# coding=utf-8 # Copyright 2022 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. """The KnowledgeNet dataset for automatically populating a knowledge base""" import json import re import datasets _CITATION = """\ @inproceedings{mesquita-etal-2019-knowledgenet, title = "{K}nowledge{N}et: A Benchmark Dataset for Knowledge Base Population", author = "Mesquita, Filipe and Cannaviccio, Matteo and Schmidek, Jordan and Mirza, Paramita and Barbosa, Denilson", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)", month = nov, year = "2019", address = "Hong Kong, China", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/D19-1069", doi = "10.18653/v1/D19-1069", pages = "749--758",} """ _DESCRIPTION = """\ KnowledgeNet is a benchmark dataset for the task of automatically populating a knowledge base (Wikidata) with facts expressed in natural language text on the web. KnowledgeNet provides text exhaustively annotated with facts, thus enabling the holistic end-to-end evaluation of knowledge base population systems as a whole, unlike previous benchmarks that are more suitable for the evaluation of individual subcomponents (e.g., entity linking, relation extraction). For instance, the dataset contains text expressing the fact (Gennaro Basile; RESIDENCE; Moravia), in the passage: "Gennaro Basile was an Italian painter, born in Naples but active in the German-speaking countries. He settled at Brünn, in Moravia, and lived about 1756..." For a description of the dataset and baseline systems, please refer to their [EMNLP paper](https://github.com/diffbot/knowledge-net/blob/master/knowledgenet-emnlp-cameraready.pdf). Note: This Datasetreader currently only supports the `train` split and does not contain negative examples """ _HOMEPAGE = "https://github.com/diffbot/knowledge-net" _LICENSE = "" # The HuggingFace dataset library don't host the datasets but only point to the original files # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) _URLS = { "train": "https://raw.githubusercontent.com/diffbot/knowledge-net/master/dataset/train.json", "test": "https://raw.githubusercontent.com/diffbot/knowledge-net/master/dataset/test-no-facts.json" } _VERSION = datasets.Version("1.1.0") _CLASS_LABELS = [ "NO_RELATION", "DATE_OF_BIRTH", "DATE_OF_DEATH", "PLACE_OF_RESIDENCE", "PLACE_OF_BIRTH", "NATIONALITY", "EMPLOYEE_OR_MEMBER_OF", "EDUCATED_AT", "POLITICAL_AFFILIATION", "CHILD_OF", "SPOUSE", "DATE_FOUNDED", "HEADQUARTERS", "SUBSIDIARY_OF", "FOUNDED_BY", "CEO" ] _NER_CLASS_LABELS = [ "O", "PER", "ORG", "LOC", "DATE" ] def get_entity_types_from_relation(relation_label): if relation_label == "DATE_OF_BIRTH": subj_type = "PER" obj_type = "DATE" elif relation_label == "DATE_OF_DEATH": subj_type = "PER" obj_type = "DATE" elif relation_label == "PLACE_OF_RESIDENCE": subj_type = "PER" obj_type = "LOC" elif relation_label == "PLACE_OF_BIRTH": subj_type = "PER" obj_type = "LOC" elif relation_label == "NATIONALITY": subj_type = "PER" obj_type = "LOC" elif relation_label == "EMPLOYEE_OR_MEMBER_OF": subj_type = "PER" obj_type = "ORG" elif relation_label == "EDUCATED_AT": subj_type = "PER" obj_type = "ORG" elif relation_label == "POLITICAL_AFFILIATION": subj_type = "PER" obj_type = "ORG" elif relation_label == "CHILD_OF": subj_type = "PER" obj_type = "PER" elif relation_label == "SPOUSE": subj_type = "PER" obj_type = "PER" elif relation_label == "DATE_FOUNDED": subj_type = "ORG" obj_type = "DATE" elif relation_label == "HEADQUARTERS": subj_type = "ORG" obj_type = "LOC" elif relation_label == "SUBSIDIARY_OF": subj_type = "ORG" obj_type = "ORG" elif relation_label == "FOUNDED_BY": subj_type = "ORG" obj_type = "PER" elif relation_label == "CEO": subj_type = "ORG" obj_type = "PER" else: raise ValueError(f"Unknown relation label: {relation_label}") return subj_type, obj_type def remove_contiguous_whitespaces(text): # +1 to account for regular whitespace at the beginning contiguous_whitespaces_indices = [(m.start(0) + 1, m.end(0)) for m in re.finditer(' +', text)] cleaned_text = re.sub(" +", " ", text) return cleaned_text, contiguous_whitespaces_indices def fix_char_index(char_index, contiguous_whitespaces_indices): new_char_index = char_index offset = 0 for ws_start, ws_end in contiguous_whitespaces_indices: if char_index >= ws_end: offset = offset + (ws_end - ws_start) new_char_index -= offset return new_char_index class KnowledgeNet(datasets.GeneratorBasedBuilder): """The KnowledgeNet dataset for automatically populating a knowledge base""" BUILDER_CONFIGS = [ datasets.BuilderConfig( name="knet", version=_VERSION, description="The original KnowledgeNet formatted for RE." ), datasets.BuilderConfig( name="knet_re", version=_VERSION, description="The original KnowledgeNet formatted for RE." ), datasets.BuilderConfig( name="knet_tokenized", version=_VERSION, description="KnowledgeNet tokenized and reformatted." ), ] DEFAULT_CONFIG_NAME = "knet" # type: ignore def _info(self): if self.config.name == "knet_tokenized": features = datasets.Features( { "doc_id": datasets.Value("string"), "passage_id": datasets.Value("string"), "fact_id": datasets.Value("string"), "tokens": datasets.Sequence(datasets.Value("string")), "subj_start": datasets.Value("int32"), "subj_end": datasets.Value("int32"), "subj_type": datasets.ClassLabel(names=_NER_CLASS_LABELS), "subj_uri": datasets.Value("string"), "obj_start": datasets.Value("int32"), "obj_end": datasets.Value("int32"), "obj_type": datasets.ClassLabel(names=_NER_CLASS_LABELS), "obj_uri": datasets.Value("string"), "relation": datasets.ClassLabel(names=_CLASS_LABELS), } ) elif self.config.name == "knet_re": features = datasets.Features( { "documentId": datasets.Value("string"), "passageId": datasets.Value("string"), "factId": datasets.Value("string"), "passageText": datasets.Value("string"), "humanReadable": datasets.Value("string"), "annotatedPassage": datasets.Value("string"), "subjectStart": datasets.Value("int32"), "subjectEnd": datasets.Value("int32"), "subjectText": datasets.Value("string"), "subjectType": datasets.ClassLabel(names=_NER_CLASS_LABELS), "subjectUri": datasets.Value("string"), "objectStart": datasets.Value("int32"), "objectEnd": datasets.Value("int32"), "objectText": datasets.Value("string"), "objectType": datasets.ClassLabel(names=_NER_CLASS_LABELS), "objectUri": datasets.Value("string"), "relation": datasets.ClassLabel(names=_CLASS_LABELS), } ) else: features = datasets.Features( { "fold": datasets.Value("int32"), "documentId": datasets.Value("string"), "source": datasets.Value("string"), "documentText": datasets.Value("string"), "passages": [{ "passageId": datasets.Value("string"), "passageStart": datasets.Value("int32"), "passageEnd": datasets.Value("int32"), "passageText": datasets.Value("string"), "exhaustivelyAnnotatedProperties": [{ "propertyId": datasets.Value("string"), "propertyName": datasets.Value("string"), "propertyDescription": datasets.Value("string"), }], "facts": [{ "factId": datasets.Value("string"), "propertyId": datasets.Value("string"), "humanReadable": datasets.Value("string"), "annotatedPassage": datasets.Value("string"), "subjectStart": datasets.Value("int32"), "subjectEnd": datasets.Value("int32"), "subjectText": datasets.Value("string"), "subjectUri": datasets.Value("string"), "objectStart": datasets.Value("int32"), "objectEnd": datasets.Value("int32"), "objectText": datasets.Value("string"), "objectUri": datasets.Value("string"), }], }], } ) return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, # This defines the different columns of the dataset and their types features=features, # Here we define them above because they are different between the two configurations # If there's a common (input, target) tuple from the features, # specify them here. They'll be used if as_supervised=True in # builder.as_dataset. supervised_keys=None, # Homepage of the dataset for documentation homepage=_HOMEPAGE, # License for the dataset if available license=_LICENSE, # Citation for the dataset citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name # 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 downloaded_files = dl_manager.download_and_extract(_URLS) # splits = [datasets.Split.TRAIN, datasets.Split.TEST] splits = [datasets.Split.TRAIN] return [datasets.SplitGenerator(name=i, gen_kwargs={"filepath": downloaded_files[str(i)], "split": i}) for i in splits] def _generate_examples(self, filepath, split): """Yields examples.""" # This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method. # It is in charge of opening the given file and yielding (key, example) tuples from the dataset # The key is not important, it's more here for legacy reason (legacy from tfds) if self.config.name == "knet_tokenized": from spacy.lang.en import English word_splitter = English() else: word_splitter = None with open(filepath, encoding="utf-8") as f: for line in f: doc = json.loads(line) if self.config.name == "knet": yield doc["documentId"], doc else: for passage in doc["passages"]: # Skip passages without facts right away if len(passage["facts"]) == 0: continue text = passage["passageText"] passage_start = passage["passageStart"] if self.config.name == "knet_tokenized": cleaned_text, contiguous_ws_indices = remove_contiguous_whitespaces(text) spacy_doc = word_splitter(cleaned_text) word_tokens = [t.text for t in spacy_doc] for fact in passage["facts"]: subj_start = fix_char_index(fact["subjectStart"] - passage_start, contiguous_ws_indices) subj_end = fix_char_index(fact["subjectEnd"] - passage_start, contiguous_ws_indices) obj_start = fix_char_index(fact["objectStart"] - passage_start, contiguous_ws_indices) obj_end = fix_char_index(fact["objectEnd"] - passage_start, contiguous_ws_indices) # Get exclusive token spans from char spans subj_span = spacy_doc.char_span(subj_start, subj_end, alignment_mode="expand") obj_span = spacy_doc.char_span(obj_start, obj_end, alignment_mode="expand") relation_label = fact["humanReadable"].split(">")[1][2:] subj_type, obj_type = get_entity_types_from_relation(relation_label) id_ = fact["factId"] yield id_, { "doc_id": doc["documentId"], "passage_id": passage["passageId"], "fact_id": id_, "tokens": word_tokens, "subj_start": subj_span.start, "subj_end": subj_span.end, "subj_type": subj_type, "subj_uri": fact["subjectUri"], "obj_start": obj_span.start, "obj_end": obj_span.end, "obj_type": obj_type, "obj_uri": fact["objectUri"], "relation": relation_label } else: for fact in passage["facts"]: relation_label = fact["humanReadable"].split(">")[1][2:] subj_type, obj_type = get_entity_types_from_relation(relation_label) id_ = fact["factId"] yield id_, { "documentId": doc["documentId"], "passageId": passage["passageId"], "passageText": passage["passageText"], "factId": id_, "humanReadable": fact["humanReadable"], "annotatedPassage": fact["annotatedPassage"], "subjectStart": fact["subjectStart"] - passage_start, "subjectEnd": fact["subjectEnd"] - passage_start, "subjectText": fact["subjectText"], "subjectType": subj_type, "subjectUri": fact["subjectUri"], "objectStart": fact["objectStart"] - passage_start, "objectEnd": fact["objectEnd"] - passage_start, "objectText": fact["objectText"], "objectType": obj_type, "objectUri": fact["objectUri"], "relation": relation_label }