VANILLA is a comprehensive framework designed to enhance Knowledge Graph Completion (KGC) by validating inferred facts using logical constraints derived from domain knowledge and normalizing the exisiting anomalies in KGs. Unlike traditional methods that rely solely on vector embeddings, VANILLA integrates symbolic reasoning with numerical learning to ensure logical validity, integrity, and accuracy of predictions. The framework uses SHACL constraints to validate predictions and supports symbolic rule mining, constraint checking, and KGC evaluation with state-of-the-art embedding models.
.
├── KG/ # Benchmark knowledge graphs
│ ├── French_Royalty/
│ ├── SGKG/
│ ├── SynthLC-1000/
│ ├── SynthLC-10000/
│ ├── YAGO3-10/
│ └── DB100K/
│
├── Rules/ # Symbolic horn rules for each benchmark
│ ├── French_Royalty/
│ ├── SGKG/
│ ├── SynthLC-1000/
│ ├── SynthLC-10000/
│ ├── YAGO3-10/
│ └── DB100K/
├── Constraints/ # SHACL constraints
│ ├── French_Royalty/
│ ├── SGKG/
│ ├── SynthLC-1000/
│ ├── SynthLC-10000/
│ ├── YAGO3-10/
│ └── DB100K/
│
├── Predictions/ # Output predictions
├── LICENSE.txt
├── README.md
├── input.json
├── requirements.txt
├── symbolic_predictions_updated.py
├── transform_new.py
└── validation.py
| KG Size | Benchmark | #Triples | #Entities | #Relations |
|---|---|---|---|---|
| Large | DB100K | 695,572 | 99,604 | 470 |
| SynthLC-10000 | 106,549 | 10,000 | 9 | |
| Medium | YAGO3-10 | 1,080,264 | 123,086 | 37 |
| SGKG | 54,585 | 36,450 | 6 | |
| Small | French Royalty | 10,526 | 2,601 | 12 |
| SynthLC-1000 | 10,668 | 1,000 | 9 |
| KG Size | Benchmark | #Constraints | #Valid | #Invalid |
|---|---|---|---|---|
| Large | DB100K | 6 | 390,351 | 62,024 |
| SynthLC-10000 | 25 | 223,523 | 26,477 | |
| Medium | YAGO3-10 | 4 | 393,205 | 58,719 |
| SGKG | 5 | 156,965 | 12,150 | |
| Small | French Royalty | 2 | 1,922 | 298 |
| SynthLC-1000 | 25 | 22,335 | 2,665 |
-
Clone the repository
git clone [email protected]:SDM-TIB/VANILLA.git
-
Install dependencies
pip install -r requirements.txt
-
Configure input Modify
input.jsonto select the benchmark KG and rule/constraint files.
Run the script to generate predictions and validate them:
python symbolic_predictions_updated.pyThis will:
- Generate inferred predictions using logical rules.
- Validate them against SHACL constraints.
- Output:
- Transformed KGs
- Constraint validation reports in
Constraints/ - Predictions in
Predictions/
We evaluate KG completion using embedding models:
- TransE, TransH, TransD
- RotatE, ComplEx, TuckER
- CompGCN
Metrics reported:
- Hits@1, Hits@3, Hits@5, Hits@10
- Mean Reciprocal Rank (MRR)
The VANILLA framework integrates symbolic rules, domain constraints, and neural embeddings for high-quality knowledge graph completion. It identifies valid and invalid triples using evolving logical constraints and employs numerical models to infer missing links, ensuring semantic consistency and logical soundness in the normalized KG.
This project is licensed under the terms of the LICENSE.txt.
VANILLA has been developed by members of the Scientific Data Management Group at TIB, as an ongoing research effort. The development is co-ordinated and supervised by Maria-Esther Vidal. We strongly encourage you to report any issues you have with VANILLA. Please, use the GitHub issue tracker to do so. VANILLA has been implemented in joint work by Disha Purohit, and Yashrajsinh Chudasama.