How Library Science Diverged from Big Data

In this episode of Crazy Wisdom, host Stewart Alsop talks with Jessica Talisman, founder of Contextually and creator of the Ontology Pipeline, about the deep connections between knowledge management, library science, and the emerging world of AI systems. Together they explore how controlled vocabularies, ontologies, and metadata shape meaning for both humans and machines, why librarianship has lessons for modern tech, and how cultural context influences what we call “knowledge.” Jessica also discusses the rise of AI librarians, the problem of “AI slop,” and the need for collaborative, human-centered knowledge ecosystems. You can learn more about her work at Ontology Pipeline

 and find her writing and talks on LinkedIn.

Check out this GPT we trained on the conversation

Timestamps

00:00 Stewart Alsop welcomes Jessica Talisman to discuss Contextually, ontologies, and how controlled vocabularies ground scalable systems.
05:00 They compare philosophy’s ontology with information science, linking meaning, categorization, and sense-making for humans and machines.
10:00 Jessica explains why SQL and Postgres can’t capture knowledge complexity and how neuro-symbolic systems add context and interoperability.
15:00 The talk turns to library science’s split from big data in the 1990s, metadata schemas, and the FAIR principles of findability and reuse.
20:00 They discuss neutrality, bias in corporate vocabularies, and why “touching grass” matters for reconciling internal and external meanings.
25:00 Conversation shifts to interpretability, cultural context, and how Western categorical thinking differs from China’s contextual knowledge.
30:00 Jessica introduces process knowledge, documentation habits, and the danger of outsourcing how-to understanding.
35:00 They explore knowledge as habit, the tension between break-things culture and library design thinking, and early AI experiments.
40:00 Libraries’ strategic use of AI, metadata precision, and the emerging role of AI librarians take focus.
45:00 Stewart connects data labeling, Surge AI, and the economics of good data with Jessica’s call for better knowledge architectures.
50:00 They unpack content lifecycle, provenance, and user context as the backbone of knowledge ecosystems.
55:00 The talk closes on automation limits, human-in-the-loop design, and Jessica’s vision for collaborative consulting through Contextually.

Key Insights

1. Ontology is about meaning, not just data structure. Jessica Talisman reframes ontology from a philosophical abstraction into a practical tool for knowledge management—defining how things relate and what they mean within systems. She explains that without clear categories and shared definitions, organizations can’t scale or communicate effectively, either with people or with machines.
2. Controlled vocabularies are the foundation of AI literacy. Jessica emphasizes that building a controlled vocabulary is the simplest and most powerful way to disambiguate meaning for AI. Machines, like people, need context to interpret language, and consistent terminology prevents the “hallucinations” that occur when systems lack semantic grounding.
3. Library science predicted today’s knowledge crisis. Stewart and Jessica trace how, in the 1990s, tech went down the path of “big data” while librarians quietly built systems of metadata, ontologies, and standards like schema.org. Today’s AI challenges—interoperability, reliability, and information overload—mirror problems library science has been solving for decades.
4. Knowledge is culturally shaped. Drawing from Patrick Lambe’s work, Jessica notes that Western knowledge systems are category-driven, while Chinese systems emphasize context. This cultural distinction explains why global AI models often miss nuance or moral voice when trained on limited datasets.
5. Process knowledge is disappearing. The West has outsourced its “how-to” knowledge—what Jessica calls process knowledge—to other countries. Without documentation habits, we risk losing the embodied know-how that underpins manufacturing, engineering, and even creative work.
6. Automation cannot replace critical thinking. Jessica warns against treating AI as “room service.” Automation can support, but not substitute, human judgment. Her own experience with a contract error generated by an AI tool underscores the importance of review, reflection, and accountability in human–machine collaboration.
7. Collaborative consulting builds knowledge resilience. Through her consultancy, Contextually, Jessica advocates for “teaching through doing”—helping teams build their own ontologies and vocabularies rather than outsourcing them. Sustainable knowledge systems, she argues, depend on shared understanding, not just good technology.