| # Data Preparation Guide |
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| ## Supported Datasets |
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| EvoRM is validated on the **ICEWS-WIKI** dataset for entity alignment (EA) tasks. |
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| ### ICEWS-WIKI (HHEA) |
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| 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 |
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|
| ``` |
| 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 |
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| | 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 | |
|
|
| --- |
|
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| ## Adding New Datasets |
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| To run EvoRM on a new dataset, ensure the following files are available: |
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| 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 |
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|
| Then run: |
| ```python |
| from LLM1_label_selector import align_entities |
| aligned_pairs = align_entities(data_dir="./data/your_dataset") |
| ``` |
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| --- |
|
|
| ## DBP15K Datasets |
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| 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) |
