dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2009) , we find that this is not enough to correctly interlink the multiple vocabularies and ontologies developed by the community. We are integrating other state-ofthe-art ontology matchers such as YAM++  (Ngo and Bellahsene, 2012)  as well as designing specific mapping curation interfaces. At the same time, identifying and harvesting into AgroPortal the mappings already produced by the community is a huge task, not yet begun. (#9) We have not automatically linked databases of annotated agronomical data using ontology concepts (from within AgroPortal). While the original BioPortal has the NCBO Resource Index  (Jonquet et al. , 2011) , we plan to rely on external annotated resources such as AgroLD  (Venkatesan et al. , 2015)  to interlink with data. To store this information, we will build on our rich mapping model in AgroPortal as presented Section 4.
rd-alliance. org/groups/agrisemantics-wg. html datasets that use them such as the CIARD RING directory (http:// ring. ciard. net) (Pesce et al. , 2011) , as that was previously indexed with some of the VEST content. The CIARD RING can be queried via SPARQL or REST API and the links between vocabularies and datasets can therefore be retrieved by any system. Such a feature, has been requested and will be among the next features of AgroPortal. In the long-term vision, AgroPortal will directly query the CIARD RING, AgroLD, or any relevant data sources like Bio2RDF or Planteome, so that a user browsing ontologies can get direct access to the data to which these ontologies link. (#12) Although community feedback is an important aspect for working group and communities, we have not successfully engaged yet our user groups to add reviews, notes, or comments about the ontologies.
Conclusion In this paper we have presented AgroPortal, an open vocabulary and ontology repository for agronomy. We have discussed five use cases already using the portal to support their work on data interoperability, and demonstrated that beyond these use cases the portal offers services of value to the broader community. The thematic boundaries of the portal are evolving (agriculture also includes animals, and is strongly related to environmental science), and over time the community will communicate what they expect to find in such a repository. The community outreach challenge of such a project is huge. It involves identifying already existing resources, whether already shared or not, encouraging their developers to make them available, and finally harvesting them into the single ontology repository, capable of providing many services across the heterogeneous content.
In addition, the AgroPortal platform offers a terrain for pursuing important informatics and semantic web issues, such as semantic annotation, multilingual ontologies, metadata description, ontology engineering and alignment, and ontology recommendation, and will. Ultimately, we believe AgroPortal provides powerful services, standards, and information that will greatly facilitate the adoption of open data in agriculture and benefit the extended agronomic community, the semantic web and data science communities, and the biomedical community that in many ways laid the groundwork that AgroPortal now leverages.
Acknowledgment This work is partly achieved within the  Semantic Indexing of French biomedical Resources  (SIFRwww. lirmm. fr/sifr)project that received funding from the  French National Research Agency  (grant  ANR-12-JS02-01001 ), the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  701771 , the  NUMEV Labex  (grant  ANR-10-LABX-20 ), the  Computational Biology Institute of Montpellier  (grant  ANR-11-BINF-0002 ), as well as by the  University of Montpellier  and the  CNRS. We also thank the  National Center for Biomedical Ontologies  for their help and time spent with us in deploying the AgroPortal.
g. , Agrovoc, GACS, NALt). Distinguish between these labels in lexical-based services (search, annotation) Others IRSTEA develops vocabularies only in French 14 Dereference URIs for ontologies LovInra, Crop Ontology When opening in a web browser a URI created by INRA or CO, display the corresponding class or property page 15 Mechanism to identify and select the relevant ontologies for a given task LovInra, VEST Facilitate the identification of relevant agronomical ontologies for non-experts 16 Enable private access to ontologies during working and/ or development phases LovInra Access and test the AnAEE Thesaurus or GCAS before they release; work on certain versions of OntoBiotope not public in OpenMinted project 17 Export ontologies in different formats, including downgrading them to CSV Crop Ontology Breeders may need simpler formats, as they may not be able to use advanced semantic web formats 18 Store the project/ontology relationships VEST, AgBioData Select and maintain a list of ontologies used by model organism databases 3.
The Resource Description Framework (RDF) is the W3C language to described data. It is the backbone of the semantic web. SPARQL is the corresponding query language. By adopting RDF as the underlying format, AgroPortal can easily make its data available as linked open data and queryable through a public SPARQL endpoint. To illustrate this, the reader may consult the Link Open Data cloud diagram (http://lod-cloud. net) that since 2017 includes ontologies imported from the NCBO BioPortal (most of the Life Sciences section).
This study concentrates on extracting painting art history knowledge from the network structure of Wikipedia. Therefore, we construct theoretical networks of webpages representing the hyper-linked structure of articles of 7 Wikipedia language editions. These 7 networks are analyzed to extract the most influential painters in each edition using Google matrix theory. Importance of webpages of over 3000 painters are measured using PageRank algorithm. The most influential painters are enlisted and their ties are studied with the reduced Google matrix analysis. Reduced Google Matrix is a powerful method that captures both direct and hidden interactions between a subset of selected nodes taking into account the indirect links between these nodes via the remaining part of large global network. This method originates from the scattering theory of nuclear and mesoscopic physics and field of quantum chaos.
From this study, we show that it is possible to extract from the components of the reduced Google matrix meaningful information on the ties between these painters. For instance, our analysis groups together painters that belong to the same painting movement and shows meaningful ties between painters of different movements. We also determine the influence of painters on world countries using link sensitivity between Wikipedia articles of painters and countries. The reduced Google matrix approach allows to obtain a balanced view of various cultural opinions of Wikipedia language editions. The world countries with the largest number of top painters of selected 7 Wikipedia editions are found to be Italy, France, Russia. We argue that this approach gives meaningful information about art and that it could be a part of extensive network analysis on human knowledge and cultures.
For various language editions of Wikipedia it was shown that the PageRank vector produces a reliable ranking of historical figures over 35 centuries of human history  [17] -  [20]  and a solid Wikipedia ranking of world universities (WRWU)  [17] ,  [21] ,  [22]. It has been shown that the Wikipedia ranking of historical figures is in a good agreement with the well-known Hart ranking  [23] , while the WRWU is in a good agreement with the Shanghai Academic ranking of world universities  [24]. At present, directed networks of real systems can be very large (about 4. 2 million articles for the English Wikipedia edition in 2013  [16]  or 3. 5 billion web pages for a publicly accessible web crawl that was gathered by the Common Crawl Foundation in 2012  [25] ). For some studies, one might be interested only in the particular interactions between a very small subset of nodes compared to the full network size.
For instance, in this paper, we are interested in capturing the interactions of nodes using the networks extracted from 7 Wikipedia language editions (FrWiki, EnWiki, DeWiki, ItWiki, EsWiki, NlWiki and RuWiki). We use the network datasets of Wikipedia 2013 described in  [20]. The selected nodes (their Wikipedia articles) are embedded in a huge complex directed network with millions of nodes. Thus, the interactions between these selected sets of nodes should be correctly determined taking into account that there are many indirect links between the webpages via all other nodes of the network. In previous works, a solution to this general problem has been proposed in  [26] -  [28]  by defining the reduced Google matrix theory. Main elements of reduced Google matrix G R will be presented in Section II.
Moreover, we provide hereafter, an analysis of the influence of top PageRank painters This paper introduces first the main elements of reduced Google matrix theory in Section II. Next, Section III presents the ranking and selection of painters based on the PageRank algorithm. In Section IV the reduced Google matrices are calculated and described for selected sets for seven different language editions. Specific emphasis is given to the very different English, French and German editions. Then, networks of friendship from direct and hidden interaction matrices are created and discussed in Section V. We show that the networks of friends completely capture the well-established history of painting by i) interconnecting densely painters of the same movement and ii) showing reasonable links between painters of different movements.
A. Top PageRank painters We are interested in this part in selecting the most influential painters representative of the seven investigated Wikipedia editions. Importance of nodes is measured in this selection process with the PageRank centrality. A Matlab script has been written to retrieve all the painters' names from the "List of painters by name" webpage  [30]  edited by Wikipedia that lists painters from all ages and various parts of the world. We have collected 3334 names. Next, we get their nodes' number in our network representation of each Wikipedia edition. Note that some names are not necessarily known for their painting art production (e. g. Hitler). Thus we have made a second check to remove such cases from our list of painters. This initial sorting leads to a group of 3249 distinct names for our 7 selected editions enlisted in  [31].
B. Global ranking of painters The above results demonstrate different cultural views on the importance of painters in the different language edition of Wikipedia. To get a global, multi-cultural importance of painters we use the approach proposed in  [19] ,  [20]. It defines a global rank Θ P with: Θ P = E (101 -R P,E ). (1) Here R P,E is the rank of the top 100 painters P in Wikipedia edition E retrieved with PageRank algorithm. The painters with the largest Θ P -score are the most important ones for E Wikipedia editions. Based on Θ P score, we have selected two different sets of painters for our investigations. a) Top 40 painters: From the Θ P -score calculated for the E = 7 Wikipedia editions of interest (e. g. EnWiki, FrWiki, RuWiki, DeWiki, ItWiki, EsWiki and NlWiki), we have selected the top 40 painters enlisted in Tab.
This shows that our network analysis captures realistic relations between painters. However, the above presentation takes into account only the opinion of the FrWiki edition with a dominance of French culture linked to French language. It is interesting to have the network structure which takes into account the opinions of all 7 editions. In fact the approach of the reduced Google matrix is well suited for this. Indeed, to perform the average over different cultures we take G R for 40 painters (size 40) and its components and take the average of these 7 matrices with equal democratic weights getting in this way the average G Rav and its average 3 components. Of course, after averaging G Rav still belongs to the class of Google matrices. The network structure obtained from G rr av + G qrnd av is shown in the bottom panel of Fig.
A. Datasets Another complementary study is presented here to visualize the influence of painters on countries. To analyze the relation between painters and countries of the world we construct a reduced Google matrix with N r = 80 nodes composed of the top 40 painters shown in Table  II  and the group of 40 countries listed in Table  IV. The painters are the ones having top Θ P -score for E = 7: EnWiki, FrWiki, RuWiki, DeWiki, ItWiki, EsWiki and NlWiki. Table II only lists short names, however, the full painter names together with their Θ P -score, birth country and life period are available as well in  [31]. The top 40 countries of EnWiki are presented in Table  IV. The names of countries are given by ISO 3166-1 alpha-2 code (see  [47] ).
C. Influence of painters on countries To analyze in a more direct way the world influence of painters we average G R matrix and its three components G pr , G rr , G qr over 7 Wikipedia editions that allows us to account for different cultural views on selected 40 painters of Table  II  and 40 countries of Table  IV. The reduced Google matrix G Rav averaged over these 7 different editions allows us to obtain a balanced view of various cultural opinions of Wikipedia language editions for a selected group of nodes representing Wikipedia articles. We determine the PageRank probability of this averaged G R matrix and compute its logarithmic derivative (sensitivity) with respect to a weight variation of a selected link going from a specific painter to a specific country. For instance, we vary the intensity in G R of the link going from Picasso to Spain, and observe the variation of PageRank for other countries.
ACKNOWLEDGMENT This work was supported by APR 2015 call of University of Toulouse and by  Région Occitanie  (project  GOMO-BILE ), MASTODONS-2017  CNRS  project  APLIGOOGLE , (see http://www. quantware. ups-tlse. fr/APLIGOOGLE/), EU CHIST-ERA   MACACO  project  ANR-13-CHR2-0002-06  (see http://macaco. inria. fr)and in part by the  Pogramme Investissements d'Avenir   ANR-11-IDEX-0002-02 , reference  ANR-10-LABX-0037-NEXT  (project  THETRACOM ); it was granted access to the  HPC resources of CALMIP (Toulouse)  under the allocation  2017-P0110. We would like to thank  Dr. Hanah Tout  from  Lebanese University  for her valuable comments and data validation.
Physiological recordings A custom-written scenario for the open source software OpenViBE  (Renard et al. , 2010)  was designed to automate the recording of EEG signals and was triggered during the task. EEG signals were recorded with a BiosemiTM Active Two 32-channel EEG system at 2,048 Hz. In the Biosemi TM system the ground electrodes used were two separate electrodes: Common Mode Sense (CMS) active electrode and Driven Right Leg (DRL) passive electrode. The EEG was recorded from 32 sites in accordance with the international 10-20 system: FC  3 ). These sites are localised around the primary motor cortex, the motor cortex, the somatosensory cortex and the occipital cortex, which allowed us to observe the physiological changes due to the task for the open eyes condition. The setup of the EEG channels was the same in both EC and EO conditions.
Signal pre-processing All offline analyses were performed using the EEGLAB toolbox  (Delorme & Makeig, 2004)  and Matlab2016a (The MathWorks Inc. Natick, MA, USA). The data were processed in General Data Format (GDF). First, raw EEG data were filtered, transformed, and Laplacian processing was applied to minimise the noisy EEG signals  (Perrin et al. , 1989). Then, EEG signals were resampled at 256 Hz and divided into 6 s epochs corresponding to 2 s before and 4 s after the mouse click for each run. Trials contaminated by excessive noise or movement artefacts were removed. Baseline was defined 2 s before each trial, therefore a specific baseline was chosen for the two conditions. Finally all runs were merged for a given condition for each subject. In this article, we chose the C 3 electrode because it corresponds to the motor area of the right hand control.
Numerical experiments with two image databases The usefulness of the NTF algorithm in the extraction of facial features for classification has been demonstrated in  [26, 50]. In  [26]  the NTF has been applied for feature extraction for face detection. The feature vector representing an image was the innerproduct between the factors. Those measurement vectors over positive (faces) and negative (non-faces) examples were fed into various classifiers, such as Support Vector Machines (SVM) and Adaboost. The MIT CBCL face repository has been used and it has been shown that the features extracted by the NTF factors generated the higher classification accuracy when compared to NMF and PCA. To examine the convergence behavior of the algorithm and the achieved decomposition accuracy, we performed decompositions of a set of example tensors.
In order to allow the comparison of decomposition errors achieved in decompositions of different tensors, we use the relative error given by C rel. = Y -ˆ Y F Y F , ( 32 ) where Y is the reconstruction tensor of Y from the estimated matrix factors A (1) , A (2) ,and A (3). For comparison a selection of four algorithms will be employed: The original NMF algorithm by  [43]  , the direct extension of the same algorithm to NTD by  [50]  i. e. higher-order nonnegative matrix factorization (HONMF), a conjugate gradient (cgP) algorithm  [56]  and an ANLS algorithm with BPP  [35]  (referred to as ANLS-BPP). All algorithms are used in a pure Matlab®implementation. NMF is a 2-way method, i. e. designed for second-order tensors (matrices), therefore the input tensors have to be vectorized. The preconditioned nonlinear cgP is an implementation specialized to third-order tensors, consequently a vectorization becomes necessary if the order of the input tensor exceeds 3.
For the evaluation and comparison of these algorithms, we use two sets of face images: The MIT center for biological and computational learning (CBCL) face database and IV 2 face database  [14]. The MIT CBCL face database contains 2429 gray scale face images with a resolution of 19 × 19 pixels. Forming a tensor of dimensions 19 × 19 × 2429 it is a good example of an input tensor of moderate size and heavily imbalanced dimensions. The face images of the CBCL database present a typical decomposition task and have been used in the evaluation of several publications dealing with matrix and tensor factorizations  [21, 27, 31, 42]. As the data set contains several images of the same person we have to assure by shuffling their order that they are not grouped together in the data tensor. To also perform tests on a fourth order tensor we selected 211 persons with at least 6 images available and formed a fourth order tensor of dimensions 19 × 19 × 6 × 211,where the first two ways correspond to the pixels, the third indexes the different images of one person and the fourth way corresponds to the different persons.
The IV 2 face database contains 4721 face gray scale face images with a resolution of 128 × 128 pixels. These images form a tensor of dimensions 128 × 128 × 4721 which presents an example of much larger size. On the machine with 4 Gigabytes of RAM which we used to perform our experiments, this tensor uses up almost half of the available memory. In Fig. 2  we display examples of reconstructed CBCL face images, as well as processing time and relative error, obtained with our algorithm and the 4 algorithms to compare with. The cgP is only used with 3D arrays and HONMF is the tensor extension of NMF. Both visual inspection as well as the reconstruction error and the processing time show that the NMF algorithm is superior to all NTF algorithms. This is due to the fact that NMF is especially designed for 2D-way arrays.
These images build the basis for the reconstruction. As can be seen, the basis images are sparse, i. e. a large number of entries is zero, because of the purely additive nature of non-negative decompositions. The obtained basis images show a similarity to the basis images obtained by other NTF and NMF algorithms, see  [21, 31, 68]. The tensor decomposition formed from IV 2 image database of dimensions 128 × 128 × 4721 is much more demanding. In fact, the HONMF and the cgP algorithms did not converge within 24 hours. However, the NMF, NsTEF and ANLS-BPP algorithms converged within 1 h. The exact processing time as well as the relative error achieved with each of the algorithms and the reconstructed  images are shown in Fig. 5. NMF achieved by far the best decomposition. The reconstruction error is almost one magnitude lower, and the reconstructed image shows much more similarity with the original than with both other algorithms.
Information retrieval from biomedical repositories has become a challenging task because of their increasing size and complexity. To facilitate the research aimed at improving the search for relevant documents, various information retrieval challenges have been launched. In this article, we present the improved medical information retrieval systems designed by Poznan University of Technology and Poznan University of Medical Sciences as a contribution to the bioCADDIE 2016 challenge-a task focusing on information retrieval from a collection of 794 992 datasets generated from 20 biomedical repositories. The system developed by our team utilizes the Terrier 4. 2 search platform enhanced by a query expansion method using word embeddings. This approach, after post-challenge modifications and improvements (with particular regard to assigning proper weights for original and expanded terms), allowed us achieving the second best infNDCG measure (0.
Introduction Biomedical research produces ever increasing amount of digital data, which is stored in a variety of formats and hosted in a multitude of different sites. These sites could be generated by original researchers, attached to journals as supplementary material, organized as datasets and kept in databases or repositories. The most common information source is literature in the form of indexed journals that in V C The Author(s) 2018. Published by Oxford University Press. electronic form reside of Pubmed platform or publisher portals. The article format has its advantage-ease of reading. Articles contain mostly unstructured information that is hard to use specialized processing, comparison, aggregation and integration. Therefore, we need transformation of this information into more structured form that can be stored in databases, collection and repositories.
This process requires development of useful data structures and indexing and extraction tools. Data is a set of values of qualitative or quantitative variables. Pieces of data are individual pieces of information. A dataset or collection of data often corresponds to the contents of a single database table, or a single statistical data matrix, where every column of the table represents a particular variable. Generic ontologies and metadata models designed for description of datasets, supplement domainspecific ontologies to describe the research field. The enormous amount of biomedical literature, the existence of data of different granularity and data heterogeneity, as well as the lack of common metadata, makes it difficult to selectively access increasingly complex relevant information. As pointed out by  (20) , 'A typical dataset available in, for instance, the gene expression repositories may contain a description, a list of keywords and a list of organisms.