# Data Preparation Guide

## Supported Datasets

EvoRM is validated on the **ICEWS-WIKI** dataset for entity alignment (EA) tasks.

### ICEWS-WIKI (HHEA)

ICEWS-WIKI is a Highly Heterogeneous Knowledge Graph Alignment dataset where:
- KG1 uses ICEWS relation IDs (e.g., 001, 002)
- KG2 uses Wikidata relation IDs (e.g., P31, P17)
- The relation schemas are completely different, making direct rule matching challenging

#### Data Structure

```
data/icews_wiki/
├── ent_ids_1          # KG1 entity ID → name mapping
├── ent_ids_2          # KG2 entity ID → name mapping
├── rel_ids_1          # KG1 relation ID → name mapping
├── rel_ids_2          # KG2 relation ID → name mapping
├── triples_1          # KG1 triples (h, r, t)
├── triples_2          # KG2 triples (h, r, t)
├── ref_pairs          # Reference (ground truth) entity pairs
├── ref_ent_ids        # Reference entity IDs
├── sup_pairs          # Supervision pairs (training)
├── message_pool/
│   ├── retriever_outputs.txt    # Candidate pairs (KG1→KG2, 5 candidates each)
│   ├── important_entities.txt   # Subset for LLM processing
│   ├── aligned_entities.txt     # Output aligned pairs
│   └── alignment_rules.txt      # Reference rules (optional)
└── evorm_state/                 # EvoRM persistent state
    ├── rules.json               # Distilled FOL rules
    └── hyperedges.json          # Hypergraph structure
```

#### Statistics

| Metric | Value |
|--------|-------|
| Reference pairs | 3,540 |
| KG1 entities | 3,540 |
| KG2 entities | 15,831 |
| Candidate pairs | 17,700 (5 per KG1 entity) |
| KG1 relations | 256 |
| KG2 relations | 682 |

---

## Adding New Datasets

To run EvoRM on a new dataset, ensure the following files are available:

1. `ent_ids_1` / `ent_ids_2`: Tab-separated entity ID → name mapping
2. `rel_ids_1` / `rel_ids_2`: Tab-separated relation ID → name mapping
3. `triples_1` / `triples_2`: Tab-separated triples (head, relation, tail)
4. `ref_pairs`: Tab-separated ground truth pairs
5. `message_pool/important_entities.txt`: Candidate pairs for LLM processing

Then run:
```python
from LLM1_label_selector import align_entities
aligned_pairs = align_entities(data_dir="./data/your_dataset")
```

---

## DBP15K Datasets

The paper also evaluates on DBP15K variant datasets (EN-FR, EN-DE, ZH-EN, JA-EN, FR-EN). These datasets follow a similar structure and can be downloaded from:
- [DBP15K](https://github.com/nju-websoft/OpenEA)
- [JAPE](https://github.com/nju-websoft/JAPE)