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ICEWS14.py
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# Copyright
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# Copyright 2023 Xueyuan Lin
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# Apache 2.0 License
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"""Loading script for DiffusionDB."""
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from typing import List, Dict
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import json
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import os
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from huggingface_hub import hf_hub_url
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import datasets
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_CITATION = """\
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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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_DESCRIPTION = """\
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TL;DR: The datasets for 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 widly 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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"""
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_HOMEPAGE = "https://github.com/LinXueyuanStdio/TFLEX"
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_LICENSE = "[Apache License 2.0](https://github.com/LinXueyuanStdio/TFLEX/blob/main/LICENSE)"
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query_name_to_args: Dict[str, List[str]] = {
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# 1. 1-hop Pe and Pt, manually
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"Pe": ["e1", "r1", "t1"],
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"Pt": ["e1", "r1", "e2"],
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# 2. entity multi-hop
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"Pe2": ["e1", "r1", "t1", "r2", "t2"],
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"Pe3": ["e1", "r1", "t1", "r2", "t2", "r3", "t3"],
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# 3. time multi-hop
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"aPt": ["s", "r", "o"],
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"bPt": ["s", "r", "o"],
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"Pt_sPe": ["e1", "r1", "t1", "r2", "e2"],
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"Pt_oPe": ["e1", "r1", "e2", "r2", "t1"],
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"Pe_Pt": ["e1", "r1", "e2", "r2", "e3"],
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"Pe_aPt": ["e1", "r1", "e2", "r2", "e3"],
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"Pe_bPt": ["e1", "r1", "e2", "r2", "e3"],
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"Pe_nPt": ["e1", "r1", "e2", "r2", "e3"],
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"Pt_sPe_Pt": ["s1", "r1", "s2", "r2", "o1", "r3", "o2"],
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"Pt_oPe_Pt": ["s1", "r1", "s2", "r2", "s3", "r3", "o1"],
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# 4. entity and & time and
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"e2i": ["e1", "r1", "t1", "e2", "r2", "t2"],
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"e3i": ["e1", "r1", "t1", "e2", "r2", "t2", "e3", "r3", "t3"],
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"t2i": ["e1", "r1", "e2", "e3", "r2", "e4"],
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"t3i": ["e1", "r1", "e2", "e3", "r2", "e4", "e5", "r3", "e6"],
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# 5. complex time and
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"e2i_Pe": ["e1", "r1", "t1", "r2", "t2", "e2", "r3", "t3"],
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"Pe_e2i": ["e1", "r1", "t1", "e2", "r2", "t2", "r3", "t3"],
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"Pt_se2i": ["e1", "r1", "t1", "e2", "r2", "t2", "r3", "e3"],
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"Pt_oe2i": ["e1", "r1", "e2", "r2", "t1", "e3", "r3", "t2"],
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"t2i_Pe": ["e1", "r1", "t1", "r2", "e2", "e3", "r3", "e4"],
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"Pe_t2i": ["e1", "r1", "e2", "r2", "e3", "e4", "r3", "e5"],
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"Pe_at2i": ["e1", "r1", "e2", "r2", "e3", "e4", "r3", "e5"],
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"Pe_bt2i": ["e1", "r1", "e2", "r2", "e3", "e4", "r3", "e5"],
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"Pe_nt2i": ["e1", "r1", "e2", "r2", "e3", "e4", "r3", "e5"],
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"between": ["e1", "r1", "e2", "e3", "r2", "e4"],
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# 5. entity not
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"e2i_N": ["e1", "r1", "t1", "e2", "r2", "t2"],
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"e3i_N": ["e1", "r1", "t1", "e2", "r2", "t2", "e3", "r3", "t3"],
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"Pe_e2i_Pe_NPe": ["e1", "r1", "t1", "e2", "r2", "t2", "r3", "t3"],
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"e2i_NPe": ["e1", "r1", "t1", "r2", "t2", "e2", "r3", "t3"],
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"e2i_PeN": ["e1", "r1", "t1", "r2", "t2", "e2", "r3", "t3"],
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# 6. time not
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"t2i_N": ["e1", "r1", "e2", "e3", "r2", "e4"],
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"t3i_N": ["e1", "r1", "e2", "e3", "r2", "e4", "e5", "r3", "e6"],
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"Pe_t2i_PtPe_NPt": ["e1", "r1", "e2", "r2", "t2", "r3", "e3", "e4", "r4", "e5"],
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"t2i_NPt": ["e1", "r1", "t1", "r2", "e2", "e3", "r3", "e4"],
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"t2i_PtN": ["e1", "r1", "t1", "r2", "e2", "e3", "r3", "e4"],
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# 7. entity union & time union
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"e2u": ["e1", "r1", "t1", "e2", "r2", "t2"],
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"Pe_e2u": ["e1", "r1", "t1", "e2", "r2", "t2", "r3", "t3"],
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"t2u": ["e1", "r1", "e2", "e3", "r2", "e4"],
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"Pe_t2u": ["e1", "r1", "e2", "r2", "e3", "e4", "r3", "e5"],
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}
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query_structures: Dict[str, str] = {
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# 1. 1-hop Pe and Pt, manually
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"Pe": "def Pe(e1, r1, t1): return Pe(e1, r1, t1)", # 1p
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"Pt": "def Pt(e1, r1, e2): return Pt(e1, r1, e2)", # 1p, temporal
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# 2. entity multi-hop
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"Pe2": "def Pe2(e1, r1, t1, r2, t2): return Pe(Pe(e1, r1, t1), r2, t2)", # 2p
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"Pe3": "def Pe3(e1, r1, t1, r2, t2, r3, t3): return Pe(Pe(Pe(e1, r1, t1), r2, t2), r3, t3)", # 3p
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# 3. time multi-hop
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"aPt": "def aPt(s, r, o): return after(Pt(s, r, o))", # a for after
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"bPt": "def bPt(s, r, o): return before(Pt(s, r, o))", # b for before
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"Pt_lPe": "def Pt_lPe(e1, r1, t1, r2, e2): return Pt(Pe(e1, r1, t1), r2, e2)", # l for left (as head entity)
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"Pt_rPe": "def Pt_rPe(e1, r1, e2, r2, t1): return Pt(e1, r1, Pe(e2, r2, t1))", # r for right (as tail entity)
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"Pt_sPe": "def Pt_sPe(e1, r1, t1, r2, e2): return Pt(Pe(e1, r1, t1), r2, e2)", # l for left (as head entity)
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"Pt_oPe": "def Pt_oPe(e1, r1, e2, r2, t1): return Pt(e1, r1, Pe(e2, r2, t1))", # r for right (as tail entity)
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"Pe_Pt": "def Pe_Pt(e1, r1, e2, r2, e3): return Pe(e1, r1, Pt(e2, r2, e3))", # at
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"Pe_aPt": "def Pe_aPt(e1, r1, e2, r2, e3): return Pe(e1, r1, after(Pt(e2, r2, e3)))", # a for after
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"Pe_bPt": "def Pe_bPt(e1, r1, e2, r2, e3): return Pe(e1, r1, before(Pt(e2, r2, e3)))", # b for before
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"Pe_nPt": "def Pe_nPt(e1, r1, e2, r2, e3): return Pe(e1, r1, next(Pt(e2, r2, e3)))", # n for next
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"Pt_sPe_Pt": "def Pt_sPe_Pt(s1, r1, s2, r2, o1, r3, o2): return Pt(Pe(s1, r1, Pt(s2, r2, o1)), r3, o2)",
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"Pt_oPe_Pt": "def Pt_oPe_Pt(s1, r1, s2, r2, s3, r3, o1): return Pt(s1, r1, Pe(s2, r2, Pt(s3, r3, o1)))",
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# 4. entity and & time and
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111 |
+
"e2i": "def e2i(e1, r1, t1, e2, r2, t2): return And(Pe(e1, r1, t1), Pe(e2, r2, t2))", # 2i
|
112 |
+
"e3i": "def e3i(e1, r1, t1, e2, r2, t2, e3, r3, t3): return And3(Pe(e1, r1, t1), Pe(e2, r2, t2), Pe(e3, r3, t3))", # 3i
|
113 |
+
"t2i": "def t2i(e1, r1, e2, e3, r2, e4): return TimeAnd(Pt(e1, r1, e2), Pt(e3, r2, e4))", # t-2i
|
114 |
+
"t3i": "def t3i(e1, r1, e2, e3, r2, e4, e5, r3, e6): return TimeAnd3(Pt(e1, r1, e2), Pt(e3, r2, e4), Pt(e5, r3, e6))", # t-3i
|
115 |
+
# 5. complex time and
|
116 |
+
"e2i_Pe": "def e2i_Pe(e1, r1, t1, r2, t2, e2, r3, t3): return And(Pe(Pe(e1, r1, t1), r2, t2), Pe(e2, r3, t3))", # pi
|
117 |
+
"Pe_e2i": "def Pe_e2i(e1, r1, t1, e2, r2, t2, r3, t3): return Pe(e2i(e1, r1, t1, e2, r2, t2), r3, t3)", # ip
|
118 |
+
"Pt_le2i": "def Pt_le2i(e1, r1, t1, e2, r2, t2, r3, e3): return Pt(e2i(e1, r1, t1, e2, r2, t2), r3, e3)", # mix ip
|
119 |
+
"Pt_re2i": "def Pt_re2i(e1, r1, e2, r2, t1, e3, r3, t2): return Pt(e1, r1, e2i(e2, r2, t1, e3, r3, t2))", # mix ip
|
120 |
+
"Pt_se2i": "def Pt_se2i(e1, r1, t1, e2, r2, t2, r3, e3): return Pt(e2i(e1, r1, t1, e2, r2, t2), r3, e3)", # mix ip
|
121 |
+
"Pt_oe2i": "def Pt_oe2i(e1, r1, e2, r2, t1, e3, r3, t2): return Pt(e1, r1, e2i(e2, r2, t1, e3, r3, t2))", # mix ip
|
122 |
+
"t2i_Pe": "def t2i_Pe(e1, r1, t1, r2, e2, e3, r3, e4): return TimeAnd(Pt(Pe(e1, r1, t1), r2, e2), Pt(e3, r3, e4))", # t-pi
|
123 |
+
"Pe_t2i": "def Pe_t2i(e1, r1, e2, r2, e3, e4, r3, e5): return Pe(e1, r1, t2i(e2, r2, e3, e4, r3, e5))", # t-ip
|
124 |
+
"Pe_at2i": "def Pe_at2i(e1, r1, e2, r2, e3, e4, r3, e5): return Pe(e1, r1, after(t2i(e2, r2, e3, e4, r3, e5)))",
|
125 |
+
"Pe_bt2i": "def Pe_bt2i(e1, r1, e2, r2, e3, e4, r3, e5): return Pe(e1, r1, before(t2i(e2, r2, e3, e4, r3, e5)))",
|
126 |
+
"Pe_nt2i": "def Pe_nt2i(e1, r1, e2, r2, e3, e4, r3, e5): return Pe(e1, r1, next(t2i(e2, r2, e3, e4, r3, e5)))",
|
127 |
+
"between": "def between(e1, r1, e2, e3, r2, e4): return TimeAnd(after(Pt(e1, r1, e2)), before(Pt(e3, r2, e4)))", # between(t1, t2) == after t1 and before t2
|
128 |
+
# 5. entity not
|
129 |
+
"e2i_N": "def e2i_N(e1, r1, t1, e2, r2, t2): return And(Pe(e1, r1, t1), Not(Pe(e2, r2, t2)))", # 2in
|
130 |
+
"e3i_N": "def e3i_N(e1, r1, t1, e2, r2, t2, e3, r3, t3): return And3(Pe(e1, r1, t1), Pe(e2, r2, t2), Not(Pe(e3, r3, t3)))", # 3in
|
131 |
+
"Pe_e2i_Pe_NPe": "def Pe_e2i_Pe_NPe(e1, r1, t1, e2, r2, t2, r3, t3): return Pe(And(Pe(e1, r1, t1), Not(Pe(e2, r2, t2))), r3, t3)", # inp
|
132 |
+
"e2i_PeN": "def e2i_PeN(e1, r1, t1, r2, t2, e2, r3, t3): return And(Pe(Pe(e1, r1, t1), r2, t2), Not(Pe(e2, r3, t3)))", # pin
|
133 |
+
"e2i_NPe": "def e2i_NPe(e1, r1, t1, r2, t2, e2, r3, t3): return And(Not(Pe(Pe(e1, r1, t1), r2, t2)), Pe(e2, r3, t3))", # pni = e2i_N(Pe(e1, r1, t1), r2, t2, e2, r3, t3)
|
134 |
+
# 6. time not
|
135 |
+
"t2i_N": "def t2i_N(e1, r1, e2, e3, r2, e4): return TimeAnd(Pt(e1, r1, e2), TimeNot(Pt(e3, r2, e4)))", # t-2in
|
136 |
+
"t3i_N": "def t3i_N(e1, r1, e2, e3, r2, e4, e5, r3, e6): return TimeAnd3(Pt(e1, r1, e2), Pt(e3, r2, e4), TimeNot(Pt(e5, r3, e6)))", # t-3in
|
137 |
+
"Pe_t2i_PtPe_NPt": "def Pe_t2i_PtPe_NPt(e1, r1, e2, r2, t2, r3, e3, e4, r4, e5): return Pe(e1, r1, TimeAnd(Pt(Pe(e2, r2, t2), r3, e3), TimeNot(Pt(e4, r4, e5))))", # t-inp
|
138 |
+
"t2i_PtN": "def t2i_PtN(e1, r1, t1, r2, e2, e3, r3, e4): return TimeAnd(Pt(Pe(e1, r1, t1), r2, e2), TimeNot(Pt(e3, r3, e4)))", # t-pin
|
139 |
+
"t2i_NPt": "def t2i_NPt(e1, r1, t1, r2, e2, e3, r3, e4): return TimeAnd(TimeNot(Pt(Pe(e1, r1, t1), r2, e2)), Pt(e3, r3, e4))", # t-pni
|
140 |
+
# 7. entity union & time union
|
141 |
+
"e2u": "def e2u(e1, r1, t1, e2, r2, t2): return Or(Pe(e1, r1, t1), Pe(e2, r2, t2))", # 2u
|
142 |
+
"Pe_e2u": "def Pe_e2u(e1, r1, t1, e2, r2, t2, r3, t3): return Pe(Or(Pe(e1, r1, t1), Pe(e2, r2, t2)), r3, t3)", # up
|
143 |
+
"t2u": "def t2u(e1, r1, e2, e3, r2, e4): return TimeOr(Pt(e1, r1, e2), Pt(e3, r2, e4))", # t-2u
|
144 |
+
"Pe_t2u": "def Pe_t2u(e1, r1, e2, r2, e3, e4, r3, e5): return Pe(e1, r1, TimeOr(Pt(e2, r2, e3), Pt(e4, r3, e5)))", # t-up
|
145 |
+
# 8. union-DM
|
146 |
+
"e2u_DM": "def e2u_DM(e1, r1, t1, e2, r2, t2): return Not(And(Not(Pe(e1, r1, t1)), Not(Pe(e2, r2, t2))))", # 2u-DM
|
147 |
+
"Pe_e2u_DM": "def Pe_e2u_DM(e1, r1, t1, e2, r2, t2, r3, t3): return Pe(Not(And(Not(Pe(e1, r1, t1)), Not(Pe(e2, r2, t2)))), r3, t3)", # up-DM
|
148 |
+
"t2u_DM": "def t2u_DM(e1, r1, e2, e3, r2, e4): return TimeNot(TimeAnd(TimeNot(Pt(e1, r1, e2)), TimeNot(Pt(e3, r2, e4))))", # t-2u-DM
|
149 |
+
"Pe_t2u_DM": "def Pe_t2u_DM(e1, r1, e2, r2, e3, e4, r3, e5): return Pe(e1, r1, TimeNot(TimeAnd(TimeNot(Pt(e2, r2, e3)), TimeNot(Pt(e4, r3, e5)))))", # t-up-DM
|
150 |
+
# 9. union-DNF
|
151 |
+
"e2u_DNF": "def e2u_DNF(e1, r1, t1, e2, r2, t2): return Pe(e1, r1, t1), Pe(e2, r2, t2)", # 2u_DNF
|
152 |
+
"Pe_e2u_DNF": "def Pe_e2u_DNF(e1, r1, t1, e2, r2, t2, r3, t3): return Pe(Pe(e1, r1, t1), r3, t3), Pe(Pe(e2, r2, t2), r3, t3)", # up_DNF
|
153 |
+
"t2u_DNF": "def t2u_DNF(e1, r1, e2, e3, r2, e4): return Pt(e1, r1, e2), Pt(e3, r2, e4)", # t-2u_DNF
|
154 |
+
"Pe_t2u_DNF": "def Pe_t2u_DNF(e1, r1, e2, r2, e3, e4, r3, e5): return Pe(e1, r1, Pt(e2, r2, e3)), Pe(e1, r1, Pt(e4, r3, e5))", # t-up_DNF
|
155 |
+
}
|
156 |
+
union_query_structures: List[str] = [
|
157 |
+
"e2u",
|
158 |
+
"Pe_e2u", # 2u, up
|
159 |
+
"t2u",
|
160 |
+
"Pe_t2u", # t-2u, t-up
|
161 |
+
]
|
162 |
+
train_query_structures: List[str] = [
|
163 |
+
# entity
|
164 |
+
"Pe",
|
165 |
+
"Pe2",
|
166 |
+
"Pe3",
|
167 |
+
"e2i",
|
168 |
+
"e3i", # 1p, 2p, 3p, 2i, 3i
|
169 |
+
"e2i_NPe",
|
170 |
+
"e2i_PeN",
|
171 |
+
"Pe_e2i_Pe_NPe",
|
172 |
+
"e2i_N",
|
173 |
+
"e3i_N", # npi, pni, inp, 2in, 3in
|
174 |
+
# time
|
175 |
+
"Pt",
|
176 |
+
"Pt_lPe",
|
177 |
+
"Pt_rPe",
|
178 |
+
"Pe_Pt",
|
179 |
+
"Pe_aPt",
|
180 |
+
"Pe_bPt",
|
181 |
+
"Pe_nPt", # t-1p, t-2p
|
182 |
+
"t2i",
|
183 |
+
"t3i",
|
184 |
+
"Pt_le2i",
|
185 |
+
"Pt_re2i",
|
186 |
+
"Pe_t2i",
|
187 |
+
"Pe_at2i",
|
188 |
+
"Pe_bt2i",
|
189 |
+
"Pe_nt2i",
|
190 |
+
"between", # t-2i, t-3i
|
191 |
+
"t2i_NPt",
|
192 |
+
"t2i_PtN",
|
193 |
+
"Pe_t2i_PtPe_NPt",
|
194 |
+
"t2i_N",
|
195 |
+
"t3i_N", # t-npi, t-pni, t-inp, t-2in, t-3in
|
196 |
+
]
|
197 |
+
test_query_structures: List[str] = train_query_structures + [
|
198 |
+
# entity
|
199 |
+
"e2i_Pe",
|
200 |
+
"Pe_e2i", # pi, ip
|
201 |
+
"e2u",
|
202 |
+
"Pe_e2u", # 2u, up
|
203 |
+
# time
|
204 |
+
"t2i_Pe",
|
205 |
+
"Pe_t2i", # t-pi, t-ip
|
206 |
+
"t2u",
|
207 |
+
"Pe_t2u", # t-2u, t-up
|
208 |
+
# union-DM
|
209 |
+
"e2u_DM",
|
210 |
+
"Pe_e2u_DM", # 2u-DM, up-DM
|
211 |
+
"t2u_DM",
|
212 |
+
"Pe_t2u_DM", # t-2u-DM, t-up-DM
|
213 |
+
]
|
214 |
+
|
215 |
+
|
216 |
+
_AUTHOR = "linxy"
|
217 |
+
_DATASET = "ICEWS14"
|
218 |
+
_URLS = {
|
219 |
+
name: hf_hub_url(f"{_AUTHOR}/{_DATASET}", filename=f"zips/{name}.zip", repo_type="dataset")
|
220 |
+
for name in ["all"] + list(query_name_to_args.keys())
|
221 |
+
} | {
|
222 |
+
"meta": hf_hub_url(f"{_AUTHOR}/{_DATASET}", filename="meta.json", repo_type="dataset")
|
223 |
+
}
|
224 |
+
|
225 |
+
|
226 |
+
class ICEWS14Dataset(datasets.GeneratorBasedBuilder):
|
227 |
+
VERSION = datasets.Version("1.0.0")
|
228 |
+
|
229 |
+
STANDARD_BUILDER_CONFIGS = [
|
230 |
+
datasets.BuilderConfig(
|
231 |
+
name=query_name,
|
232 |
+
version=datasets.Version("1.0.0"),
|
233 |
+
description=query_structures[query_name],
|
234 |
+
)
|
235 |
+
for query_name in list(query_name_to_args.keys())
|
236 |
+
]
|
237 |
+
BUILDER_CONFIGS = [
|
238 |
+
datasets.BuilderConfig(
|
239 |
+
name="meta",
|
240 |
+
version=VERSION,
|
241 |
+
description=f"The meta of data, including entity/relation/timestamp count, entity2idx, relation2idx, timestamp2idx, etc.",
|
242 |
+
),
|
243 |
+
datasets.BuilderConfig(
|
244 |
+
name="all",
|
245 |
+
version=VERSION,
|
246 |
+
description=f"All types of queries. Train: {train_query_structures}, Valid | Test: {test_query_structures}",
|
247 |
+
),
|
248 |
+
] + STANDARD_BUILDER_CONFIGS
|
249 |
+
|
250 |
+
DEFAULT_CONFIG_NAME = "all" # It's not mandatory to have a default configuration. Just use one if it make sense.
|
251 |
+
|
252 |
+
def _info(self):
|
253 |
+
if self.config.name == "meta":
|
254 |
+
features = datasets.Features(
|
255 |
+
{
|
256 |
+
"dataset": datasets.Value("string"),
|
257 |
+
"entity_count": datasets.Value("int32"),
|
258 |
+
"relation_count": datasets.Value("int32"),
|
259 |
+
"timestamp_count": datasets.Value("int32"),
|
260 |
+
"valid_triples_count": datasets.Value("int32"),
|
261 |
+
"test_triples_count": datasets.Value("int32"),
|
262 |
+
"train_triples_count": datasets.Value("int32"),
|
263 |
+
"triple_count": datasets.Value("int32"),
|
264 |
+
"query_meta": datasets.Sequence(
|
265 |
+
feature={
|
266 |
+
"query_name": datasets.Value("string"),
|
267 |
+
"queries_count": datasets.Value("int32"),
|
268 |
+
"avg_answers_count": datasets.Value("float"),
|
269 |
+
"train": {
|
270 |
+
"queries_count": datasets.Value("int32"),
|
271 |
+
"avg_answers_count": datasets.Value("float"),
|
272 |
+
},
|
273 |
+
"valid": {
|
274 |
+
"queries_count": datasets.Value("int32"),
|
275 |
+
"avg_answers_count": datasets.Value("float"),
|
276 |
+
},
|
277 |
+
"test": {
|
278 |
+
"queries_count": datasets.Value("int32"),
|
279 |
+
"avg_answers_count": datasets.Value("float"),
|
280 |
+
},
|
281 |
+
}
|
282 |
+
),
|
283 |
+
"entity2idx": datasets.Sequence(
|
284 |
+
feature={
|
285 |
+
"name": datasets.Value("string"),
|
286 |
+
"id": datasets.Value("int32"),
|
287 |
+
}
|
288 |
+
),
|
289 |
+
"relation2idx": datasets.Sequence(
|
290 |
+
feature={
|
291 |
+
"name": datasets.Value("string"),
|
292 |
+
"id": datasets.Value("int32"),
|
293 |
+
}
|
294 |
+
),
|
295 |
+
"timestamp2idx": datasets.Sequence(
|
296 |
+
feature={
|
297 |
+
"name": datasets.Value("string"),
|
298 |
+
"id": datasets.Value("int32"),
|
299 |
+
}
|
300 |
+
),
|
301 |
+
}
|
302 |
+
)
|
303 |
+
else:
|
304 |
+
features = datasets.Features(
|
305 |
+
{
|
306 |
+
"query_name": datasets.Value("string"),
|
307 |
+
"definition": datasets.Value("string"),
|
308 |
+
"query": datasets.Sequence(feature=datasets.Value("int32")),
|
309 |
+
"answer": datasets.Sequence(feature=datasets.Value("int32")),
|
310 |
+
"easy_answer": datasets.Sequence(feature=datasets.Value("int32")),
|
311 |
+
"args": datasets.Sequence(feature=datasets.Value("string")),
|
312 |
+
}
|
313 |
+
)
|
314 |
+
return datasets.DatasetInfo(
|
315 |
+
description=_DESCRIPTION,
|
316 |
+
features=features,
|
317 |
+
homepage=_HOMEPAGE,
|
318 |
+
license=_LICENSE,
|
319 |
+
citation=_CITATION,
|
320 |
+
)
|
321 |
+
|
322 |
+
def _split_generators(self, dl_manager: datasets.download.DownloadManager):
|
323 |
+
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
|
324 |
+
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
|
325 |
+
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
|
326 |
+
url = _URLS[self.config.name]
|
327 |
+
if self.config.name == "meta":
|
328 |
+
data_file = dl_manager.download(_URLS["meta"])
|
329 |
+
return [
|
330 |
+
datasets.SplitGenerator(
|
331 |
+
name=datasets.Split.TRAIN,
|
332 |
+
# These kwargs will be passed to _generate_examples
|
333 |
+
gen_kwargs={
|
334 |
+
"filepath": data_file,
|
335 |
+
"split": "meta",
|
336 |
+
},
|
337 |
+
)
|
338 |
+
]
|
339 |
+
data_dir = dl_manager.download_and_extract(url)
|
340 |
+
return [
|
341 |
+
datasets.SplitGenerator(
|
342 |
+
name=datasets.Split.TRAIN,
|
343 |
+
# These kwargs will be passed to _generate_examples
|
344 |
+
gen_kwargs={
|
345 |
+
"filepath": os.path.join(data_dir, "train.jsonl"),
|
346 |
+
"split": "train",
|
347 |
+
},
|
348 |
+
),
|
349 |
+
datasets.SplitGenerator(
|
350 |
+
name=datasets.Split.VALIDATION,
|
351 |
+
# These kwargs will be passed to _generate_examples
|
352 |
+
gen_kwargs={
|
353 |
+
"filepath": os.path.join(data_dir, "valid.jsonl"),
|
354 |
+
"split": "valid",
|
355 |
+
},
|
356 |
+
),
|
357 |
+
datasets.SplitGenerator(
|
358 |
+
name=datasets.Split.TEST,
|
359 |
+
# These kwargs will be passed to _generate_examples
|
360 |
+
gen_kwargs={
|
361 |
+
"filepath": os.path.join(data_dir, "test.jsonl"),
|
362 |
+
"split": "test",
|
363 |
+
},
|
364 |
+
),
|
365 |
+
]
|
366 |
+
|
367 |
+
def _generate_examples(self, filepath, split):
|
368 |
+
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
|
369 |
+
# This method yields (key, example) tuples from the dataset.
|
370 |
+
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
|
371 |
+
if not os.path.exists(filepath):
|
372 |
+
return
|
373 |
+
if split == "meta":
|
374 |
+
with open(filepath, "r", encoding="utf-8") as f:
|
375 |
+
data = json.load(f)
|
376 |
+
yield 0, data
|
377 |
+
return
|
378 |
+
with open(filepath, "r", encoding="utf-8") as f:
|
379 |
+
for key, row in enumerate(f):
|
380 |
+
data = json.loads(row)
|
381 |
+
query_name = data["query_name"]
|
382 |
+
easy_answer = data["easy_answer"] if "easy_answer" in data else []
|
383 |
+
yield key, {
|
384 |
+
"query_name": query_name,
|
385 |
+
"query": data["query"],
|
386 |
+
"answer": data["answer"],
|
387 |
+
"easy_answer": easy_answer,
|
388 |
+
"args": query_name_to_args[query_name],
|
389 |
+
"definition": query_structures[query_name],
|
390 |
+
}
|