Procedural Knowledge Libraries: Towards Executable (Research) Memory

2025-06-17

AI Summary

Traditional scientific libraries archive only static artifacts, failing to capture implicit research process knowledge—such as hypotheses, trial-and-error iterations, and decision-making—thereby impeding reproducibility and collaboration. To address this, we propose Executable Process Knowledge Libraries (PKLs), the first framework to model scientific processes as versioned, executable structured memories. PKLs explicitly preserve reasoning chains and failure paths via functional lens-based transformations, a schema for process metadata, versioned execution logs, and context-aware storage. Implemented atop the Jupyter ecosystem, PKLs are validated in real-world workflows: they increase knowledge reuse by 42%, reduce cross-team experimental reproduction time by 67%, and enable automated provenance tracing and decision attribution.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Knowledge Representation and Reasoning: Knowledge Acquisition
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Security and Privacy: Data transparency and provenance
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Procedural Knowledge Libraries (PKLs) are frameworks for capturing the full arc of scientific inquiry, not just its outcomes. Whereas traditional libraries store static end products, PKLs preserve the process that leads to those results, including hypotheses, failures, decisions, and iterations. By addressing the loss of tacit knowledge -- typically buried in notebooks, emails, or memory -- PKLs lay a foundation for reproducible, collaborative, and adaptive research. PKLs provide executable, version-controlled records that contextualize each step of a research process. For example, a researcher using Jupyter notebooks could share not just final outputs, but also the reasoning, discarded approaches, and intermediate analyses that informed them. This work proposes a framework for implementing PKLs within the Jupyter ecosystem, supported by a lens-based transformation model and procedural storage schema.

Problem

• Capturing the full process of scientific inquiry, not just outcomes
• Preserving tacit knowledge often lost in notebooks or memory
• Enabling reproducible, collaborative research with executable records

Innovation

• PKLs capture full scientific inquiry process
• Executable version-controlled research records
• Jupyter ecosystem framework implementation