| --- |
| license: other |
| license_name: original-source-licences |
| pretty_name: KGGuard+W checkpoints and reproduction artifacts |
| tags: |
| - knowledge-graph |
| - error-detection |
| - fact-checking |
| - reproducibility |
| language: |
| - en |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # KGGuard+W: checkpoints and reproduction artifacts |
|
|
| This repository holds every artifact needed to reproduce the results of **KGGuard+W**, a method that detects erroneous facts in knowledge graphs. It covers DBpedia Accuracy and FactBench. Code: GitHub `coderkhongodo/KGG` (see its README). |
|
|
| KGGuard+W does **not** train a neural network of its own. Seven label-free evidence sources are calibrated into log-likelihood ratios around a hierarchical predicate prior, then combined by a cross-fitted, monotone boosted GAM: |
|
|
| - **C:** SHACL constraints |
| - **S:** structural anomaly (Isolation Forest) |
| - **S_sup:** relation-conditional structural support |
| - **M:** sentence similarity |
| - **T:** text mention |
| - **N:** NLI on entity descriptions |
| - **W:** NLI on retrieved web passages |
| |
| The learned state, i.e. the "checkpoint", is the fitted fusion model. Missing evidence contributes exactly 0, and per-source contributions are exact. |
| |
| ## Contents |
| |
| | Path | What | |
| |---|---| |
| | `checkpoints/<dataset>/<run>/kgguard_plus_w_seed<s>.joblib` | final fusion model (KGGuard+W) per dataset × run × seed; `.json` = readable description | |
| | `checkpoints/<dataset>/<run>/kgguard_plus_kg_only_seed<s>.joblib` | the same model without web evidence (KGGuard+) | |
| | `checkpoints/verified_metrics.csv` | test metrics of every checkpoint; they equal the reported ones (refit check ≤ 1e-9) | |
| | `outputs/<dataset>/<run>/scores/` | every cached evidence and baseline score, including LLM zero-shot, FactCheck (DKA, GIV-F, RAG, consensus), TransE, ComplEx, CAGED, PaTyBRED, SDValidate | |
| | `data/raw`, `data/interim`, `data/interim_ms` | raw and processed facts, frozen splits, context graphs; `interim_ms` = the 5 fresh splits | |
| | `data/cache` | SPARQL / HTTP responses, encoder embeddings, NLI and LLM / FactCheck answers, retrieved web passages | |
| | `reference/<dataset>/<run>/` | reported results (E1–E7) used for comparison; `reference/summaries/`: multi-split, robustness, label efficiency, paper tables | |
| | `explanations/<dataset>/<run>/` | alert lists with per-source explanations, test predictions | |
| | `MANIFEST.json` | sha256 of every file, library versions, code commit | |
| |
| Runs: |
| - `v7` is the design split, with seeds 13, 42 and 2027. |
| - `ms7_s101` … `ms7_s505` are five fresh grouped splits, each with seed 13. |
| |
| ## Reproduce |
| |
| ```bash |
| git clone https://github.com/coderkhongodo/KGG.git && cd KGG |
| pip install -e ".[dev,hf]" |
| PYTHONPATH=src python scripts/reproduce_from_hf.py --local-dir hf_checkpoint --full-e1 --ablation |
| ``` |
| |
| The script: |
| 1. downloads this repository and verifies every file against `MANIFEST.json`; |
| 2. reloads every checkpoint, scores the test facts and compares AP, AUROC, F1 and BAcc with the reported values; |
| 3. refits every model from the downloaded scores and compares again; |
| 4. with `--full-e1`, re-runs the full main comparison (all baselines, 3 seeds, bootstrap); |
| 5. with `--ablation`, re-runs the ablation of the proposed method (15 variants, design split and 5 fresh splits). |
| |
| No network access, LLM or GPU is needed. |
| |
| **Verified** on a fresh clone with an anonymous download, on CPU (32 threads): |
|
|
| | Check | Result | |
| |---|---| |
| | sha256 of all 3,860 files | 0 mismatches | |
| | 32 checkpoints, inference | AP / AUROC / F1 / BAcc identical (0 difference) | |
| | 32 checkpoints, retrained from the downloaded scores | identical | |
| | full E1 (29 methods x 3 seeds x 2 datasets) | identical | |
| | ablation (240 rows) | identical | |
|
|
| Checkpoints are pickles (joblib). Load them only if you trust the source, preferably with the same scikit-learn version: |
| - Python 3.12 |
| - scikit-learn 1.9.1 |
| - numpy 2.5.4 |
| - pandas 3.1.1 |
|
|
| ## Main results (test AP, mean of 3 seeds) |
|
|
| | | DBpedia Accuracy | FactBench | |
| |---|---|---| |
| | best published baseline | 0.257 (LLM zero-shot) | 0.834 (FactCheck-RAG) | |
| | CAGED (CIKM'22) | 0.142 | 0.533 | |
| | **KGGuard+W** | **0.470** | **0.928** | |
|
|
| ## Data sources and licences |
|
|
| Redistributed for reproducibility, under the licences of the original sources: |
| - **DBpedia Accuracy** (Marchesin, Silvello, Alonso, HCOMP 2024) and DBpedia context data: CC BY-SA. |
| - **FactBench** (DeFacto, AKSW). |
| - **Web passages** retrieved from the FactCheck corpus (SIGIR'25 / EDBT'26). |
|
|
| Please cite the original datasets when you use them. |
|
|
