of procedural knowledge as integral to the work of building things, not as an afterthought or administrative burden. When we sent manufacturing to China, India and the Philippines, we divested from the opportunity to learn, iterate, fail and improve. We eliminated critical feedback loops, a requisite for capturing and documenting procedural knowledge. We dissolved communities of practice that essential sources for process knowledge. And crucially, thanks to gapping holes in the end-to-end process knowledge fabric, we stopped investing in the knowledge infrastructure required to capture and maintain our understanding of how complex systems actually work. The Great Unbundling The story begins in the 1880s and accelerates through the 1990s and 2000s with what business strategists celebrated this as 'disaggregation' and 'core competency focus.' Companies would concentrate on their 'core' activities - typically defined as customer-facing brand management, product design and strategic decision making - while outsourcing everything else. Manufacturing was among the first to go, particularly in electronics, textiles and eventually more sophisticated dry and wet goods. But the outsourcing movement didn't stop at physical manufacturing. By the early 2000s, a new category emerged: Knowledge Process Outsourcing (KPO). Unlike traditional Business Process Outsourcing (BPO), which focused on routine transactional work like call centers and data entry, KPO involved outsourcing knowledge-intensive activities that required specialized expertise and analytical skills1. Legal research, financial analysis, market research, engineering design, pharmaceutical R&D, the very activities that generated and required deep process knowledge, were increasingly sent offshore to providers in India, the Philippines and China. The logic made sense. Why maintain expensive in-house capabilities when you could access global talent pools at a fraction of the cost? Why invest in training and developing institutional memory when specialized KPO firms could provide on-demand expertise? The KPO industry exploded. By 2006, India's KPO sector alone was estimated at $1.5 billion, growing to over $12 billion by 2015.2 The Philippines positioned itself as a hub for 'non-voice' back office services. China became the world's factory, but increasingly also its laboratory for manufacturing process innovation. What went largely unexamined was what happened to process knowledge when these activities migrated. The assumption was that process knowledge could be cleanly separated from execution - that 'knowing how' could remain in Western headquarters while 'doing what' happened elsewhere. This assumption proved catastrophically wrong. Look no further than current struggles in developing knowledge infrastructures in technology organizations and massive failures of agentic AI systems. (see my series, 'Why AI Isn't Autonomous (Yet)'). Shenzhen and Process Knowledge To understand what was lost, we must first understand what was gained. Dan Wang's Breakneck provides the most compelling account of how China, and Shenzhen in particular, transformed manufacturing offshoring into a comprehensive accumulation of process knowledge.3 I adore Wang's book so much, I have read it twice, and highly recommend...///continue reading on my Substack, Intentional Arrangement
The Context Gap
Foundation Capital context graphs thesis
Foundation Capital
Foundation Capital essay that names context graphs as an enterprise AI opportunity and supplies the primary thesis critiqued by the article.
AI's Trillion-Dollar Opportunity: Context Graphs
Newsletter featuring curated, original content about information architecture, semantics, and knowledge infrastructure.
The Knowledge Engineer
Co-host associated with the It is About Data media reference.
Matt Housley
The Context Gap JSON-LD companion
Location referenced as part of offshored execution and knowledge work.
Bangalore
The article argues that context graph is a market-friendly name for process knowledge, procedural knowledge, institutional memory, and formal knowledge-management practices that already existed.
China
Referenced as an analog from process mining.
Celonis
Commenter focused on decision-making systems, safety, and wisdom after the fact.
Fred Lardaro
Jessica Talisman comment
thank you, Andi
A New Name for an Old Wound In December 2025, Foundation Capital published an essay titled 'AI's Trillion-Dollar Opportunity: Context Graphs,' which has since become one of the most-discussed pieces in enterprise AI. The thesis holds that agents are hitting a wall that governance alone cannot solve, and the wall is not missing data - it is missing decision traces. Foundation Capital proposes the 'context graph' as th
A New Name for an Old Wound
A New Name for an Old Wound In December 2025, Foundation Capital published an essay titled 'AI's Trillion-Dollar Opportunity: Context Graphs,' which has since become one of the most-discussed pieces in enterprise AI. The thesis holds that agents are hitting a wall that governance alone cannot solve, and the wall is not missing data - it is missing decision traces. Foundation Capital proposes the 'context graph' as the answer, characterized as a living record of decision traces stitched across entities and time, where precedent becomes searchable. Within a month, Dharmesh Shah of HubSpot was calling context graphs 'a system of record for decisions, not just data,' Aaron Levie of Box declared we had entered 'the era of context,' and Arvind Jain of Glean wrote that the concept 'finally has a name.' In other words, a rebranding of a discipline lost. Allow me a moment of professional indignation. The thing being so deftly branded is process knowledge, procedural knowledge and institutional memory, core tenants of library and information science and the knowledge management. And the organization of information and knowledge is exactly what American and Western companies systematically dismantled over decades of outsourcing. 2026 is the year venture capital rebranded the absence of knowledge management as a trillion-dollar opportunity. I want to be careful here as the Foundation Capital authors are not wrong about the problem. They have correctly diagnosed that enterprise AI agents fail in the gap between what happened and why it was allowed to happen - the gap between systems of record and the reasoning that connects inputs to outputs. They are also correct that 'the context that justified [a decision] isn't preserved,' that 'you can't replay the state of the world at decision time,' and that without that capacity, agents inherit their parent systems' blind spots. This is true. It is also exactly what knowledge engineers, ontologists and information architects have been saying for more than forty years. There is a deeper irony here. Foundation Capital observes that 'capturing decision traces requires being in the execution path at commit time, not bolting on governance after the fact.' Quite right. But who removed themselves from the execution path? Western companies did, deliberately, when they outsourced the execution. The 'decision traces' the industry is now scrambling to capture accounts for the traces that were never captured in the first place - because the work that would have generated them was sent to Shenzhen, Bangalore and Manila, and the sociotechnical ethos that would have documented them was dismantled along with the apprenticeship systems and communities of practice that sustained it. Context graphs are not a new invention. They are a market response to a self-inflicted wound. The thing being sold as the next platform shift is the recovery of process knowledge that was treated as boring stuff, sent away, and lost. Foundation Capital itself acknowledges the analog when they compare the opportunity to process mining, where companies like Celonis built businesses out of helping enterprises see workflows they had ceased to understand. The pattern is the same. We outsourced the work and lost the procedural knowledge. This is the convenience trap at organizational scale. Decisions to outsource seems locally rational - lower cost, cleaner balance sheets, focused 'core competencies.' The aggregate consequence emerges as the erosion of the knowledge infrastructures necessary to operate the systems we nominally own. Now we are told that purchasing context graph software from a verticalized startup will close the gap. Perhaps this is true. But software cannot reconstitute communities of practice or regenerate apprenticeship lineages. Software cannot retroactively document the forty years of operational decisions.
A living record of decision traces stitched across entities and time, making precedent searchable.
Context graph
HubSpot founder referenced for describing context graphs as systems of record for decisions.
Dharmesh Shah
Event location shown in source media references.
San Francisco, California
Method for analyzing event logs to reveal operational workflows; used as an analogy for context graphs.
Process mining
Organization associated with Dharmesh Shah in the article.
HubSpot
Foundation Capital context graphs one month in
Cover image from LinkedIn Open Graph metadata.
LinkedIn article cover image
Why are communities of practice central?
Substack source linked through footnotes for KPO, market estimates, and the process-knowledge-management continuation.
Process Knowledge Management
Model provenance and temporal validity
Use provenance and time-aware modeling so later agents can replay the state of the world at decision time.
The thread asks whether companies can rebuild the communities of practice behind the graph or will keep trying to buy the artifact without restoring the discipline, while also showing how practitioners can publish and inspect graph artifacts directly.
W3C
W3C SKOS recommendation named in the article for vocabulary control.
SKOS Simple Knowledge Organization System Reference
Host associated with the ThoughtSpot podcast media reference.
Cindy Howson
When we sent manufacturing to China, India and the Philippines, we divested from the opportunity to learn, iterate, fail and improve. We eliminated critical feedback loops, a requisite for capturing and documenting procedural knowledge. We dissolved communities of practice that essential sources for process knowledge. And crucially, thanks to gapping holes in the end-to-end process knowledge fabric, we stopped invest
The Great Unbundling
When we sent manufacturing to China, India and the Philippines, we divested from the opportunity to learn, iterate, fail and improve. We eliminated critical feedback loops, a requisite for capturing and documenting procedural knowledge. We dissolved communities of practice that essential sources for process knowledge. And crucially, thanks to gapping holes in the end-to-end process knowledge fabric, we stopped investing in the knowledge infrastructure required to capture and maintain our understanding of how complex systems actually work. The Great Unbundling The story begins in the 1880s and accelerates through the 1990s and 2000s with what business strategists celebrated this as 'disaggregation' and 'core competency focus.' Companies would concentrate on their 'core' activities - typically defined as customer-facing brand management, product design and strategic decision making - while outsourcing everything else. Manufacturing was among the first to go, particularly in electronics, textiles and eventually more sophisticated dry and wet goods. But the outsourcing movement didn't stop at physical manufacturing. By the early 2000s, a new category emerged: Knowledge Process Outsourcing (KPO). Unlike traditional Business Process Outsourcing (BPO), which focused on routine transactional work like call centers and data entry, KPO involved outsourcing knowledge-intensive activities that required specialized expertise and analytical skills1. Legal research, financial analysis, market research, engineering design, pharmaceutical R&D, the very activities that generated and required deep process knowledge, were increasingly sent offshore to providers in India, the Philippines and China. The logic made sense. Why maintain expensive in-house capabilities when you could access global talent pools at a fraction of the cost? Why invest in training and developing institutional memory when specialized KPO firms could provide on-demand expertise? The KPO industry exploded. By 2006, India's KPO sector alone was estimated at $1.5 billion, growing to over $12 billion by 2015.2 The Philippines positioned itself as a hub for 'non-voice' back office services. China became the world's factory, but increasingly also its laboratory for manufacturing process innovation.
Business Process Outsourcing
Organizational knowledge accumulated through documentation, apprenticeship, and communities of practice.
Institutional memory
W3C provenance ontology for modeling provenance information.
PROV-O
Web Ontology Language used for formal reasoning structures.
OWL
What does the comment thread add about culture?
Principles for publishing structured data on the Web using HTTP IRIs and typed links between resources.
Linked Data
Organization associated with the Context Graphs and Process Knowledge event reference.
Neo4j
How is process mining used as an analogy?
A practical sequence implied by the article and comments for restoring process knowledge behind context graphs.
How to close the context gap
HTTP extension used here as a mounted file folder workflow for publishing generated RDF and HTML artifacts.
WebDAV
Box leader referenced for the phrase era of context.
Aaron Levie
Wikipedia contributors
Wikipedia reference linked directly from the article for Business Process Outsourcing.
Organization associated with Kingsley Idehen and the Virtuoso/URIBurner Linked Data publication stack.
OpenLink Software
Outsourcing of specialized knowledge-intensive work such as legal research, analytics, engineering design, and research.
Knowledge Process Outsourcing
Dan Wang
Book by Dan Wang cited for the account of China, Shenzhen, and the engineering-state frame behind process-knowledge accumulation.
Breakneck: China's Quest to Engineer the Future
Discipline concerned with capturing, organizing, maintaining, and applying organizational knowledge.
Knowledge management
A procedural knowledge graph is an ontology-grounded graph that captures process knowledge with provenance, temporal validity, entity resolution, controlled vocabularies, and decision traces.
Practice of supplying agents and systems with evidence-rich context.
Context engineering
A graph grounded in formal ontologies, provenance, temporal validity, entity resolution, and controlled vocabularies.
Procedural knowledge graph
Questions and answers generated from the article and visible comment thread.
FAQ: The Context Gap
Outsourcing is presented as a long-term driver of process-knowledge loss because organizations separated execution from the environments where knowledge was generated, documented, and transmitted.
Organization mentioned by Andrei Bacu in the comments.
Antifragile.AI
Why AI Is not Autonomous Yet
The comments connect the thesis to government decision making, wisdom after the fact, EU AI Act compliance, context engineering, observability, Linked Data publication workflows, and the need to rebuild communities of practice.
What role did outsourcing play in the argument?
JavaScript visualization library used by the KG Explorer.
D3.js
Commenter; Partner, Data & AI Engineering Lead; ontologist.
Charles Ivie
Organization associated with Aaron Levie in the article.
Box
Rebuild communities of practice
Support the human practices that make process knowledge trustworthy, maintained, and operationally useful.
Regulatory context mentioned in the comments as a target for knowledge graph and ontology work.
EU AI Act
Runtime used by the collection generator.
Node.js
Author of Goliath's Curse, referenced in Jessica Talisman reply.
Luke Kemp
15h
Shashi Bhatnagar comment
This is brilliant articulation of risks we face - and what to do to influence the mitigation. A very multidimensional view can be formed from the same article.
Skill used to generate RDF-backed HTML and Markdown companions.
rdf-infographic-skill
Commenter framing the article as rediscovery and repackaging of lost knowledge practices.
Andi Willmott
Books, essays, source notes, media references, standards, and public identity links used by or named in the article and visible comment thread.
Reference corpus for The Context Gap
Outsourcing of routine transactional processes such as call centers and data entry.
Process mining is used as an analog because companies such as Celonis built businesses by helping enterprises see workflows they had ceased to understand.
W3C model for controlled vocabularies, concept schemes, and thesauri.
SKOS
United States
Book recommended by Jessica Talisman in the comment thread, relevant to slow institutional decline and collapse dynamics.
Goliath's Curse: The History and Future of Societal Collapse
Web of linked RDF data and dereferenceable identifiers used to publish knowledge graphs for machine and human access.
Semantic Web
AI systems whose failures are framed as symptoms of weak process knowledge and missing decision traces.
Agentic AI systems
Expanded visible comments captured from the LinkedIn article reader aside content.
Visible LinkedIn comment thread
Transfer of work outside an organization, treated in the article as a driver of lost process knowledge.
Outsourcing
What is missing if companies only buy context graph software?
Manufacturing and process-knowledge accumulation example in the article.
Shenzhen
Charles Ivie comment
Very astute observations Jessica Talisman. It reminds me of so many decisions that are made by every government. Quick decisions are made with unknown consequences yet to be realised. Often resulting in the cure being worse than the original pathogen. We are, as a species, so destined to find a quick fix, only to realise it cases a slow decline.
Which semantic-web standards does the article mention?
Published the context graphs market thesis that the article critiques.
Link comments and institutional feedback
Treat expert discussion as knowledge-bearing evidence and connect comments to the claims, questions, and concepts they refine.
India
Philippines
Social structure through which practitioners transmit process knowledge and improve work.
Community of practice
Operational know-how about how work is performed, improved, and transmitted.
Process knowledge
Commenter raising the cultural side of rebuilding communities of practice.
Andreas Schurch
Andi Willmott comment
What is lost is found again - just repackaged. Jessica Talisman, really enjoyed your observations linking the past days to today - hugely astute.
Commenter; Founder and CEO at OpenLink Software, discussing publication of the generated knowledge graph as Linked Data.
Kingsley Uyi Idehen
Glean leader referenced for saying the concept finally has a name.
Arvind Jain
LinkedIn publishing embed for the Neo4J event referenced in the source article.
Neo4J Event. Context Graphs and Process Knowledge
Context Graph and Process Knowledge, Jessica Talisman, Contextually LLC
Wikipedia reference linked directly from the article for Knowledge Process Outsourcing.
Why does the article critique the phrase context graph?
Glean context data platform
Andreas Schurch comment
Jessica, love this! The phrase "context graph" is useful, but your point that the market is rediscovering process knowledge, ontology, and institutional memory is spot on. Curious how others here are thinking about the cultural side of this: can companies rebuild the communities of practice behind the graph, or will they keep trying to buy the artifact without restoring the discipline? We have seen this movie before.
55m
Fred knowledge and wisdom!
Skill used to generate the RDF knowledge graph.
kg-generator skill
They are the social infrastructure through which operational knowledge, craft judgement, documentation habits, and apprenticeship practices are sustained.
The gap between recorded facts and the reasoning, permissions, and conditions that made decisions possible.
Context gap
Kingsley Uyi Idehen comment
Jessica, here is a knowledge graph, deployed as a Semantic Web, constructed from notes generated by my AI agent from this post. Publication for Web access and upload to the underlying Virtuoso-hosted knowledge graph is achieved by mounting a Virtuoso WebDAV folder to the local operating system, copying local files into the mounted folder, and letting Linked Data principles provide Web-scale data access by reference. Links include https://linkeddata.uriburner.com/DAV/demos/daas/context-gap-jessica-talisman-gpt5-1.html and hashtags #AgenticWeb #ContextGraph #KnowledgeGraphs #LinkedData #SemanticWeb #HowTo #UseCase.
Organization associated with Andreas Schurch in the comment metadata.
Duodata
LinkedIn publishing embed titled The Context Gap, present in the source article before the podcasts section.
Article continuation embed
How do comments extend the article?
The Context Gap HTML companion
Follow-up Foundation Capital essay referenced for Dharmesh Shah and the system-of-record-for-decisions framing.
Context Graphs, One Month In
Manila
W3C provenance ontology named in the article as a method for modeling provenance.
PROV-O: The PROV Ontology
Tony Baer
Generation environment used to create and validate this RDF-backed bundle.
Codex desktop workspace
The Context Gap RDF Turtle companion
Charles outsource or automate without much in between. Currently reading Goliath's Curse by Luke Kemp which I think you'd appreciate
58m
Fred Lardaro comment
Jessica Talisman!!! Everything old is new again. If all our experts disappeared... would our decision making systems be able to explain how decisions were made? One consolation is that we are pretty good at gaining wisdom after the fact.
Evidence of why a decision was allowed to happen at a particular time.
Decision trace
Author of The Context Gap; information systems, taxonomy, ontology, and knowledge graph practitioner.
The context gap is the difference between knowing what happened and knowing why a decision was justified, permitted, and executed in a given operational moment.
Capture decision traces at execution time
Record the decision context, inputs, actors, approvals, constraints, and alternatives while work is performed.
RDF source of truth for an HTML and Markdown collection derived from the article and visible comments.
The Context Gap knowledge graph
Ontology and artifact graph for Jessica Talisman's LinkedIn article and visible comment thread.
Comment-thread framing that the article can inform mitigation of multidimensional organizational and AI risks.
Risk mitigation
LinkedIn publishing embed for the ThoughtSpot podcast with Cindy Howson referenced in the source article.
The Data and AI Chief. How Semantic Layers and Ontologies Create Trusted AI
How Semantic Layers and Ontologies Create Trusted AI
Normalize entities and vocabularies
Use entity resolution and controlled vocabularies so people, systems, decisions, processes, and evidence can be joined reliably.
Defined terms extracted from the article and visible comment thread.
Glossary: context gap and process knowledge
What went largely unexamined was what happened to process knowledge when these activities migrated. The assumption was that process knowledge could be cleanly separated from execution - that 'knowing how' could remain in Western headquarters while 'doing what' happened elsewhere. This assumption proved catastrophically wrong. Look no further than current struggles in developing knowledge infrastructures in technology
Shenzhen and Process Knowledge
What went largely unexamined was what happened to process knowledge when these activities migrated. The assumption was that process knowledge could be cleanly separated from execution - that 'knowing how' could remain in Western headquarters while 'doing what' happened elsewhere. This assumption proved catastrophically wrong. Look no further than current struggles in developing knowledge infrastructures in technology organizations and massive failures of agentic AI systems. (see my series, 'Why AI Isn't Autonomous (Yet)'). Shenzhen and Process Knowledge To understand what was lost, we must first understand what was gained. Dan Wang's Breakneck provides the most compelling account of how China, and Shenzhen in particular, transformed manufacturing offshoring into a comprehensive accumulation of process knowledge.3 I adore Wang's book so much, I have read it twice, and highly recommend...///continue reading on my Substack, Intentional Arrangement
Related Substack series linked in the article when discussing knowledge infrastructure failures and agentic AI systems.
Why AI Isn't Autonomous Yet
Publisher and media organization associated with the Data Superstream reference.
O Reilly Media
LinkedIn publishing embed for the It is About Data episode referenced in the source article.
It is About Data. Knowledge Graphs, Ontologies and Context
Knowledge Graphs, Ontologies and Context (Ep 56 with Jessica Talisman)
What is a procedural knowledge graph?
The Context Gap Markdown companion
The article mentions PROV-O for provenance, SKOS for vocabulary control, and OWL for reasoning structures.
Comment-thread idea that the same article can support many perspectives when represented as a graph.
Multidimensional view
Workflow where files copied to a mounted Virtuoso WebDAV folder become Web-accessible resources and graph inputs.
Virtuoso WebDAV publication
Software alone cannot restore documentation practices, apprenticeship lineages, communities of practice, or cultural respect for knowledge engineering work.
Commenter building knowledge graph and ontology work around EU AI Act compliance.
Andrei Bâcu
LinkedIn publishing embed for the O Reilly Data Superstream reference in the source article.
O Reilly Data Superstream. The Ontology Pipeline for AI Systems
Knowledge Infrastructures and the Ontology Pipeline for AI Systems with Jessica Talisman
What is the context gap?
Hybrid relational/RDF database and Linked Data server platform on which URIBurner runs.
OpenLink Virtuoso
Commenter connecting the article to risk mitigation and multidimensional views.
Shashi Bhatnagar
W3C OWL 2 overview named in the article for reasoning structures.
OWL 2 Web Ontology Language Document Overview
Author of Breakneck, cited for China, Shenzhen, and engineering-state process knowledge.
Linked Data resolver and SPARQL entry point used for entity hyperlinks.
URIBurner
Andrei Bâcu comment
Jessica Talisman I really appreciate that you are sharing all this valuable information in an usable format. Have started building a knowledge graph and an ontology for the EU AI Act at Antifragile.AI, for streamlining compliance & cybersecurity and helping systems to become antifragile in time. Context graphs and context engineering are core components for providing evidence-based assertions and remediations, advanced observability and even inputs for RL of Agents.
Glean article referenced for Arvind Jain and the claim that the context graph concept finally has a name.
Context Data Platform