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--- |
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license: apache-2.0 |
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task_categories: |
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- graph-ml |
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language: |
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- en |
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size_categories: |
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- 10M<n<100M |
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--- |
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TL;DR: The datasets for the temporal knowledge graph reasoning task. |
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[[Github]](https://github.com/LinXueyuanStdio/TFLEX) |
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[[OpenReview]](https://openreview.net/forum?id=oaGdsgB18L) |
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[[arXiv]](https://arxiv.org/abs/2205.14307) |
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- Built over ICEWS and GDELT, which are widely used benchmarks in TKGC. |
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- First introduced in paper "TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge Graph" |
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- Please refer to the original paper for more details. |
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See also: [[ICEWS14]](https://huggingface.co/datasets/linxy/ICEWS14) [[ICEWS05_15]](https://huggingface.co/datasets/linxy/ICEWS05_15) |
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## 🔬 Usage |
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```python |
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>>> dataset = load_dataset("linxy/GDELT", "all") |
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>>> len(dataset["train"]) + len(dataset["validation"]) + len(dataset["test"]) |
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22117475 |
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>>> dataset["train"][0] |
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{'query_name': 'Pe', |
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'definition': 'def Pe(e1, r1, t1): return Pe(e1, r1, t1)', |
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'query': [483, 18, 217], |
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'answer': [26, 33, 40, 45, 65, 105, 107, 121, 139, 172, 187, 216, 264, 270, 313, 460, 480, 493], |
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'easy_answer': [], |
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'args': ['e1', 'r1', 't1']} |
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>>> dataset["test"][0] |
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{'query_name': 'Pe2', |
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'definition': 'def Pe2(e1, r1, t1, r2, t2): return Pe(Pe(e1, r1, t1), r2, t2)', |
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'query': [242, 38, 229, 1, 244], |
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'answer': [9, 11, 24, 46, 76, 121, 140, 146, 209, 275, 280, 300, 380, 445, 463, 484], |
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'easy_answer': [9, 11, 24, 46, 76, 146, 280, 300, 380, 445, 484], |
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'args': ['e1', 'r1', 't1', 'r2', 't2']} |
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``` |
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'args' is the argument list of the query function, where name starting with 'e' is entity, and 'r' for relation, 't' for timestamp. |
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assert len(query) == len(args) |
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In order to decode query ids into text, we should use a vocabulary (i.e. entity2idx, relation2idx and timestamp2idx). |
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Therefore, we use the code below to load meta info which contains the vocabulary: |
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```python |
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>>> dataset = load_dataset("linxy/GDELT", "meta") |
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>>> meta_info = dataset_meta["train"][0] |
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>>> meta_info |
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{'dataset': 'GDELT', |
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'entity_count': 500, |
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'relation_count': 20, |
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'timestamp_count': 366, |
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'valid_triples_count': 330906, |
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'test_triples_count': 330845, |
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'train_triples_count': 2308165, |
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'triple_count': 2969916, |
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'query_meta': {'query_name': [...], 'queries_count': [...], 'avg_answers_count': [...], ...}, |
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'entity2idx': {'name': [...], 'id': [...]}, |
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'relation2idx': {'name': [...], 'id': [...]}, |
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'timestamp2idx': {'name': [...], 'id': [...]}, |
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``` |
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Since the ids in the vocabulary are already sorted, we directly decode to access the name text: |
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```python |
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>>> query |
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[483, 18, 217] |
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>>> args |
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['e1', 'r1', 't1'] |
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>>> for idx, arg_type in zip(query, args): |
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if arg_type.startswith('e') or arg_type.startswith('s') or arg_type.startswith('o'): # s, o, e1, e2, ... |
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print(idx, meta_info['entity2idx']['name'][idx]) |
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elif arg_type.startswith('r'): # r, r1, r2, ... |
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print(idx, meta_info['relation2idx']['name'][idx]) |
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elif arg_type.startswith('t'): # t, t1, t2, ... |
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print(idx, meta_info['timestamp2idx']['name'][idx]) |
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``` |
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Besides, we also provide query-type-specific subparts. |
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```python |
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>>> dataset = load_dataset("linxy/GDELT", "e2i") |
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>>> some_datasets = [load_dataset("linxy/GDELT", query_name) for query_name in meta_info['query_meta']['query_name']] |
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``` |
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Help yourself! |
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<details> |
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<summary>👈 🔎 Dataset statistics: queries_count</summary> |
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| query | ICEWS14| | | ICEWS05_15| | | GDELT | | | |
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| :---- | :---- | :---- | :--- | :---- | :---- | :--- | :---- | :---- | :--- | |
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| | train | valid | test | train | valid | test | train | valid | test | |
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| Pe | 66783 | 8837 | 8848 | 344042 | 45829 | 45644 | 1115102 | 273842 | 273432 | |
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| Pe2 | 72826 | 3482 | 4037 | 368962 | 10000 | 10000 | 2215309 | 10000 | 10000 | |
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| Pe3 | 72826 | 3492 | 4083 | 368962 | 10000 | 10000 | 2215309 | 10000 | 10000 | |
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| e2i | 72826 | 3305 | 3655 | 368962 | 10000 | 10000 | 2215309 | 10000 | 10000 | |
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| e3i | 72826 | 2966 | 3023 | 368962 | 10000 | 10000 | 2215309 | 10000 | 10000 | |
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| Pt | 42690 | 7331 | 7419 | 142771 | 28795 | 28752 | 687326 | 199780 | 199419 | |
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| aPt | 13234 | 4411 | 4411 | 68262 | 10000 | 10000 | 221530 | 10000 | 10000 | |
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| bPt | 13234 | 4411 | 4411 | 68262 | 10000 | 10000 | 221530 | 10000 | 10000 | |
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| Pe_Pt | 7282 | 3385 | 3638 | 36896 | 10000 | 10000 | 221530 | 10000 | 10000 | |
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| Pt_sPe_Pt | 13234 | 5541 | 6293 | 68262 | 10000 | 10000 | 221530 | 10000 | 10000 | |
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| Pt_oPe_Pt | 13234 | 5480 | 6242 | 68262 | 10000 | 10000 | 221530 | 10000 | 10000 | |
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| t2i | 72826 | 5112 | 6631 | 368962 | 10000 | 10000 | 2215309 | 10000 | 10000 | |
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| t3i | 72826 | 3094 | 3296 | 368962 | 10000 | 10000 | 2215309 | 10000 | 10000 | |
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| e2i_N | 7282 | 2949 | 2975 | 36896 | 10000 | 10000 | 221530 | 10000 | 10000 | |
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| e3i_N | 7282 | 2913 | 2914 | 36896 | 10000 | 10000 | 221530 | 10000 | 10000 | |
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| Pe_e2i_Pe_NPe | 7282 | 2968 | 3012 | 36896 | 10000 | 10000 | 221530 | 10000 | 10000 | |
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| e2i_PeN | 7282 | 2971 | 3031 | 36896 | 10000 | 10000 | 221530 | 10000 | 10000 | |
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| e2i_NPe | 7282 | 3061 | 3192 | 36896 | 10000 | 10000 | 221530 | 10000 | 10000 | |
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| t2i_N | 7282 | 3135 | 3328 | 36896 | 10000 | 10000 | 221530 | 10000 | 10000 | |
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| t3i_N | 7282 | 2924 | 2944 | 36896 | 10000 | 10000 | 221530 | 10000 | 10000 | |
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| Pe_t2i_PtPe_NPt | 7282 | 3031 | 3127 | 36896 | 10000 | 10000 | 221530 | 10000 | 10000 | |
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| t2i_PtN | 7282 | 3300 | 3609 | 36896 | 10000 | 10000 | 221530 | 10000 | 10000 | |
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| t2i_NPt | 7282 | 4873 | 5464 | 36896 | 10000 | 10000 | 221530 | 10000 | 10000 | |
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| e2u | - | 2913 | 2913 | - | 10000 | 10000 | - | 10000 | 10000 | |
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| Pe_e2u | - | 2913 | 2913 | - | 10000 | 10000 | - | 10000 | 10000 | |
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| t2u | - | 2913 | 2913 | - | 10000 | 10000 | - | 10000 | 10000 | |
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| Pe_t2u | - | 2913 | 2913 | - | 10000 | 10000 | - | 10000 | 10000 | |
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| t2i_Pe | - | 2913 | 2913 | - | 10000 | 10000 | - | 10000 | 10000 | |
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| Pe_t2i | - | 2913 | 2913 | - | 10000 | 10000 | - | 10000 | 10000 | |
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| e2i_Pe | - | 2913 | 2913 | - | 10000 | 10000 | - | 10000 | 10000 | |
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| Pe_e2i | - | 2913 | 2913 | - | 10000 | 10000 | - | 10000 | 10000 | |
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| between | 7282 | 2913 | 2913 | 36896 | 10000 | 10000 | 221530 | 10000 | 10000 | |
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| Pe_aPt | 7282 | 4134 | 4733 | 68262 | 10000 | 10000 | 221530 | 10000 | 10000 | |
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| Pe_bPt | 7282 | 3970 | 4565 | 36896 | 10000 | 10000 | 221530 | 10000 | 10000 | |
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| Pt_sPe | 7282 | 4976 | 5608 | 36896 | 10000 | 10000 | 221530 | 10000 | 10000 | |
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| Pt_oPe | 7282 | 3321 | 3621 | 36896 | 10000 | 10000 | 221530 | 10000 | 10000 | |
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| Pt_se2i | 7282 | 3226 | 3466 | 36896 | 10000 | 10000 | 221530 | 10000 | 10000 | |
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| Pt_oe2i | 7282 | 3236 | 3485 | 36896 | 10000 | 10000 | 221530 | 10000 | 10000 | |
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| Pe_at2i | 7282 | 4607 | 5338 | 36896 | 10000 | 10000 | 221530 | 10000 | 10000 | |
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| Pe_bt2i | 7282 | 4583 | 5386 | 36896 | 10000 | 10000 | 221530 | 10000 | 10000 | |
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</details> |
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<details> |
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<summary>👈 🔎 Dataset statistics: avg_answers_count</summary> |
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| query | ICEWS14| | | ICEWS05_15| | | GDELT | | | |
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| :---- | :---- | :---- | :--- | :---- | :---- | :--- | :---- | :---- | :--- | |
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| | train | valid | test | train | valid | test | train | valid | test | |
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|Pe | 1.09 | 1.01 | 1.01 | 1.07 | 1.01 | 1.01 | 2.07 | 1.21 | 1.21| |
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|Pe2 | 1.03 | 2.19 | 2.23 | 1.02 | 2.15 | 2.19 | 2.61 | 6.51 | 6.13| |
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|Pe3 | 1.04 | 2.25 | 2.29 | 1.02 | 2.18 | 2.21 | 5.11 | 10.86 | 10.70| |
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|e2i | 1.02 | 2.76 | 2.84 | 1.01 | 2.36 | 2.52 | 1.05 | 2.30 | 2.32| |
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|e3i | 1.00 | 1.57 | 1.59 | 1.00 | 1.26 | 1.26 | 1.00 | 1.20 | 1.35| |
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|Pt | 1.71 | 1.22 | 1.21 | 2.58 | 1.61 | 1.60 | 3.36 | 1.66 | 1.66| |
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|aPt | 177.99 | 176.09 | 175.89 | 2022.16 | 2003.85 | 1998.71 | 156.48 | 155.38 | 153.41| |
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|bPt | 181.20 | 179.88 | 179.26 | 1929.98 | 1923.75 | 1919.83 | 160.38 | 159.29 | 157.42| |
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|Pe_Pt | 1.58 | 7.90 | 8.62 | 2.84 | 18.11 | 20.63 | 26.56 | 42.54 | 41.33| |
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|Pt_sPe_Pt | 1.79 | 7.26 | 7.47 | 2.49 | 13.51 | 10.86 | 4.92 | 14.13 | 12.80| |
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|Pt_oPe_Pt | 1.75 | 7.27 | 7.48 | 2.55 | 13.01 | 14.34 | 4.62 | 14.47 | 12.90| |
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|t2i | 1.19 | 6.29 | 6.38 | 3.07 | 29.45 | 25.61 | 1.97 | 8.98 | 7.76| |
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|t3i | 1.01 | 2.88 | 3.14 | 1.08 | 10.03 | 10.22 | 1.06 | 3.79 | 3.52| |
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|e2i_N | 1.02 | 2.10 | 2.14 | 1.01 | 2.05 | 2.08 | 2.04 | 4.66 | 4.58| |
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|e3i_N | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.02 | 1.19 | 1.37| |
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|Pe_e2i_Pe_NPe | 1.04 | 2.21 | 2.25 | 1.02 | 2.16 | 2.19 | 3.67 | 8.54 | 8.12| |
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|e2i_PeN | 1.04 | 2.22 | 2.26 | 1.02 | 2.17 | 2.21 | 3.67 | 8.66 | 8.36| |
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|e2i_NPe | 1.18 | 3.03 | 3.11 | 1.12 | 2.87 | 2.99 | 4.00 | 8.15 | 7.81| |
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|t2i_N | 1.15 | 3.31 | 3.44 | 1.21 | 4.06 | 4.20 | 2.91 | 8.78 | 7.56| |
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|t3i_N | 1.00 | 1.02 | 1.03 | 1.01 | 1.02 | 1.02 | 1.15 | 3.19 | 3.20| |
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|Pe_t2i_PtPe_NPt | 1.08 | 2.59 | 2.70 | 1.08 | 2.47 | 2.62 | 4.10 | 12.02 | 11.37| |
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|t2i_PtN | 1.41 | 5.22 | 5.47 | 1.70 | 8.10 | 8.11 | 4.56 | 12.56 | 11.32| |
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|t2i_NPt | 8.14 | 25.96 | 26.23 | 66.99 | 154.01 | 147.34 | 17.58 | 35.60 | 32.22| |
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|e2u | 0.00 | 3.12 | 3.17 | 0.00 | 2.38 | 2.40 | 0.00 | 5.04 | 5.41| |
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|Pe_e2u | 0.00 | 2.38 | 2.44 | 0.00 | 1.24 | 1.25 | 0.00 | 9.39 | 10.78| |
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|t2u | 0.00 | 4.35 | 4.53 | 0.00 | 5.57 | 5.92 | 0.00 | 9.70 | 10.51| |
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|Pe_t2u | 0.00 | 2.72 | 2.83 | 0.00 | 1.24 | 1.28 | 0.00 | 9.90 | 11.27| |
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|t2i_Pe | 0.00 | 1.03 | 1.03 | 0.00 | 1.01 | 1.02 | 0.00 | 1.34 | 1.44| |
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|Pe_t2i | 0.00 | 1.14 | 1.16 | 0.00 | 1.07 | 1.08 | 0.00 | 2.01 | 2.20| |
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|e2i_Pe | 0.00 | 1.00 | 1.00 | 0.00 | 1.00 | 1.00 | 0.00 | 1.07 | 1.10| |
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|Pe_e2i | 0.00 | 2.18 | 2.24 | 0.00 | 1.32 | 1.33 | 0.00 | 5.08 | 5.49| |
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|between | 122.61 | 120.94 | 120.27 | 1407.87 | 1410.39 | 1404.76 | 214.16 | 210.99 | 207.85| |
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|Pe_aPt | 4.67 | 16.73 | 16.50 | 18.68 | 43.80 | 46.23 | 49.31 | 66.21 | 68.88| |
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|Pe_bPt | 4.53 | 17.07 | 16.80 | 18.70 | 45.81 | 48.23 | 67.67 | 84.79 | 83.00| |
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|Pt_sPe | 8.65 | 28.86 | 29.22 | 71.51 | 162.36 | 155.46 | 27.55 | 45.83 | 43.73| |
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|Pt_oPe | 1.41 | 5.23 | 5.46 | 1.68 | 8.36 | 8.21 | 3.84 | 11.31 | 10.06| |
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|Pt_se2i | 1.31 | 5.72 | 6.19 | 1.37 | 9.00 | 9.30 | 2.76 | 8.72 | 7.66| |
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|Pt_oe2i | 1.32 | 6.51 | 7.00 | 1.44 | 10.49 | 10.89 | 2.55 | 8.17 | 7.27| |
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|Pe_at2i | 7.26 | 22.63 | 21.98 | 30.40 | 60.03 | 53.18 | 88.77 | 101.60 | 101.88| |
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|Pe_bt2i | 7.27 | 21.92 | 21.23 | 30.31 | 61.59 | 64.98 | 88.80 | 100.64 | 100.67| |
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</details> |
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<br/> |
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## ✉️ Contact |
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- Lin Xueyuan: linxy59@mail2.sysu.edu.cn |
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## 🤝 Citation |
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Please condiser citing this paper if you use the ```code``` or ```data``` from our work. Thanks a lot :) |
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(`Xueyuan et al., 2023` preferred, instead of `Lin et al., 2023`) |
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```bibtex |
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@inproceedings{ |
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xueyuan2023tflex, |
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title={TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge Graph}, |
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author={Lin Xueyuan and Haihong E and Chengjin Xu and Gengxian Zhou and Haoran Luo and Tianyi Hu and Fenglong Su and Ningyuan Li and Mingzhi Sun}, |
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booktitle={Thirty-seventh Conference on Neural Information Processing Systems}, |
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year={2023}, |
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url={https://openreview.net/forum?id=oaGdsgB18L} |
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} |
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``` |
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--- |
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TFLEX is released under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0) license. |
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<p align="right">(<a href="#top">back to top</a>)</p> |
