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# coding=utf-8
# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
BioRelEx is a biological relation extraction dataset. Version 1.0 contains 2010
annotated sentences that describe binding interactions between various
biological entities (proteins, chemicals, etc.). 1405 sentences are for
training, another 201 sentences are for validation. They are publicly available
at https://github.com/YerevaNN/BioRelEx/releases. Another 404 sentences are for
testing which are kept private for at this Codalab competition
https://competitions.codalab.org/competitions/20468. All sentences contain words
"bind", "bound" or "binding". For every sentence we provide: 1) Complete
annotations of all biological entities that appear in the sentence 2) Entity
types (32 types) and grounding information for most of the proteins and families
(links to uniprot, interpro and other databases) 3) Coreference between entities
in the same sentence (e.g. abbreviations and synonyms) 4) Binding interactions
between the annotated entities 5) Binding interaction types: positive, negative
(A does not bind B) and neutral (A may bind to B)
"""
import itertools as it
import json
from collections import defaultdict
from typing import Dict, List, Tuple
import datasets
from .bigbiohub import kb_features
from .bigbiohub import BigBioConfig
from .bigbiohub import Tasks
# TODO: Add BibTeX citation
_LANGUAGES = ['English']
_PUBMED = True
_LOCAL = False
_CITATION = """\
@inproceedings{khachatrian2019biorelex,
title = "{B}io{R}el{E}x 1.0: Biological Relation Extraction Benchmark",
author = "Khachatrian, Hrant and
Nersisyan, Lilit and
Hambardzumyan, Karen and
Galstyan, Tigran and
Hakobyan, Anna and
Arakelyan, Arsen and
Rzhetsky, Andrey and
Galstyan, Aram",
booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5019",
doi = "10.18653/v1/W19-5019",
pages = "176--190"
}
"""
_DATASETNAME = "biorelex"
_DISPLAYNAME = "BioRelEx"
_DESCRIPTION = """\
BioRelEx is a biological relation extraction dataset. Version 1.0 contains 2010
annotated sentences that describe binding interactions between various
biological entities (proteins, chemicals, etc.). 1405 sentences are for
training, another 201 sentences are for validation. They are publicly available
at https://github.com/YerevaNN/BioRelEx/releases. Another 404 sentences are for
testing which are kept private for at this Codalab competition
https://competitions.codalab.org/competitions/20468. All sentences contain words
"bind", "bound" or "binding". For every sentence we provide: 1) Complete
annotations of all biological entities that appear in the sentence 2) Entity
types (32 types) and grounding information for most of the proteins and families
(links to uniprot, interpro and other databases) 3) Coreference between entities
in the same sentence (e.g. abbreviations and synonyms) 4) Binding interactions
between the annotated entities 5) Binding interaction types: positive, negative
(A does not bind B) and neutral (A may bind to B)"""
_HOMEPAGE = "https://github.com/YerevaNN/BioRelEx"
_LICENSE = 'License information unavailable'
_URLS = {
_DATASETNAME: {
"train": "https://github.com/YerevaNN/BioRelEx/releases/download/1.0alpha7/1.0alpha7.train.json",
"dev": "https://github.com/YerevaNN/BioRelEx/releases/download/1.0alpha7/1.0alpha7.dev.json",
},
}
_SUPPORTED_TASKS = [
Tasks.NAMED_ENTITY_RECOGNITION,
Tasks.NAMED_ENTITY_DISAMBIGUATION,
Tasks.RELATION_EXTRACTION,
Tasks.COREFERENCE_RESOLUTION,
]
_SOURCE_VERSION = "1.0.0"
_BIGBIO_VERSION = "1.0.0"
class BioRelExDataset(datasets.GeneratorBasedBuilder):
"""BioRelEx is a biological relation extraction dataset."""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
BUILDER_CONFIGS = [
BigBioConfig(
name="biorelex_source",
version=SOURCE_VERSION,
description="BioRelEx source schema",
schema="source",
subset_id="biorelex",
),
BigBioConfig(
name="biorelex_bigbio_kb",
version=BIGBIO_VERSION,
description="BioRelEx BigBio schema",
schema="bigbio_kb",
subset_id="biorelex",
),
]
DEFAULT_CONFIG_NAME = "biorelex_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"paperid": datasets.Value("string"),
"interactions": [
{
"participants": datasets.Sequence(datasets.Value("int32")),
"type": datasets.Value("string"),
"implicit": datasets.Value("bool"),
"label": datasets.Value("int32"),
}
],
"url": datasets.Value("string"),
"text": datasets.Value("string"),
"entities": [
{
"is_state": datasets.Value("bool"),
"label": datasets.Value("string"),
"names": [
{
"text": datasets.Value("string"),
"is_mentioned": datasets.Value("bool"),
"mentions": datasets.Sequence(
[datasets.Value("int32")]
),
}
],
"grounding": [
{
"comment": datasets.Value("string"),
"entrez_gene": datasets.Value("string"),
"source": datasets.Value("string"),
"link": datasets.Value("string"),
"hgnc_symbol": datasets.Value("string"),
"organism": datasets.Value("string"),
}
],
"is_mentioned": datasets.Value("bool"),
"is_mutant": datasets.Value("bool"),
}
],
"_line_": datasets.Value("int32"),
"id": datasets.Value("string"),
}
)
elif self.config.schema == "bigbio_kb":
features = kb_features
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=str(_LICENSE),
citation=_CITATION,
)
def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
urls = _URLS[_DATASETNAME]
data_dir = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_dir["train"],
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": data_dir["dev"],
},
),
]
def _generate_examples(self, filepath) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
with open(filepath, "r", encoding="utf8") as f:
data = json.load(f)
data = self._prep(data)
if self.config.schema == "source":
for key, example in enumerate(data):
yield key, example
elif self.config.schema == "bigbio_kb":
for key, example in enumerate(data):
example_ = self._source_to_kb(example)
yield key, example_
def _prep(self, data):
for example in data:
for entity in example["entities"]:
entity["names"] = self._json_dict_to_list(entity["names"], "text")
if entity["grounding"] is None:
entity["grounding"] = []
else:
entity["grounding"] = [entity["grounding"]]
return data
def _json_dict_to_list(self, json, new_key):
list_ = []
for key, values in json.items():
assert isinstance(values, dict), "Child element is not a dict"
assert new_key not in values, "New key already in values"
values[new_key] = key
list_.append(values)
return list_
def _source_to_kb(self, example):
example_id = example["id"]
entities_, corefs_, ref_id_map = self._get_entities(
example_id, example["entities"]
)
relations_ = self._get_relations(
example_id, ref_id_map, example["interactions"]
)
document_ = {
"id": example_id,
"document_id": example["paperid"],
"passages": [
{
"id": example_id + ".sent",
"type": "sentence",
"text": [example["text"]],
"offsets": [[0, len(example["text"])]],
}
],
"entities": entities_,
"coreferences": corefs_,
"relations": relations_,
"events": [],
}
return document_
def _get_entities(self, example_id, entities):
entities_ = []
corefs_ = []
eid = it.count(0)
cid = it.count(0)
# dictionary mapping the original ref ids (indexes of entities) for relations
org_rel_ref_id_2_kb_entity_id = defaultdict(list)
for relation_ref_id, entity in enumerate(entities):
# get normalization for entities
normalized_ = self._get_normalizations(entity)
# create entity for each synonym
coref_eids_ = []
for names in entity["names"]:
for id, mention in enumerate(names["mentions"]):
entity_id = example_id + ".ent" + str(next(eid)) + "_" + str(id)
org_rel_ref_id_2_kb_entity_id[relation_ref_id].append(entity_id)
coref_eids_.append(entity_id)
entities_.append(
{
"id": entity_id,
"type": entity["label"],
"text": [names["text"]],
"offsets": [mention],
"normalized": normalized_,
}
)
# create coreferences
coref_id = example_id + ".coref" + str(next(cid))
corefs_.append(
{
"id": coref_id,
"entity_ids": coref_eids_,
}
)
return entities_, corefs_, org_rel_ref_id_2_kb_entity_id
def _get_normalizations(self, entity):
normalized_ = []
if entity["grounding"]:
assert len(entity["grounding"]) == 1
if entity["grounding"][0]["entrez_gene"] != "NA":
normalized_.append(
{
"db_name": "NCBI gene",
"db_id": entity["grounding"][0]["entrez_gene"],
}
)
if entity["grounding"][0]["hgnc_symbol"] != "NA":
normalized_.append(
{"db_name": "hgnc", "db_id": entity["grounding"][0]["hgnc_symbol"]}
)
# maybe parse some other ids?
source = entity["grounding"][0]["source"]
if (
source != "NCBI gene"
and source != "https://www.genenames.org/data/genegroup/"
): # NCBI gene is same as entrez
normalized_.append(
self._parse_id_from_link(
entity["grounding"][0]["link"], entity["grounding"][0]["source"]
)
)
return normalized_
def _get_relations(self, example_id, org_rel_ref_id_2_kb_entity_id, interactions):
rid = it.count(0)
relations_ = []
for interaction in interactions:
rel_id = example_id + ".rel" + str(next(rid))
assert len(interaction["participants"]) == 2
subjects = org_rel_ref_id_2_kb_entity_id[interaction["participants"][0]]
objects = org_rel_ref_id_2_kb_entity_id[interaction["participants"][1]]
for s in subjects:
for o in objects:
relations_.append(
{
"id": rel_id + "s" + s + ".o" + o,
"type": interaction["type"],
"arg1_id": s,
"arg2_id": o,
"normalized": [],
}
)
return relations_
def _parse_id_from_link(self, link, source):
source_template_map = {
"uniprot": ["https://www.uniprot.org/uniprot/"],
"pubchem:compound": ["https://pubchem.ncbi.nlm.nih.gov/compound/"],
"pubchem:substance": ["https://pubchem.ncbi.nlm.nih.gov/substance/"],
"pfam": ["https://pfam.xfam.org/family/", "http://pfam.xfam.org/family/"],
"interpro": [
"http://www.ebi.ac.uk/interpro/entry/",
"https://www.ebi.ac.uk/interpro/entry/",
],
"DrugBank": ["https://www.drugbank.ca/drugs/"],
}
# fix exceptions manually
if source == "https://enzyme.expasy.org/EC/2.5.1.18" and link == source:
return {"db_name": "intenz", "db_id": "2.5.1.18"}
elif (
source == "https://www.genome.jp/kegg-bin/show_pathway?map=ko04120"
and link == source
):
return {"db_name": "kegg", "db_id": "ko04120"}
elif (
source == "https://www.genome.jp/dbget-bin/www_bget?enzyme+2.7.11.1"
and link == source
):
return {"db_name": "intenz", "db_id": "2.7.11.1"}
elif (
source == "http://www.chemspider.com/Chemical-Structure.7995676.html"
and link == source
):
return {"db_name": "chemspider", "db_id": "7995676"}
elif source == "intenz":
id = link.split("=")[0]
return {"db_name": source, "db_id": id}
else:
link_templates = source_template_map[source]
for template in link_templates:
if link.startswith(template):
id = link.replace(template, "")
id = id.split("?")[0]
assert "/" not in id
return {"db_name": source, "db_id": id}
assert (
False
), f"No template found for {link}, choices: {repr(link_templates)}"
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