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README.md

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----
-license: mit
----

SciGraph.py

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-# -*- coding: utf-8 -*-
-"""
-@Project    : indexing
-@File       : SciGraph
-@Email      : [email protected]
-@Author     : Yan Yuchen
-@Time       : 2023/3/9 12:53
-"""
-import json
-import datasets
-import pandas as pd
-import numpy as np
-from sklearn.model_selection import train_test_split
-
-
-_CITATION = """\
-@InProceedings{yan-EtAl:2022:Poster,
-  author    = {Yuchen Yan and Chong Chen},
-  title     = {SciGraph: A Knowledge Graph Constructed by Function and Topic Annotation of Scientific Papers},
-  booktitle = {3rd Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE2022), June 20-24, 2022, Cologne, Germany and Online},
-  month     = {June},
-  year      = {2022},
-  address   = {Beijing, China},
-  url       = {https://ceur-ws.org/Vol-3210/paper16.pdf}
-}
-"""
-
-_DESCRIPTION = """\
-"""
-
-_HOMEPAGE = ""
-
-# The license information was obtained from https://github.com/boudinfl/ake-datasets as the dataset shared over here is taken from here
-_LICENSE = ""
-
-_URLS = {
-    'classes': 'class.json',
-    'function': 'assign.json',
-    'topic': 'paper_new.json'
-}
-
-# TODO: Add link to the official dataset URLs here
-
-
-# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
-class SciGraph(datasets.GeneratorBasedBuilder):
-    """TODO: Short description of my dataset."""
-
-    VERSION = datasets.Version("0.0.1")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="function", version=VERSION,
-                               description="This part of my dataset covers extraction"),
-        datasets.BuilderConfig(name="topic", version=VERSION,
-                               description="This part of my dataset covers generation")
-    ]
-    
-    DEFAULT_CONFIG_NAME = "function"
-
-    def _info(self):
-        classes = ['综述与进展', '论证与对比', '思考与探讨', '原理与计算', '技术与方法', '设计与应用']
-        if self.config.name == "function":  # This is the name of the configuration selected in BUILDER_CONFIGS above
-            features = datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "abstract": datasets.Value("string"),
-                    "label": datasets.features.ClassLabel(names=classes, num_classes=len(classes))
-                }
-            )
-        else:
-            features = datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "abstract": datasets.Value("string"),
-                    "keywords": datasets.features.Sequence(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,
-            homepage=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        data_dir = dl_manager.download_and_extract(_URLS)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "split": "train",
-                    "classes": data_dir['classes'],
-                    "function": data_dir['function'],
-                    "topic": data_dir['topic']
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "split": "test",
-                    "classes": data_dir['classes'],
-                    "function": data_dir['function'],
-                    "topic": data_dir['topic']
-                },
-            )
-        ]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, split, classes, function, topic):
-        if self.config.name == 'function':
-            with open(classes, 'r') as f:
-                functions = list(json.load(f).keys())
-            data = pd.read_json(function)
-            data = data.loc[data[functions].sum(axis=1) == 1]
-            data['label'] = [functions[row.tolist().index(1)] for index, row in data[functions].iterrows()]
-            data = data[['_id', 'abstract', 'label']]
-            
-            
-            train_data, test_data = train_test_split(data, test_size=0.1, random_state=42)
-            if split == 'train':
-                for idx, row in train_data.iterrows():
-                    yield idx, {
-                        "id": row._id,
-                        "abstract": row.abstract,
-                        "label": row.label
-                    }
-            elif split == 'test':
-                for idx, row in test_data.iterrows():
-                    yield idx, {
-                        "id": row._id,
-                        "abstract": row.abstract,
-                        "label": -1
-                    }
-
-        if self.config.name == 'topic':
-            data = pd.read_json(topic)
-            data = data.replace(to_replace=r'^\s*$', value=np.nan, regex=True).dropna(subset=['keywords'], axis=0)
-
-            train_data, test_data = train_test_split(data, test_size=0.1, random_state=42)
-            if split == 'train':
-                for idx, row in train_data.iterrows():
-                    yield idx, {
-                        "id": row._id,
-                        "abstract": row.abstract,
-                        "keywords": row.keywords.split('#%#')
-                    }
-            elif split == 'test':
-                for idx, row in test_data.iterrows():
-                    yield idx, {
-                        "id": row._id,
-                        "abstract": row.abstract,
-                        "keywords": row.keywords.split('#%#')
-                    }

class.json

@@ -1,8 +0,0 @@
-{
-  "综述与进展": ["综述", "进展", "现状", "展望", "启示", "趋势", "前景"],
-  "论证与对比": ["说明", "评价", "评估", "分析", "对比", "比较", "改善", "验证", "改进"],
-  "思考与探讨": ["思考", "讨论", "探讨", "意义", "浅谈", "探析", "建议", "探索", "探究"],
-  "原理与计算": ["基础", "计算", "理论", "原理", "求解", "规律", "理念", "性质", "机制"],
-  "技术与方法": ["技术", "方法", "算法", "模型", "思路", "对策", "措施", "策略", "方式"],
-  "设计与应用": ["设计", "实现", "应用", "实践", "方案", "案例", "运用", "制作", "研发", "研制"]
-}

paper_new.json → function/sci_graph-test.parquet

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assign.json → function/sci_graph-train.parquet

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topic/sci_graph-test.parquet

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topic/sci_graph-train-00000-of-00002.parquet

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topic/sci_graph-train-00001-of-00002.parquet

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