Datasets:

linxy
/

GDELT

	# Copyright 2023 Xueyuan Lin
	# Apache 2.0 License
	"""Loading script for DiffusionDB."""
	from typing import List, Dict
	import json
	import os
	from huggingface_hub import hf_hub_url
	import datasets


	_CITATION = """\
	@inproceedings{
	xueyuan2023tflex,
	title={TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge Graph},
	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},
	booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
	year={2023},
	url={https://openreview.net/forum?id=oaGdsgB18L}
	}\
	"""

	_DESCRIPTION = """\
	TL;DR: The datasets for temporal knowledge graph reasoning task.

	[[Github]](https://github.com/LinXueyuanStdio/TFLEX)
	[[OpenReview]](https://openreview.net/forum?id=oaGdsgB18L)
	[[arXiv]](https://arxiv.org/abs/2205.14307)

	- Built over ICEWS and GDELT, which are widly used benchmarks in TKGC.
	- First introduced in paper "TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge Graph"
	- Please refer to the original paper for more details.
	"""

	_HOMEPAGE = "https://github.com/LinXueyuanStdio/TFLEX"

	_LICENSE = "[Apache License 2.0](https://github.com/LinXueyuanStdio/TFLEX/blob/main/LICENSE)"

	query_name_to_args: Dict[str, List[str]] = {
	# 1. 1-hop Pe and Pt, manually
	"Pe": ["e1", "r1", "t1"],
	"Pt": ["e1", "r1", "e2"],
	# 2. entity multi-hop
	"Pe2": ["e1", "r1", "t1", "r2", "t2"],
	"Pe3": ["e1", "r1", "t1", "r2", "t2", "r3", "t3"],
	# 3. time multi-hop
	"aPt": ["s", "r", "o"],
	"bPt": ["s", "r", "o"],
	"Pt_sPe": ["e1", "r1", "t1", "r2", "e2"],
	"Pt_oPe": ["e1", "r1", "e2", "r2", "t1"],
	"Pe_Pt": ["e1", "r1", "e2", "r2", "e3"],
	"Pe_aPt": ["e1", "r1", "e2", "r2", "e3"],
	"Pe_bPt": ["e1", "r1", "e2", "r2", "e3"],
	"Pe_nPt": ["e1", "r1", "e2", "r2", "e3"],
	"Pt_sPe_Pt": ["s1", "r1", "s2", "r2", "o1", "r3", "o2"],
	"Pt_oPe_Pt": ["s1", "r1", "s2", "r2", "s3", "r3", "o1"],
	# 4. entity and & time and
	"e2i": ["e1", "r1", "t1", "e2", "r2", "t2"],
	"e3i": ["e1", "r1", "t1", "e2", "r2", "t2", "e3", "r3", "t3"],
	"t2i": ["e1", "r1", "e2", "e3", "r2", "e4"],
	"t3i": ["e1", "r1", "e2", "e3", "r2", "e4", "e5", "r3", "e6"],
	# 5. complex time and
	"e2i_Pe": ["e1", "r1", "t1", "r2", "t2", "e2", "r3", "t3"],
	"Pe_e2i": ["e1", "r1", "t1", "e2", "r2", "t2", "r3", "t3"],
	"Pt_se2i": ["e1", "r1", "t1", "e2", "r2", "t2", "r3", "e3"],
	"Pt_oe2i": ["e1", "r1", "e2", "r2", "t1", "e3", "r3", "t2"],
	"t2i_Pe": ["e1", "r1", "t1", "r2", "e2", "e3", "r3", "e4"],
	"Pe_t2i": ["e1", "r1", "e2", "r2", "e3", "e4", "r3", "e5"],
	"Pe_at2i": ["e1", "r1", "e2", "r2", "e3", "e4", "r3", "e5"],
	"Pe_bt2i": ["e1", "r1", "e2", "r2", "e3", "e4", "r3", "e5"],
	"Pe_nt2i": ["e1", "r1", "e2", "r2", "e3", "e4", "r3", "e5"],
	"between": ["e1", "r1", "e2", "e3", "r2", "e4"],
	# 5. entity not
	"e2i_N": ["e1", "r1", "t1", "e2", "r2", "t2"],
	"e3i_N": ["e1", "r1", "t1", "e2", "r2", "t2", "e3", "r3", "t3"],
	"Pe_e2i_Pe_NPe": ["e1", "r1", "t1", "e2", "r2", "t2", "r3", "t3"],
	"e2i_NPe": ["e1", "r1", "t1", "r2", "t2", "e2", "r3", "t3"],
	"e2i_PeN": ["e1", "r1", "t1", "r2", "t2", "e2", "r3", "t3"],
	# 6. time not
	"t2i_N": ["e1", "r1", "e2", "e3", "r2", "e4"],
	"t3i_N": ["e1", "r1", "e2", "e3", "r2", "e4", "e5", "r3", "e6"],
	"Pe_t2i_PtPe_NPt": ["e1", "r1", "e2", "r2", "t2", "r3", "e3", "e4", "r4", "e5"],
	"t2i_NPt": ["e1", "r1", "t1", "r2", "e2", "e3", "r3", "e4"],
	"t2i_PtN": ["e1", "r1", "t1", "r2", "e2", "e3", "r3", "e4"],
	# 7. entity union & time union
	"e2u": ["e1", "r1", "t1", "e2", "r2", "t2"],
	"Pe_e2u": ["e1", "r1", "t1", "e2", "r2", "t2", "r3", "t3"],
	"t2u": ["e1", "r1", "e2", "e3", "r2", "e4"],
	"Pe_t2u": ["e1", "r1", "e2", "r2", "e3", "e4", "r3", "e5"],
	}
	query_structures: Dict[str, str] = {
	# 1. 1-hop Pe and Pt, manually
	"Pe": "def Pe(e1, r1, t1): return Pe(e1, r1, t1)", # 1p
	"Pt": "def Pt(e1, r1, e2): return Pt(e1, r1, e2)", # 1p, temporal
	# 2. entity multi-hop
	"Pe2": "def Pe2(e1, r1, t1, r2, t2): return Pe(Pe(e1, r1, t1), r2, t2)", # 2p
	"Pe3": "def Pe3(e1, r1, t1, r2, t2, r3, t3): return Pe(Pe(Pe(e1, r1, t1), r2, t2), r3, t3)", # 3p
	# 3. time multi-hop
	"aPt": "def aPt(s, r, o): return after(Pt(s, r, o))", # a for after
	"bPt": "def bPt(s, r, o): return before(Pt(s, r, o))", # b for before
	"Pt_lPe": "def Pt_lPe(e1, r1, t1, r2, e2): return Pt(Pe(e1, r1, t1), r2, e2)", # l for left (as head entity)
	"Pt_rPe": "def Pt_rPe(e1, r1, e2, r2, t1): return Pt(e1, r1, Pe(e2, r2, t1))", # r for right (as tail entity)
	"Pt_sPe": "def Pt_sPe(e1, r1, t1, r2, e2): return Pt(Pe(e1, r1, t1), r2, e2)", # l for left (as head entity)
	"Pt_oPe": "def Pt_oPe(e1, r1, e2, r2, t1): return Pt(e1, r1, Pe(e2, r2, t1))", # r for right (as tail entity)
	"Pe_Pt": "def Pe_Pt(e1, r1, e2, r2, e3): return Pe(e1, r1, Pt(e2, r2, e3))", # at
	"Pe_aPt": "def Pe_aPt(e1, r1, e2, r2, e3): return Pe(e1, r1, after(Pt(e2, r2, e3)))", # a for after
	"Pe_bPt": "def Pe_bPt(e1, r1, e2, r2, e3): return Pe(e1, r1, before(Pt(e2, r2, e3)))", # b for before
	"Pe_nPt": "def Pe_nPt(e1, r1, e2, r2, e3): return Pe(e1, r1, next(Pt(e2, r2, e3)))", # n for next
	"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)",
	"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)))",
	# 4. entity and & time and
	"e2i": "def e2i(e1, r1, t1, e2, r2, t2): return And(Pe(e1, r1, t1), Pe(e2, r2, t2))", # 2i
	"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
	"t2i": "def t2i(e1, r1, e2, e3, r2, e4): return TimeAnd(Pt(e1, r1, e2), Pt(e3, r2, e4))", # t-2i
	"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
	# 5. complex time and
	"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
	"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
	"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
	"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
	"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
	"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
	"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
	"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
	"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)))",
	"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)))",
	"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)))",
	"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
	# 5. entity not
	"e2i_N": "def e2i_N(e1, r1, t1, e2, r2, t2): return And(Pe(e1, r1, t1), Not(Pe(e2, r2, t2)))", # 2in
	"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
	"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
	"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
	"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)
	# 6. time not
	"t2i_N": "def t2i_N(e1, r1, e2, e3, r2, e4): return TimeAnd(Pt(e1, r1, e2), TimeNot(Pt(e3, r2, e4)))", # t-2in
	"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
	"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
	"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
	"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
	# 7. entity union & time union
	"e2u": "def e2u(e1, r1, t1, e2, r2, t2): return Or(Pe(e1, r1, t1), Pe(e2, r2, t2))", # 2u
	"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
	"t2u": "def t2u(e1, r1, e2, e3, r2, e4): return TimeOr(Pt(e1, r1, e2), Pt(e3, r2, e4))", # t-2u
	"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
	# 8. union-DM
	"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
	"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
	"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
	"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
	# 9. union-DNF
	"e2u_DNF": "def e2u_DNF(e1, r1, t1, e2, r2, t2): return Pe(e1, r1, t1), Pe(e2, r2, t2)", # 2u_DNF
	"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
	"t2u_DNF": "def t2u_DNF(e1, r1, e2, e3, r2, e4): return Pt(e1, r1, e2), Pt(e3, r2, e4)", # t-2u_DNF
	"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
	}
	union_query_structures: List[str] = [
	"e2u",
	"Pe_e2u", # 2u, up
	"t2u",
	"Pe_t2u", # t-2u, t-up
	]
	train_query_structures: List[str] = [
	# entity
	"Pe",
	"Pe2",
	"Pe3",
	"e2i",
	"e3i", # 1p, 2p, 3p, 2i, 3i
	"e2i_NPe",
	"e2i_PeN",
	"Pe_e2i_Pe_NPe",
	"e2i_N",
	"e3i_N", # npi, pni, inp, 2in, 3in
	# time
	"Pt",
	"Pt_lPe",
	"Pt_rPe",
	"Pe_Pt",
	"Pe_aPt",
	"Pe_bPt",
	"Pe_nPt", # t-1p, t-2p
	"t2i",
	"t3i",
	"Pt_le2i",
	"Pt_re2i",
	"Pe_t2i",
	"Pe_at2i",
	"Pe_bt2i",
	"Pe_nt2i",
	"between", # t-2i, t-3i
	"t2i_NPt",
	"t2i_PtN",
	"Pe_t2i_PtPe_NPt",
	"t2i_N",
	"t3i_N", # t-npi, t-pni, t-inp, t-2in, t-3in
	]
	test_query_structures: List[str] = train_query_structures + [
	# entity
	"e2i_Pe",
	"Pe_e2i", # pi, ip
	"e2u",
	"Pe_e2u", # 2u, up
	# time
	"t2i_Pe",
	"Pe_t2i", # t-pi, t-ip
	"t2u",
	"Pe_t2u", # t-2u, t-up
	# union-DM
	"e2u_DM",
	"Pe_e2u_DM", # 2u-DM, up-DM
	"t2u_DM",
	"Pe_t2u_DM", # t-2u-DM, t-up-DM
	]


	_AUTHOR = "linxy"
	_DATASET = "GDELT"
	_URLS = {
	name: hf_hub_url(f"{_AUTHOR}/{_DATASET}", filename=f"zips/{name}.zip", repo_type="dataset")
	for name in ["all"] + list(query_name_to_args.keys())
	} \| {
	"meta": hf_hub_url(f"{_AUTHOR}/{_DATASET}", filename="meta.json", repo_type="dataset")
	}


	class GDELTDataset(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("1.0.0")

	STANDARD_BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name=query_name,
	version=datasets.Version("1.0.0"),
	description=query_structures[query_name],
	)
	for query_name in list(query_name_to_args.keys())
	]
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="meta",
	version=VERSION,
	description=f"The meta of data, including entity/relation/timestamp count, entity2idx, relation2idx, timestamp2idx, etc.",
	),
	datasets.BuilderConfig(
	name="all",
	version=VERSION,
	description=f"All types of queries. Train: {train_query_structures}, Valid \| Test: {test_query_structures}",
	),
	] + STANDARD_BUILDER_CONFIGS

	DEFAULT_CONFIG_NAME = "all" # It's not mandatory to have a default configuration. Just use one if it make sense.

	def _info(self):
	if self.config.name == "meta":
	features = datasets.Features(
	{
	"dataset": datasets.Value("string"),
	"entity_count": datasets.Value("int32"),
	"relation_count": datasets.Value("int32"),
	"timestamp_count": datasets.Value("int32"),
	"valid_triples_count": datasets.Value("int32"),
	"test_triples_count": datasets.Value("int32"),
	"train_triples_count": datasets.Value("int32"),
	"triple_count": datasets.Value("int32"),
	"query_meta": datasets.Sequence(
	feature={
	"query_name": datasets.Value("string"),
	"queries_count": datasets.Value("int32"),
	"avg_answers_count": datasets.Value("float"),
	"train": {
	"queries_count": datasets.Value("int32"),
	"avg_answers_count": datasets.Value("float"),
	},
	"valid": {
	"queries_count": datasets.Value("int32"),
	"avg_answers_count": datasets.Value("float"),
	},
	"test": {
	"queries_count": datasets.Value("int32"),
	"avg_answers_count": datasets.Value("float"),
	},
	}
	),
	"entity2idx": datasets.Sequence(
	feature={
	"name": datasets.Value("string"),
	"id": datasets.Value("int32"),
	}
	),
	"relation2idx": datasets.Sequence(
	feature={
	"name": datasets.Value("string"),
	"id": datasets.Value("int32"),
	}
	),
	"timestamp2idx": datasets.Sequence(
	feature={
	"name": datasets.Value("string"),
	"id": datasets.Value("int32"),
	}
	),
	}
	)
	else:
	features = datasets.Features(
	{
	"query_name": datasets.Value("string"),
	"definition": datasets.Value("string"),
	"query": datasets.Sequence(feature=datasets.Value("int32")),
	"answer": datasets.Sequence(feature=datasets.Value("int32")),
	"easy_answer": datasets.Sequence(feature=datasets.Value("int32")),
	"args": datasets.Sequence(feature=datasets.Value("string")),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager: datasets.download.DownloadManager):
	# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
	# 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.
	# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
	url = _URLS[self.config.name]
	if self.config.name == "meta":
	data_file = dl_manager.download(_URLS["meta"])
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": data_file,
	"split": "meta",
	},
	)
	]
	data_dir = dl_manager.download_and_extract(url)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, "train.jsonl"),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, "valid.jsonl"),
	"split": "valid",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, "test.jsonl"),
	"split": "test",
	},
	),
	]

	def _generate_examples(self, filepath, split):
	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	# This method yields (key, example) tuples from the dataset.
	# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
	if not os.path.exists(filepath):
	return
	if split == "meta":
	with open(filepath, "r", encoding="utf-8") as f:
	data = json.load(f)
	yield 0, data
	return
	with open(filepath, "r", encoding="utf-8") as f:
	for key, row in enumerate(f):
	data = json.loads(row)
	query_name = data["query_name"]
	easy_answer = data["easy_answer"] if "easy_answer" in data else []
	yield key, {
	"query_name": query_name,
	"query": data["query"],
	"answer": data["answer"],
	"easy_answer": easy_answer,
	"args": query_name_to_args[query_name],
	"definition": query_structures[query_name],
	}