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Knowledge Value Lab (KVL)
A Framework for Measuring the Marginal Value of Knowledge Assets for AI Systems
Concept Note and Implementation Plan
Executive Summary
Organizations around the world are investing heavily in creating, curating, digitizing, licensing, and publishing knowledge assets for use in artificial intelligence systems.
These assets include:
- Research papers
- Technical reports
- Books
- Policy documents
- Government publications
- Educational materials
- Domain-specific knowledge bases
- Datasets
- Web archives
Despite growing investments in AI-ready content, there is currently no widely accepted method for answering a fundamental question:
How much value does a newly available knowledge asset contribute to AI systems?
A document may contain information that is already embedded in existing foundation models, in which case its marginal contribution is small. Alternatively, it may contain unique knowledge that significantly improves retrieval systems, AI assistants, decision-support tools, and downstream applications.
Knowledge Value Lab (KVL) is a proposed framework and software platform designed to measure the marginal value of knowledge assets for AI systems. The platform evaluates individual documents, datasets, repositories, and collections using a standardized methodology that combines knowledge novelty, retrieval performance, answer quality, grounding, and user demand.
The result is a transparent and reproducible "Knowledge Value Score" that quantifies the contribution of information assets to AI ecosystems.
1. Motivation
The emergence of foundation models has fundamentally changed how knowledge is consumed and utilized.
Historically, the value of a document was measured through indicators such as:
- Citations
- Downloads
- Sales
- Views
- Academic impact
These measures provide limited insight into the role of knowledge within AI systems.
A document that is rarely cited may substantially improve an AI assistant's ability to answer questions. Conversely, a highly cited document may contribute little additional value if its contents are already extensively represented within existing training corpora.
Organizations increasingly face decisions regarding:
- Which content should be digitized?
- Which repositories should be prioritized?
- Which datasets deserve funding?
- Which knowledge assets should be licensed for AI applications?
- Which public data investments create the greatest societal return?
KVL seeks to provide evidence-based answers to these questions.
2. Vision
To create a standardized framework for measuring the contribution of knowledge assets to AI systems, enabling informed decisions about data investments, content sharing, and knowledge infrastructure development.
3. Core Research Questions
Knowledge Novelty
How much information contained in an asset is already known by contemporary AI models?
Retrieval Utility
How much does the asset improve information retrieval systems?
Generation Utility
How much does the asset improve AI-generated responses?
Attribution Utility
Can improvements be directly attributed to the asset?
Demand Utility
How frequently is the knowledge needed by users?
Social Utility
What societal value may arise from making the knowledge available?
4. Conceptual Framework
KVL treats every knowledge asset as a potential contributor to AI capability.
The framework estimates value across five dimensions.
Dimension 1: Knowledge Novelty
Measures whether the information contained within a document is already represented in existing AI models.
Examples:
- Recently published research
- Local knowledge
- Proprietary content
- Specialized technical documentation
- Low-resource language materials
may receive high novelty scores.
Widely distributed information may receive lower scores.
Outputs
Knowledge Novelty Score
0–100
Dimension 2: Retrieval Utility
Measures whether the asset improves search and retrieval systems.
Typical evaluation metrics include:
- Recall@K
- Mean Reciprocal Rank
- nDCG
- Context Precision
- Context Recall
Outputs
Retrieval Utility Score
0–100
Dimension 3: Generation Utility
Measures whether access to the asset improves AI-generated outputs.
Applications include:
- Question answering
- Summarization
- Advisory systems
- Research assistants
- Educational tutors
- Enterprise knowledge assistants
Evaluation criteria include:
- Accuracy
- Completeness
- Specificity
- Relevance
- Actionability
- Safety
Outputs
Generation Utility Score
0–100
Dimension 4: Attribution and Grounding
Measures whether observed improvements genuinely originate from the asset.
Key questions include:
- Is the document being retrieved?
- Is evidence from the document being used?
- Are generated outputs properly grounded?
Outputs
Grounding Score
0–100
Dimension 5: Demand Utility
Measures the practical importance of the knowledge.
Examples include:
- Frequency of related user queries
- Coverage of unmet information needs
- Relevance to priority domains
- Geographic relevance
- Language coverage
Outputs
Demand Utility Score
0–100
5. Knowledge Value Score
The overall score combines all dimensions into a single measure.
KVS =
30% Knowledge Novelty
20% Retrieval Utility
25% Generation Utility
15% Grounding Utility
10% Demand Utility
Result:
0–100
Classification:
0–20 Minimal Value
21–40 Incremental Value
41–60 Moderate Value
61–80 High Value
81–100 Transformational Value
6. System Architecture
Module A: Knowledge Novelty Engine
Functions:
- Claim extraction
- Question generation
- Closed-book model evaluation
- Cross-model comparison
- Novelty estimation
Outputs:
Knowledge Novelty Score
Module B: Retrieval Evaluation Engine
Functions:
- Index creation
- Retrieval benchmarking
- Search quality assessment
- Comparative experiments
Outputs:
Retrieval Utility Score
Module C: Generation Evaluation Engine
Functions:
- Response generation
- Multi-model testing
- Quality assessment
- Human and AI judging
Outputs:
Generation Utility Score
Module D: Attribution Engine
Functions:
- Citation analysis
- Evidence tracing
- Source attribution
- Grounding verification
Outputs:
Grounding Score
Module E: Demand Analysis Engine
Functions:
- Query log analysis
- Topic modeling
- Gap detection
- User demand estimation
Outputs:
Demand Utility Score
7. User Experience
Users upload:
- Word documents
- Web pages
- Datasets
- Knowledge collections
The platform automatically:
- Ingests content
- Extracts claims
- Generates evaluation tasks
- Executes experiments
- Computes scores
- Produces a report
Typical runtime:
Minutes to hours depending on corpus size.
8. Dashboard Outputs
The platform generates a Knowledge Value Report containing:
Overall Knowledge Value Score
Knowledge Novelty Assessment
Retrieval Impact Analysis
Generation Impact Analysis
Attribution Assessment
Demand Analysis
Recommended Actions
Examples:
- Publish openly
- Prioritize indexing
- Translate into additional languages
- Integrate into retrieval systems
- Acquire licensing rights
- Merge with related collections
9. Extension to Repository-Level Evaluation
The framework can be applied to:
- Digital libraries
- Academic repositories
- Government archives
- Corporate knowledge bases
- Publisher collections
- Data commons
- Open data platforms
This enables comparative analyses such as:
- Which repository contributes the most novel knowledge?
- Which collection generates the largest gains in AI performance?
- Which public data investments generate the greatest value?
10. Social Return on Knowledge
An optional extension estimates downstream societal value.
Knowledge assets are evaluated not only by their impact on AI performance but also by their contribution to real-world outcomes.
Examples:
Document → Improved AI Output → Better Decision → Improved Outcome
Possible outcome domains include:
- Education
- Healthcare
- Agriculture
- Public administration
- Climate adaptation
- Scientific research
This extension enables estimation of a Social Return on Knowledge (SRK) score.
11. Long-Term Vision
Knowledge Value Lab aims to become a standard for measuring the value of knowledge in the AI era.
Just as citation metrics transformed scholarly communication and web analytics transformed digital publishing, KVL seeks to establish a new class of metrics that quantify how knowledge contributes to artificial intelligence systems.
The ultimate goal is to enable governments, publishers, researchers, funders, and technology developers to make evidence-based decisions about the creation, sharing, preservation, and financing of knowledge assets in a world increasingly mediated by AI.