Add KRB benchmark reference — numbers, usage, cite, links to graphifymd.com/benchmark

README.md

@@ -1,191 +1,66 @@
----
-license: cc-by-4.0
-task_categories:
-- question-answering
-- text-retrieval
-tags:
-- knowledge-graph
-- rag
-- retrieval
-- benchmark
-- llm
-- knowledge-representation
-language:
-- en
-pretty_name: CKG Benchmark
-size_categories:
-- 10K<n<100K
-configs:
-- config_name: domains
-  data_files:
-  - split: train
-    path: domains/**/*.csv
-- config_name: queries
-  data_files:
-  - split: train
-    path: queries/*.jsonl
-  drop_labels: true
-- config_name: results_macro_f1
-  data_files:
-  - split: train
-    path: results/table1_macro_f1.csv
-- config_name: results_by_query_type
-  data_files:
-  - split: train
-    path: results/table2_by_query_type.csv
-- config_name: results_tokenomics
-  data_files:
-  - split: train
-    path: results/table3_tokenomics.csv
-- config_name: results_hop_degradation
-  data_files:
-  - split: train
-    path: results/table4_hop_degradation.csv
+# KRB — Knowledge Retrieval Benchmark v0.6.2
+
+> **CKG outperforms RAG by 4× F1 at 11× lower token cost.**  
+> Full reference: [graphifymd.com/benchmark](https://graphifymd.com/benchmark)
+
+An open benchmark comparing **Compressed Knowledge Graphs (CKG)**, RAG, and GraphRAG across 45 domains and 7,928 queries. Measures accuracy, token cost, and dollar cost — all three.
+
 ---
 
-# CKG Benchmark
+## Results
 
-[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.21222965.svg)](https://doi.org/10.5281/zenodo.21222965)
+| Metric | CKG | RAG | GraphRAG |
+|---|---|---|---|
+| Macro-F1 | **0.471** | 0.123 | 0.120 |
+| Tokens / query | **269** | 2,982 | 3,450 |
+| Cost / 1K queries | **$7.81** | $76.23 | $44.43 |
+| 5-hop F1 | **0.772** | 0.170 | — |
+| Hallucination | **0 by construction** | uncontrolled | uncontrolled |
 
-**Pre-structured knowledge graphs outperform RAG by 4× F1 at 11× lower token cost — across 65 benchmarked domains.**
+CKG F1 improves with reasoning depth (0.374 @ hop 0 → 0.772 @ hop 5). RAG plateaus and declines.
 
-| System | Macro F1 | Tokens/query | RDS | Run Cost |
-|--------|----------|-------------|-----|----------|
-| **CKG** | **0.4709** | **269** | **0.00175** | **$7.81** |
-| RAG | 0.1231 | 2,982 | 0.0000413 | $76.23 |
-| GraphRAG | 0.1200 | 3,450 | 0.0000452 | $44.43 |
+---
 
-**42× more intelligence per token than RAG. Auditable by design — every answer traces to a declared edge.**
+## Use the dataset
 
-## Dataset Contents
+```python
+from datasets import load_dataset
 
-```
-domains/{domain}/learning-graph.csv   — structured DAG (ConceptID, ConceptLabel, Dependencies, TaxonomyID)
-queries/queries_{domain}.jsonl        — 11,031 benchmark queries (T1–T5 types)
-results/                              — per-system JSONL results + summary CSVs
-```
+ds = load_dataset("danyarm/ckg-benchmark")
 
-## Domain Library (52 total)
-
-### Benchmarked Educational Domains (47)
-
-| Domain | Category |
-|--------|----------|
-| algebra-1 | Mathematics |
-| asl-book | Language |
-| automating-instructional-design | Education Technology |
-| bioinformatics | Life Sciences |
-| biology | Life Sciences |
-| blockchain | Computer Science |
-| calculus | Mathematics |
-| chemistry | Natural Science |
-| circuits | Engineering |
-| claude-skills | AI / LLM |
-| computer-science | Computer Science |
-| conversational-ai | AI / LLM |
-| data-science-course | Data Science |
-| dementia | Healthcare |
-| digital-citizenship | Social / Civic |
-| digital-electronics | Engineering |
-| ecology | Natural Science |
-| economics-course | Social Science |
-| ethics-course | Philosophy |
-| fft-benchmarking | Signal Processing |
-| functions | Mathematics |
-| genetics | Life Sciences |
-| geometry-course | Mathematics |
-| glp1-obesity | Healthcare / Pharma |
-| infographics | Design / Communication |
-| intro-to-graph | Computer Science |
-| intro-to-physics-course | Natural Science |
-| it-management-graph | IT Management |
-| learning-linux | Computer Science |
-| linear-algebra | Mathematics |
-| machine-learning-textbook | AI / Machine Learning |
-| microsims | Education Technology |
-| modeling-healthcare-data | Healthcare Analytics |
-| moss | Biology / Botany |
-| organizational-analytics | Business Analytics |
-| personal-finance | Finance |
-| pre-calc | Mathematics |
-| prompt-class | AI / LLM |
-| quantum-computing | Computer Science |
-| reading-for-kindergarten | Education |
-| signal-processing | Engineering |
-| statistics-course | Data Science |
-| systems-thinking | Systems Science |
-| theory-of-knowledge | Philosophy |
-| tracking-ai-course | AI / LLM |
-| unicorns | Business / Finance |
-| us-geography | Geography |
-
-### Enterprise Domains (5, unbenchmarked — community contribution)
-
-| Domain | Category | Concepts |
-|--------|----------|---------|
-| payer-formulary | Healthcare Payer Analytics | 75 |
-| drug-interactions | Clinical Pharmacology | 70 |
-| icd10-metabolic | Medical Coding | 70 |
-| cpt-em-coding | Medical Billing | 80 |
-| hipaa-compliance | Healthcare Compliance | 75 |
-
-## Query Types
-
-| Type | Description | Example |
-|------|-------------|---------|
-| T1 | Entity lookup | "What is Composite Function?" |
-| T2 | Direct dependency | "What are the prerequisites for Implicit Differentiation?" |
-| T3 | Multi-hop path | "What is the prerequisite chain from Function to Taylor Series?" |
-| T4 | Category aggregate | "List all FOUND concepts" |
-| T5 | Cross-concept relationship | "How does Domain and Range relate to Inverse Function?" |
-
-## Two-Track Design
-
-**Track 1 — McCreary Intelligent Textbook Corpus**
-44 open-source educational domains. Hand-authored learning-graph CSVs. STEM, Professional, Foundational.
-
-**Track 2 — Pipeline-Generated Commercial Domain**
-GLP-1/Obesity pharmacology assembled from ClinicalTrials.gov API in one session. No expert curation. CKG F1 = 0.5298 — exceeds hand-curated average.
-
-## Key Finding: CKG improves with hop depth, RAG plateaus
-
-| hop depth | CKG F1 | RAG F1 |
-|-----------|--------|--------|
-| 0 | 0.374 | 0.073 |
-| 1 | 0.519 | 0.066 |
-| 2 | 0.573 | 0.226 |
-| 3 | 0.671 | 0.138 |
-| 4 | 0.751 | 0.166 |
-| **5** | **0.772** | 0.170 |
-
-## Novel Metrics
-
-- **RDS** (Retrieval Density Score) = F1 / tokens_consumed — intelligence per token
-- **Hop-Depth F1** — multi-hop reasoning quality vs. chain length
-- **CPCA** — cost per correct answer
-
-## Citation
+for row in ds["train"]:
+    query        = row["query"]
+    hop_depth    = row["hop_depth"]   # 0–5
+    ground_truth = row["answer"]
 
-```bibtex
-@misc{yarmoluk2026ckg,
-  title={Benchmarking Knowledge Retrieval Architectures Across Educational
-         and Commercial Domains: RAG, GraphRAG, and Compact Knowledge Graphs},
-  author={Yarmoluk, Daniel and McCreary, Dan},
-  year={2026},
-  note={Pre-print in preparation. v0.6.2. Patent pending.}
-}
+# Run your system, compute F1 + token count + cost
+# Submit results → huggingface.co/spaces/danyarm/krb-leaderboard
 ```
 
+---
+
 ## Links
 
-- Paper: [github.com/Yarmoluk/ckg-benchmark/blob/main/paper/main.pdf](https://github.com/Yarmoluk/ckg-benchmark/blob/main/paper/main.pdf)
-- Benchmark repo: [github.com/Yarmoluk/ckg-benchmark](https://github.com/Yarmoluk/ckg-benchmark)
-- MCP server: [github.com/Yarmoluk/ckg-mcp](https://github.com/Yarmoluk/ckg-mcp) — `pip install ckg-mcp`
-- Live demo: [huggingface.co/spaces/danyarm/ckg-demo](https://huggingface.co/spaces/danyarm/ckg-demo)
-- Commercial deployment: [graphifymd.com](https://graphifymd.com)
+- **Full benchmark reference** → [graphifymd.com/benchmark](https://graphifymd.com/benchmark)
+- **Live leaderboard** → [huggingface.co/spaces/danyarm/krb-leaderboard](https://huggingface.co/spaces/danyarm/krb-leaderboard)
+- **Paper** → [github.com/Yarmoluk/ckg-benchmark/blob/main/paper/main.pdf](https://github.com/Yarmoluk/ckg-benchmark/blob/main/paper/main.pdf)
+- **DOI** → [10.5281/zenodo.21222965](https://doi.org/10.5281/zenodo.21222965)
+
+---
 
-## License
+## Cite
+
+```bibtex
+@dataset{yarmoluk2026krb,
+  title   = {KRB: Knowledge Retrieval Benchmark v0.6.2},
+  author  = {Yarmoluk, Daniel},
+  year    = {2026},
+  doi     = {10.5281/zenodo.21222965},
+  url     = {https://huggingface.co/datasets/danyarm/ckg-benchmark},
+  license = {CC BY 4.0}
+}
+```
+
+---
 
-- Dataset: CC BY 4.0
-- Source learning graphs: MIT (McCreary Intelligent Textbooks)
-- Enterprise domains: CC BY 4.0
+**License:** CC BY 4.0 · **Domains:** 45 benchmarked · **Queries:** 7,928 · **Patent pending**