dataesr/scientific-paragraphs-categorization huggingface dataset:
The provision of samples to the Molecule Archive has been the origin of many publications that were done in collaboration with compound providers and re-usersapproving that the provision of data can result directly in more visibility and impact for the scientists sharing their research results. 32, 33, 34, 35, 36, 37, 38  https://doi. org/10. 26434/chemrxiv-2023-2dd4c ORCID: https://orcid. org/0000-0001-9513-2468 Content not peer-reviewed by ChemRxiv. License: CC BY 4. 0 generation of, e. g. , molecular descriptors facilitate the work of the scientists as the most suitable descriptors for a search for chemical compounds are generated by the software without additional efforts. The combination of information entered by the user and system-generated information directly forms the virtual metadata representation of the sample and defines the digital object.
With the available information on the sample, the submission of research data can be started. The data has to be prepared according to discipline-specific standards, described in detail in the onlinedocumentation of the Chemotion repository. 46  The better the data is prepared according to the given best practice examples, the FAIRer is the data that is accessible in the repository in the end. After submitting the dataset, the data is reviewed for plausibility and completeness, before it is open and accessible. As soon as the virtual sample representation is visible (along with the data), the correlation of research data and materials can be started by the team of the repository. Setting up a legal framework for sharing materials: Sharing of materials in a scientific environment can be done in two ways: either via a donation of the material from one scientific group to another one, or via the transfer of material under certain negotiated conditions.
Availability of data and materials The Supplemental Information (SI, file 1) includes two examples as representative metadata schema of a virtual sample representation available in the Chemotion repository (chapter 1), and a detailed description of how the samples visible in the Chemotion repository website can be assessed for further re-use through the provided request form (chapter 2). The SI -part 1 covers further additional information on the processes of the Molecule Archive (chapter 3) including a template of a data upload form that is used to register samples in the Molecule Archive (chapter 4).
Acknowledgements https://doi. org/10. 26434/chemrxiv-2023-2dd4c ORCID: https://orcid. org/0000-0001-9513-2468 Content not peer-reviewed by ChemRxiv. License: CC BY 4. 0 We are very thankful to the members of the  Stefan Bräse group  who contributed to the establishment of the repository and the Molecule Archive. We thank the following scientists who provided samples for the Molecule Archive and the Chemotion repository to improve the workflows described in this publication: scientists from  Karlsruhe Institute of Technology, KIT, Germany  (department at KIT):  Changming Hu  (INT),  Timo Sehn  ( IOC ),  Lena Pilz  ( IFG ),  Ilona Wagner  ( IFG ); scientists from other universities:  Violeta Vetsova ,  Rachel Janssen  (both  LMU, Munich, Germany ),  Sylvain Grosjean  ( Université de Franche-Comté -UFC, Besancon, France ),  Miro Hałaczkiewicz  ( RPTU Kaiserslautern-Landau, Germany ),  Robert Forster ,  Rainer Wiechert  (both  JGU Mainz, Germany ),  Felix Potlitz  ( University of Greifswald, Germany ) and  Fabian Thomas ,  Regina Schmidt ,  Christian Conrads  ( RWTH Aachen University ).
Software: The infrastructure described in this article is built with the use of open source software that was developed at KIT and was described in previous articles. Both the Chemotion repository and the software behind the Molecule Archive were developed based on components of the source code of Chemotion ELN. 39, 40  Further extensions of Chemotion ELN, including submission and reviewing workflow, provide the necessary functionality to operate the research data repository Chemotion. 14, 41  The source code for the Chemotion repository can be obtained from GitHub  42. For the operation of the Molecule Archive, Chemotion ELN was also used and adapted with a plugin  43  to keep additional information on the sample information as provided by the owner and was extended with Foreign Data Wrappers (FDW)  44  for smooth integration of data from the Chemotion repository and the Molecule Archive.
Submission of data to the Chemotion repository: The submission of data to the Chemotion repository isin the examples described in this articlethe next step towards FAIR data and FAIR-AR samples. It enables the generation of the FAIR metadata of the samples' virtual representation and the storage of FAIR research data. The data uploaded can be managed through either the GUI of the repository or a transfer of data from an ELN to the repository. Both ways to get data available in the repository are established and were used by the groups who contributed to this work. Both processes include a request to the user to add information on the sample that was used for the measurement of the data. Usually this is the chemical structure of the compound assigned to the sample and additional information on the purity of the sample and other characteristics.
This information is used by the software to automatically generate further sample metadata that can be used to identify the sample and to search for the sample. The automatic https://doi. org/10. 26434/chemrxiv-2023-2dd4c ORCID: https://orcid. org/0000-0001-9513-2468 Content not peer-reviewed by ChemRxiv. License: CC BY 4. 0 Altogether, more than three dozen research groups at universities and other noncommercial research entities are already partners of the Molecule Archive network, and scientists working at these sites can work under the existing MTA. Other scientists who work at institutions that do not have an agreement yet can request to start the MTA generation process with their institution.
Submission of Samples to the Molecule Archive: The submission of samples to the Molecule Archive works via a simple workflow: The Molecule Archive provides suitable standard vessels that are sent to the compound provider. The vessels are calibrated and carry a unique number for their later identification. A table sheet is provided to the users, which is required to register the sample in the database of the Molecule Archive. The sample providers need to give brief information on the chemical structure assigned to the materials in the form of the corresponding SMILES code, the code of the used vessel, the internal laboratory ID and properties such as the approximate purity of the material and the filled mass (an example is added to the SI, section 4). The filled vessels are then sent back to the Molecule Archive using the provided packaging material, and the digital upload form is transmitted via email.
During and after the Covid-19 pandemic, people relied heavily on the internet for information because of its easy accessibility. However, the spread of fake information through this medium has been fast-growing, especially during and after the pandemic. It is therefore imperative to design and develop systems that can filter out fake and untrue news from social media platforms. This solution can be provided utilizing the power of artificial intelligence and machine learning methods. This study, therefore, aims to evaluate the performance of 5 machine learning models used in detecting Covid-19 fake news. The models were trained using the Covid-19 dataset gathered online. The dataset contains 7,262 real news and 9,727 fake news, totaling 16,989 news altogether. 80% of this dataset was used for training the models while 20% was used for testing them.
A good example of binary classification is fake news detection while handwritten character recognition best describes a multiclass classification  [5]. Various studies and research have been carried out on the detection of fake news including  [6]  which employed 4 text feature extraction techniques and 10 ML and deep learning methods for the detection. The accuracy of the methods tested ranged from 0. 81 to 1. 00. A study in  [7]  developed different ML ensemble models such as voting, bagging and boosting classifiers for 4 real-life datasets. The developed ensemble models performed better than the base learners used for comparison. A study in  [8]  curated a set of data with 10,700 social media stories containing real and fake news on the coronavirus. Different ML models were used for a binary classification task and the best performance of 0.
93 F1-score was achieved with SVM  [8]. Using ensemble ML models, better accuracy was achieved on the LIAR dataset  [9]. The ensemble models achieved precision and recall of about 70% as against weak learners with about 39%. A multi-web platform voting framework was developed  [10]. Upon performance evaluation, it was observed that the model was a good classifier as it correctly identified about 98% of the COVID misinformation  [10]. A detection system using particle swarm optimization, the genetic algorithm, and the salp swarm algorithm was the subject of the study  [11]. The genetic algorithm model achieved an accuracy of 75. 4% while reducing the number of features. [11]. A comparative analysis of fake news classification ML models used on 3 datasets was presented  [12]. The SVM classifier model had the best correct predictions with 0.
61, 0. 97 and 0. 96 on the Liar, Fake Job Posting and Fake News datasets respectively  [12]. An automated false news detection system for COVID-19 using 5 ML and deep learning (DL) algorithms was evaluated  [13]. The DL models utilized the bidirectional encoder representations from transformers (BERT). BERT had an accuracy of 0. 988 as a classifier and 0. 991 with deep learning models  [13]. Most of the research studies on the detection of fake news are aimed towards the English language, which is why the study in  [14]  focused on detecting fake news in Hindi. Detecting fake news in languages with little research resources such as Hindi was carried out using a stack of pre-trained transformer models. The study reveals that the use of transfer learning from XLM-RoBERTa, mBERT and ELECTRA was able to achieve increased efficiency of fake news detection in Hindi  [14].
The study of  [15]  presents a technique for detecting fake news using multimodal EFND. A Multilayer Perceptron was implemented and the results of the study showed that for the PolitiFact and GossipCop datasets, the EFND achieved an accuracy of 0. 988% and 0. 990%, respectively  [15]. This project aims to design an efficient Covid-19 fake news detection and classification system using ML algorithms and also focuses on the comparative analysis of the performance of ML models in detecting and classifying Covid-19 fake news. The classification models employed in this project are Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR) and k-nearest neighbour (KNN). The developed systems can be implemented and incorporated into online social media networks and news outlets for filtering out fake news from available information.
Covid-19 News Dataset Data collection involves the gathering and measuring of information from countless different sources. The dataset used in this work consists of news articles labelled as fake or real and are gathered from online sources. The dataset contains 7,262 real news and 9,727 fake news, totalling 16,989 news altogether. In Table  1 , each news article in the dataset has a title, outcome and label while Figure  1  shows the normalized frequency of the target labels. The real news is labelled as 0 while the fake news is labelled as 1. A picture shows hydroxychloroquine is being sold on public trains in Rio de Janeiro fake 1 6 There is evidence that chlorine dioxide cures COVID-19 fake 1 Figure  1. Normalized Frequency Count of the Outcome Labels.
Feature Extraction The dataset gathered was in raw form and cannot be used for analysis, hence the need for data preprocessing. Data preprocessing involves converting the raw data into a clean dataset. Stop-word removal, tokenization, lower casing, sentence segmentation, and punctuation removal were data preprocessing steps taken to remove unwanted information from the data and consequently reduce the dataset's original size. These were done using the NLTK (The Natural Language Toolkit) package. Furthermore, the dataset was converted from texts to numerical values because the models can only understand numerical data, using the TF-IDF Vectorizer. To train the models, the dataset was randomly split into the training and testing dataset in a ratio of 4-to-1. The algorithms learned using the training dataset and evaluated with the test dataset to confirm that the models had not overfitted or memorized the train data.
Decision Tree Classifier A Decision Tree (DT) is a flowchart-like tree structure in which an internal node represents a feature, a branch is a decision rule, and the leaf nodes represent the decision outcomes. The model partitions the tree recursively based on the attribute values to classify the output label. Entropy measure is used to identify the root node of the DT. Entropy is defined in Equation  2. 𝐸𝑛𝑡𝑟𝑜𝑝𝑦 (𝐷) = ∑ -𝑝 𝑖 𝑙𝑜𝑔 2 (𝑝 𝑖 ) 𝑛 𝑖=1 (2) Where D is the data split, n is the number of decision classes, and pi is the proportion of records under the decision class i. The maximum number of splits in the DT designed is 200.
Support Vector Machine The SVM is a supervised learning model used for both classification and regression and is widely used in text classification. Each data point is plotted in n-dimensional space with n being the number of features in the dataset. The model then performs classification by finding the optimal hyper-plane that differentiates the target labels. Kernel SVM is used for optimization as this is not linear data. A regularization parameter was set for 1. 70 and the kernel coefficient for the radial basis function was set as scale.
K-Nearest Neighbor The k-nearest neighbours (KNN) algorithm uses the distance between data points to make classifications. The class labels are selected based on the majority vote of the selected number of neighbors. The Euclidean distance is used to measure proximity as shown in Equation  4. The number of neighbors or the value of k is set to 14 and a weights parameter is set as distance. 𝐸𝑢𝑐𝑙𝑖𝑑𝑒𝑎𝑛 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 = √∑(𝑥 𝑖 -𝑦 𝑖 ) 2 𝑛 𝑖=1 (4) Where n is the length of the dataset, xi is the instances in the training set and yi is the instances in the test set.
CONCLUSION The comparative analysis of the performance of 5 machine learning classification models that are used to distinguish between real and fake Covid-19 news was carried out. The dataset contains 16,989 covid-19 news and was partitioned into training and testing to train and evaluate the models respectively. The evaluation results show that the Support Vector Machine has the highest positive predictions and the best performance in all evaluation scenarios. This study, therefore recommends SVM as the best classification algorithm for differentiating between real and fake Covid-19 news. The study has not studied and considered the impact and effect of feature selection on the detection of covid-19 fake news. Further studies will be encouraged to implement feature selection and in addition, the best-performing model can be deployed to a web application for real-time detection and filtering of covid-19 fake news.
Effectiveness vs Efficiency Tradeoff. Is the runtime overhead of each method worth paying, with respect to the achieved effectiveness? Q4. Characteristics of datasets. To which characteristics of the datasets (e. g. , sparsity, number of entity pairs in seed alignment, heterogeneity in terms of literals, predicate names and entity names) are supervised, semi-supervised and unsupervised methods sensitive? Although several recent works  (Zeng et al. 2021; Choudhary et al. 2021; Wang et al. 2017; Sun et al. 2020; Zhang et al. 2020; Zhao et al. 2022; Jiang et al. 2021; Wang et al. 2021; Leone et al. 2022; Zhang et al. 2022; Chaurasiya et al. 2022)  1 3 Knowledge graph embedding methods for entity alignment: an… survey embedding-based entity alignment methods, only few of them  (Sun et al. 2020; Zhang et al.
(2022) ,  Chaurasiya et al. (2022)  either. Moreover, benchmarking efforts such as  Zhang et al. (2020) , OpenEA  (Sun et al. 2020) , EAE  (Zhao et al. 2022)  and  Leone et al. (2022) ,  Zhang et al. (2022) ,  Chaurasiya et al. (2022) , do not shed light on questions Q1, Q2 and Q3, addressed in our work. Furthermore, only a subset of the dataset characteristics we study in our work, such as the density of the KGs, and the similarity of entity names, have been considered by previous works to answer question Q4. To compare the effectiveness and efficiency of the methods in realistic settings, we have extended the testbed of datasets with pairs of KGs usually considered in related empirical studies. Specifically, OpenEA, EAE and  Leone et al. (2022) ,  Zhang et al. (2022) ,  Chaurasiya et al.
(2022)  employ only datasets with a low number of entities featuring descriptions and literal values. In our testbed, we have included five additional datasets whose unique characteristics allow us to draw new insights regarding the evaluated EA methods, which were not previously reported in  Sun et al. (2020) ,  Zhang et al. (2020) ,  Zhao et al. (2022) ,  Leone et al. (2022) ,  Zhang et al. (2022) ,  Chaurasiya et al. (2022). More precisely, supervised methods like RDGCN exploiting KG relations, are outperformed by unsupervised (AttrE) and semi-supervised (KDCoE) methods that exploit the similarity of literals in datasets of decreasing density, but with rich factual information (i. e. , attributes). Rather than simply reporting the raw experimental results, we conduct a metalevel analysis, aiming to find statistically significant correlations between the methods and the dataset characteristics (meta-features).
Furthermore, we consider the non-parametric Friedman test  (Demsar 2006 ) and the post-hoc Nemenyi test  (Nemenyi 1963) , in order to perform a pairwise comparison of the methods and rank them based on their effectiveness across all datasets of our testbed. Finally, we are interested in the methods' training time curve and potential effectiveness vs efficiency trade-offs instead of simply reporting overall runtimes (as in OpenEA and EAE). In a nutshell, the contributions of this work are the following: • In Sect. 2, we present a qualitative comparison of state-of-the-art embeddingbased entity alignment methods that span from supervised, i. e. , MTransE  (Chen et al. 2017) , MTransE+RotatE  (Sun et al. 2020) , MultiKE  (Zhang et al. 2019) , RDGCN  (Wu et al. 2019) , RREA(basic)  (Mao et al.
2020b) , BERT_INT  (Tang et al. 2020) , to unsupervised, i. e. , AttrE  (Trisedya et al. 2019) , and semi-supervised, i. e. , KDCoE  (Chen et al. 2018) , RREA(semi)  (Mao et al. 2020b ), paradigms. They are representative methods of different embedding families covering both relation-and attribute-based, but also considering one-hop and multi-hop neighborhoods in KGs, as well as different negative sampling strategies. • In Sect. 3, we describe our framework for a fair empirical comparison of the different methods. We detail the extended testbed of datasets that exhibit diverse characteristics (w. r. t. KG density, entity naming, textual descriptions, etc. ) usually encountered in reality along with the corresponding pre-processing pipelines. We additionally introduce the evaluation protocol and metrics capturing different aspects of the methods' effectiveness.
• In Sect. 4, we report and analyze the results of a series of experiments, including a comparison to a state-of-the-art non-embedding-based method, PARIS  (Suchanek et al. 2011) , conducted to answer the four open questions introduced previously, using a reliable, statistically sound methodology. First, we discover a statistical significant ranking of the methods according to their effectiveness across all real-world KGs of our testbed. Then, we study interesting trade-offs in terms of their effectiveness and efficiency. Last but not least, we extract statistically significant correlations between the methods' performance with various characteristics of our datasets (i. e. , meta features). Finally, the main conclusions drown from our experiments, as well as the plans for future work, are discussed in Sect.
Embedding module KG embedding methods proposed for the task of link prediction are used to implement the embedding module of entity alignment methods. There are several families of KG embedding methods for link prediction have been proposed in the literature, e. g. ,  Yang et al. (2015) ,  Trouillon et al. (2016) ;  Nickel and Kiela (2017). In this paper, we are interested in contrasting translational methods such Knowledge graph embedding methods for entity alignment: an… as TransE  (Bordes et al. 2013)  and RotatE  (Sun et al. 2019) , with Graph Neural Networks such as Graph Convolutional Networks (GCNs)  (Kipf and Welling 2017)  and Graph Attention Networks (GATs)  (Velickovic et al. 2018).
Attribute names Some embedding-based entity alignment methods use the attribute names for learning the entity embeddings. Usually, attribute names are exploited by the methods in order to enhance the similarity measure of entities and 1 3 not as main source of information for learning the embeddings. That is because it requires KGs to have similar naming schemes, which is not usual in real-world KGs. MultiKE is the only method that utilizes this type of information, requiring a homogeneous naming in terms of attributes. Thus, we expect this feature to perform better in datasets originating from the same or similar data sources (e. g. , Wikipediabased, or synthetic KGs).
3 Knowledge graph embedding methods for entity alignment: an… For the datasets from OpenEA, three well-known KGs were used as sources: DBpedia (version 2016-10)  (Lehmann et al. 2015) , Wikidata (version 20160801)  (Vrandecic and Krötzsch 2014)  and YAGO3  (Rebele et al. 2016). The seed alignment is constructed by the owl:sameAs links, which were provided with those KGs. In addition, it is hard for embedding-based approaches to run on full KGs due to the large candidate space, hence the authors of OpenEA sampled real-world KGs, by their iterative degree-based sampling algorithm described in Sect. 3. 4. The resulting KGs have a size of 15K entities each, constituting the datasets D_W_15K_V1 and D_Y_15K_V1. In order to examine the behaviour of the methods with respect to the density of the KGs, the authors of OpenEA generated an additional dense version for each dataset (D_W_15K_V2 and D_Y_15K_V2).
For generating the dense version, they deleted entities (nodes) with low ( ≤ 5 ) node degrees and they performed again their iterative degree-based sampling algorithm, described in Sect. 3. 4. The above mentioned datasets are described as follows: • D_W_15K_V1 and D_W_15K_V2 are the sparse and the dense datasets, respectively, that were constructed from DBpedia and Wikidata KGs, describing actors, musicians, writers, films, songs, cities, football players and football teams. We chose them in order to examine the influence of KG density variations in the relation-and attribute-based methods. In addition, these datasets have both low entity name similarity and a low number of entities that have long textual descriptions, undermining the methods that use them. In these datasets, relation and attribute names have been replaced with special ids, which influences in a negative way methods that perform schema alignment using predicate names.