dataesr/scientific-paragraphs-categorization huggingface dataset:
Ontology conceptualisation In order to conceptualise an ontology that structures and standardises the description of NLP tools we proceeded to a joint study of: • Documentation for various NLP tools processing aforementioned languages that have been installed and closely tested; • A large collection (around ten thousands) of structured ReadMe in the Markdown format, crawled from GitHub repositories; • Meta-data models for Language Resources (LR) as the CMDI  (Broeder et al. , 2012)  or META-SHARE meta-data model ontology  (McCrae et al. , 2015). This study gave us guidelines to define bundles of properties sharing a similar semantic. For example, properties referring to the affiliation of the tool (as hasAuthor, hasLaboratory or hasProjet), to its installation or its usage. We distinguish two levels of meta-data: 1) a mandatory level providing basic elements that constitute a ReadMe file and 2) a nonmandatory level that contains additional information as relations to other tools, fields or methods.
The SKOS ontology structure is automatically created from the OWL ontology. Thus, adding a new language essentially consists in adding within SKOS TFs in that particular language to each OWL property. Translation functions are of two kinds: 1. P(V 1 ) ; * V 1 *@lang 2. P(V 1 ,V 2 ) ; * V 1 * V 2 * or * V 2 * V 1 * @lang with P a property, * a set of words that can be empty, V 1 , V 2 values of the property P and @lang an OWL language tag that determines the language in which the property is lexicalised. Below, two examples of tranlation functions for Japanese that have been associated to the properties authorFirstName and download. • authorFirstName(V 1 ) ; 作成者名: V 1 @jp • download(V 1 ,V 2 ) ; V 2 から V 1 をダウン ロ-ドする。@jp 3 Natural language generation of multilingual ReadMe files In our framework, each NLP expert finds, installs and uses available NLP tools processing the language he speaks.
Then, he describes every tool that runs correctly via an on-line platform connected to the ontology (cf. Figure  1 ). Elements of description do not only come from an existing ReadMe as if they exist, they are rarely exhaustive. Hence, experts also gather tool information from the web and during installing and testing each tool. At this step, the OWL ontology is filled and the translated functions of each property are defined within the SKOS ontology. Our aim is to generate ordered and structured ReadMe files in different languages. To do so, we use Natural language generation (NLG) techniques adapted to the Semantic Web (also named Ontology verbalisation)  (Staykova, 2014; Bouayad-Agha et al. , 2014; Cojocaru and Trãuşan Matu, 2015; Keet and Khumalo, 2016). NLG can be divided in several tasks  (Reiter and Dale, 2000; Staykova, 2014).
Ontology content aggregation and lexicalisation Following the heuristics proposed in  (Androutsopoulos et al. , 2014)  and  (Cojocaru and Trãuşan Matu, 2015)  to obtain concise text, OWL property values are aggregated when they characterise the same object. For example, if an execution procedure (ep i ) has two values for operating system (ex : Linux and Mac) then the two values are merged as the following below: hasOS(ep i ,Linux) ∧ hasOS(ep i ,Mac) ⇒ hasOS(ep i ,Linux and Mac) The last step consists in property lexicalisation. While a number of approaches rely on ontology elements' names and labels (often in English) to infer a lexicalisation  (Bontcheva, 2005; SUN and MELLISH, 2006; Williams et al. , 2011) , in our approach, the lexicalisation of properties depend only on their translation functions.
During the ontology verbalisation, each targeted language is processed one after the other. The TF of encountered properties for the current language is retrieved and used to lexicalise the property. Property values are considered as variables of the TFs. They are not translated as we ensure that they are as much as possible independent of the language. Figure  3  gives an example of two installation procedures for the NLP tool Jieba that processes Chinese. In this example, actions are lexicalised in English. Furthermore, the lexicalised command lines appear in between brackets. As a result of this generation, all ReadMe files have the same structure, organisation and, as much as possible, level of detail, especially regarding installation and execution procedures which represent the key information for a tool usage.
Conclusion We proposed an ontology-based approach for generating simple, structured and organised ReadMe files in different languages. Readme structuring and lexicalisation is guided by the ontology properties and their associated translation functions for the targeted languages. The generated ReadMes are intended to be accessible via an on-line platform. This platform documents in several languages NLP tools processing different languages. In a near future, we plan to evaluate the complexity for end-users of different level of expertise to install and execute NLP tools using our generated ReadMe files. We also hope that, as a side-product, the proposed conceptualisation may provide a starting point to establish guidelines and best practices that NLP tool documentation often lacks, especially for under-resourced languages.
go to the directory jieba-0. 38 (cd jieba-0. 38/) 4-type the command: python setup. py install Procedure name: pip -ubuntu 1 -type the command: sudo pip install jieba Procedure name: wget -ubuntu 1-download jieba-0. 38. zip via wget (wget https://pypi. python. org/packages/f6/86 /9e721cc52075a07b7d07eb12bcb5dde771d35332a 3dae1e14ae4290a197a/jieba-0. 38. zip) 2-unzip jieba-0. 38. zip (unzip jieba-0. 38. zip) 3-.
H Nuclear Magnetic Resonance (NMR)-based metabolic profiling is very promising for the diagnostic of the stages of chronic kidney disease (CKD). Because of the high dimension of NMR spectra datasets and the complex mixture of metabolites in biological samples, the identification of discriminant biomarkers of a disease is challenging. None of the widely used chemometric methods in NMR metabolomics performs a local exhaustive exploration of the data. We developed a descriptive and easily understandable approach that searches for discriminant local phenomena using an original exhaustive rule-mining algorithm in order to predict two groups of patients: 1) patients having low to mild CKD stages with no renal failure and 2) patients having moderate to established CKD stages with renal failure. Our predictive algorithm explores the m-dimensional variable space to capture the local overdensities of the two groups of patients under the form of easily interpretable rules.
Afterwards, a L2penalized logistic regression on the discriminant rules was used to build predictive models of the CKD stages. We explored a complex multi-source dataset that included the clinical, demographic, clinical chemistry, renal pathology and urine metabolomic data of a cohort of 110 patients. Given this multi-source dataset and the complex nature of metabolomic data, we analyzed 1-and 2-dimensional rules in order to integrate the information carried by the interactions between the variables. The results indicated that our local algorithm is a valuable analytical method for the precise characterization of multivariate CKD stage profiles and as efficient as the classical global model using chi2 variable section with an approximately 70% of good classification level. The resulting predictive models predominantly identify urinary metabolites (such as 3-hydroxyisovalerate, carnitine, citrate, dimethylsulfone, creatinine and N-methylnicotinamide) as relevant variables indicating that CKD significantly affects the urinary metabolome.
Moreover, the clinical and biological characteristics of the patients (such as age, sex, weight, diet or chemistry information) might affect the spectral composition of the samples as well as the outcome of interest adding potential discriminant sources of data  [15]. Hence, considering the nature of the metabolomic data and the possible usefulness of additional sources of data, we propose to extend our previous work by adding 2D rules (i. e. , two distinct conditions of variables) in order to integrate the interactions between the variables. In addition, we considered in this study other available sources of data. By this process, we aimed to accomplish the following: (1) to verify whether the multidimensional rules enhanced the models in terms of predictive power and medical knowledge and (2) to determine which were the most relevant variables among the metabolomic and clinical data.
Therefore, we could provide indications into the way in which the urine metabolome and other clinico-biological data characterize the CKD stages. We used a multi-source dataset of clinical data, demographics, clinical chemistry, renal pathology and urine metabolomic data as explanatory variables as an input for our algorithm. We wanted to test whether and how a combination of variables allows us to describe individuals with an estimated Glomerular Filtration Rate (eGFR) ! 60 ml/min/1. 73 m 2 corresponding to KDIGO CKD stages 1 and 2 (no renal failure) and individuals with an eGFR < 60 ml/min/ 1. 73 m 2 corresponding to KDIGO CKD stages 3 to 5 (renal failure). We first constructed the local 1D rules and 1&2D rules models (i. e. , resp. with and without variable interactions) solely on metabolomic data on the one hand and on the multi-source data on the other.
Given the initial number of variables, we observed that our local variable selection is particularly stringent leading to low complexity models. The stability measures were lower for the local models than for the global model. A perfect overlap between 1D rules and even more between 2D rules is likely strongly disturbed by several phenomena as follows: 1) the low number of patients leading to small differences in the dataset splits (train/test) might have induced strong perturbations in the rules' generation, 2) the nature of the metabolomic data including the overlap of some metabolites in the 1 H NMR spectra and that two variables could interact together because they could correspond to buckets (i. e. , parts of the metabolomic spectrum, see Methods) that fit the same molecule, or that belong to the same function or biological pathway.
Metabolomic profiles reflect CKD severity We investigated the variables selected by the models to identify the urine metabolomic profile predictive of CKD. For the global model, 42 buckets out of the 184 were present at least once (Fig  1 ). The most commonly identified buckets (present in at least 50% of the models) were 1. 28 (3-hydroxyisovalerate); 2. 48 (carnitine); 2. 56, 2. 68 and 2. 72 (citrate); 3. 16 (dimethylsulfone); 3. 56 (glycine); 3. 96 (creatinine); 4. 44 (trigonelline), and 4. 48 (N-methyl nicotinamide). The corresponding metabolites were identify manually using the Human Metabolome Database (HMDB), http:// www. hmdb. ca/ [16, 17]. For the local 1D model, at least one significant rule was selected for each one of the 42 preselected global variables (Buckets) for at least one of the two subgroups of patients (eGFR < 60 ml/min/1.
73m 2 or eGFR ! 60 ml/min/1. 73m 2 ) (Fig  2 ). The rules characterized by the same bucket for both eGFR < 60 and ! 60 ml/min/1. 73m 2 are generally disjoint (i. e. , covered On both types of datasets, metabolomics (urine 1 H NMR metabolomic data alone) and multi-source (clinical data, demographics, clinical chemistry, renal pathology, urine metabolomic data), for each of the three models (global, local 1D, local 1&2D), the table reports the F1-score (mean±sd), the average complexity computed over 100 runs, the p-value estimated with the permutation test, the most frequent C regularization coefficient(s) and its occurrence frequency in parentheses (freq) across the 100 runs as well as the stability of the model defined as the common percentage of selected variables between runs (mean±sd). doi:10.
Multi-source profile of CKD severity The models computed on the multi-source dataset (i. e. , clinical, demographic, clinical chemistry, renal pathology and urine metabolomic data) were in most cases composed of the same variables as those from the models applied solely to the metabolomics dataset. Among the variables selected by the global model, only the following two variables were not   This figure shows the frequency heatmap of the interaction (color scale) of each bucket pair over the 100 local 1&2D models. The more robust the interaction is, the darker it will be. The interactions corresponding to the subgroup of patients with an eGFR < 60 ml/min/1. 73m 2 are displayed in the lower triangle and conversely, the interactions corresponding to the subgroup of patients with an eGFR ! 60 ml/min/ 1.
73m 2 are displayed in the upper triangle. The limit of the two triangles is represented by the red dashed-line. Additionally, the local 1&2D models (Fig  7 ) highlight that these variables interacted for individuals with an eGFR < 60 ml/min/1. 73m 2 and with the buckets belonging to the cluster identified in the metabolomic local 1&2D models (Figs  3  and 4 ) for both the groups of individuals < 60 and ! 60 ml/min/1. 73m 2. However, these two non-metabolomic variables were present in less than 35% of the 100 global models, explaining the similar performances obtained on both metabolomic and multi-source datasets with the three type of models. Simply knowing the concentration of urinary metabolites allows us to correctly identify the eGFR class of individuals.
It is worth noting that these relevant results have been found notwithstanding a coarse 1 H NMR spectra pre-processing. In terms of data, several ways of improving this study will be to refine the CKD stages, increase the number of subjects and review the H NMR spectra pre-processing. The usefulness of our rule-mining algorithm in medicine is straightforward because it allows us to identify non-invasive biomarkers of ongoing renal injury and it could facilitate the generation of hypotheses about the CKD pathogenesis. Indeed, searching for local overdensities in an m-dimensional space, explained by easily interpretable rules, is thus seemingly ideal for generating hypotheses for large datasets to unravel the inherent complexity in biological systems. This proof of concept study will pave the way for further applications of our rule-mining algorithm in renal medicine, for example, for the prediction of the response to a therapy.
Methods Dataset Recruitment and sample collection. The Paris Descartes University ethics committee (the Patients Protection Committee) approved the study design. Before any medical consultation, the patients provided written consent and the cohort was then anonymized. Between 2013 and 2014, 110 consecutive CKD patients referred to the Nephrology Department of the Georges Pompidou European Hospital (Paris, France) for a kidney biopsy were included in the (see Table  2  for the baseline characteristics of the study cohort). The selection criteria for performing the patient biopsy were as follow: an estimated glomerular filtration rate (eGFR) < 60ml/min and/or proteinuria > 0. 5 g/l and/or the presence of hematuria. Urine and blood samples were collected, at the time of biopsy (mid-morning) and stored at -80˚C until routine clinical chemistry and metabolomic analyses.
). Urine 1 H NMR spectra were measured at 300K on a Bruker Avance II 500 MHz spectrometer (Brucker Biospin GmbH, Rheinstetten, Germany) equipped with a SampleXpress automation sample changer and a standard 5 mm BBI probe with Z-gradient. The spectra acquisition was based on a 1D Nuclear Overhauser Effect Spectroscopy (NOESY) pulse sequence with presaturation for water suppression. The parameters used for the pulse sequence were as follows: a relaxation delay of 1 s, a mixing time of 100 ms, an acquisition time of 1. 36 s and a 90 degree pulse length of 8 μs. Data points (32K) were collected during 64 scans with a spectral width of 20 ppm. The preprocessing of the urine 1 H NMR spectra was performed with MestReNova 8. 0 software. A line-broadening factor of 0. 3 Hz prior to Fourier transformation was applied.
The spectra were then phased, baseline corrected, and referenced to TSP. Each NMR spectrum (0. 16 to 9. 00 ppm) was reduced by an equidistant binning method with a bin width 0. 04 ppm to limit misalignment problems. The spectral regions corresponding to urea (5. 45 to 6. 48 ppm) and water (4. 48 to 4. 96 ppm) were deleted to remove variability because of the suppression of water. Then, an integral normalization (typically a constant integral of 100) was performed on the remaining 184 bins. The resulting matrix was then normalized with a pareto scaling giving an NMR spectra dataset of 110 individuals and 184 buckets. Finally, we discretized the 184 buckets with a 10 quantile-based binning.
For the classification step, we used the L2-regularized logistic regression leading to linear and smooth solutions. These models were first only applied on the metabolomic data and then on the multi-source data. The data used for the supervised analysis are available in Supporting Information (see S1 Dataset). Our rule-mining algorithm has been developed in-house in python and we used the famous sklearn python library for the chi2 test and the logistic regression. Rule-mining algorithm. A complete description of our rule-mining algorithm is detailed in Algorithm 1  [14]. We illustrated the process of this algorithm in Fig 8. It will exhaustively explore the multi-dimensional variable space in order to extract regions (i. e. hypercube) having over-concentration of a class of subjects (here, one of the two CKD severity stages).
We recall that these regions may be represented under the form of a rule: Rule : fVariableðsÞ conditionðsÞg where variable(s) condition(s) correspond to one value or a range/set of values of a given variable observed in the dataset. For example, 1D and 2D rules might be: 1D rule: {variable: 'bucket 2. 14';condition: bucket bins 1 to 4} and 2D rule: {variable 1: 'sex';condition: 'women' AND variable 2: 'bucket 3. 16';condition: bucket bin 6}. We represented these rules as binary variable spanning all the subjects, designated here as local variables, and used as input to the classifier (Fig  8A ). To identify the most discriminative rules, we used the following two quality measures: the rule modality size and the zscore. The rule modality size is the number of subjects in the rule having one of the two modalities of interest.
We selected rules with a rule modality size ! 10 to ensure robust and generalizable rules. We selected rules with a z-score!1. 96 corresponding to a significant difference between the proportion of the outcome modality in the rule and in the total population. Based on these candidate rules, we removed the redundant rules, i. e. , those sharing the same feature(s) and condition(s) and having worse quality measures. This step of our algorithm is called minimization (Fig  8B ). We can summarize the minimization procedure in two steps. The first step consisted in deleting rules that are included in larger rules with smaller z-score and the second step consisted in deleting rules covering smaller rules with higher z-score. Evaluation. Because we had a small number of subjects, we first splitted the dataset into a train (80%) and a test (20%) sets in which the percentages of samples of each class were preserved.
We performed this step 100 times to avoid being dependent on a particular random selection of a test set, which could lead to biased results. For each of these 100 models, we optimized the C parameter of the L2-regularized logistic regression with an inner cross-validation composed of 10 runs of 2-fold cross-validation to ensure a sufficient number of individuals in the less represented class. We computed the average F1 score over 100 splits, fixing C to be the most frequent value optimized in the inner cross-validation. Then, we assessed the statistical significance of our evaluation procedure using a permutation test of 1000 runs. This test evaluated whether our models, built on the original dataset, were significantly better than any other models obtained by randomly permuting the patients' output of the original dataset (eGFR < 60 ml/min/1.
Introduction There is an increasing interest in implementing data warehouses with NoSQL systems  [19]  including document-oriented systems such as MongoDB  [5]. NoSQL systems are an interesting alternative to relational databases (RDBMS), because they offer interesting scaling, replication and flexibility features. Until now, the different studies have focused on modelling issues, instantiation and OLAP cuboids. The management of complex hierarchies  [6, 12]  is an important issue in data warehousing. We introduce in this paper the management of complex hierarchies and summarizability issues with document-oriented data warehouses. In OLAP settings, it is common to analyse data on different dimension combinations. During analysis, we can drill-down or roll-up at different levels of detail using the hierarchy of dimensions.
4. 1! Experimental setup We propose two sets of experiments. The first set is about instantiating a data warehouse with the data model we proposed. We use for this purpose data from the Twitter case study. We load data and we study performance on a set of OLAP queries. The second set of experiments is about validating our algorithm for data summarization with complex hierarchies. We also compare our approach to two approaches from state-of-the-art namely: ─! The approach of Pederson and al  [12] : an approach that is considered as a reference approach for the summarizability issues ─! The approach of Hachicha and al  [6] ; that also uses a correction strategy when aggregating. These two approaches are meant for the relational model; we have adapted them for document-oriented systems. Hardware: The experiments are done on a cluster composed of 6 PCs, (4 core-i5, 8GB RAM, 2TB disks, 1Gb/s network), each being a worker node and one node acts as dispatcher.
Dataset: The data is obtained with the Twitter API for data streaming. Tweets are returned in JSON data format with each tweet having 67 data fields. We process tweets to follow the data model we have defined earlier. We also add a dimension called subject that has as attributes topic and category. These extra data is fictional and we introduce here arbitrarily non-strict hierarchy issues and non-covering hierarchy issues. Queries: We test our approach to implemente the conceptuel model to logical model, on 3 query sets. Three query sets are created with 3 queries per set. The query complexity increases from Q1 to Q3. Q1 involves 1 dimension, Q2 involves 2 dimensions and Q3 involves 3 dimensions.
6! Conclusions In this paper, we have studied complex hierarchies and summarization issues in the context of document-oriented implementations of data warehouses. First, we have proposed a set of rules to automatically translate the conceptual multidimensional schema at the level of logic NoSQL document oriented systems. Furthermore we have conducted a set of experiments to study the loading processes and interrogation. Then, we have tested our approach on datasets with Twitter tweets. Different volumes have been used. We have used MongoDB as the data base NoSQL. The first results show that our approach offers the best results and the best analysis. As for future work we hope to conduct investigations at the level of columns oriented models. These two latter use the versioned values a very interesting point for updating data warehouses.