dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2015). Knowledge base/graph: A Knowledge base is a database providing supplementary descriptive and semantic information about entities. The semantic information is contained in a knowledge graph, where a node represents an entity, and an edge represents a semantic relation. ). An edge between two entities E 1 and E 2 represents a url link from E 1 web page to E 2 web page. can be of any kind (directed, weighted,. ). See Fig. 2  for an example. Named entity linking (NEL): Given a named entity query, the purpose of named entity linking is to identify the corresponding ground truth entity (gold entity) in a database (knowledge base). For a detailed description of a concrete competition in entity linking, we refer to  (Ji et al. , 2014). Individual & collective linking: Linking can be done individually or collectively.
Contributions In this work, we provide a brief survey of existing methods for named entity linking. Then, we investigate a method for individual named entity linking. The first step of this method, refered as entity filtering, reduces entity candidates to top K entities for one query. The second step, refered as entity identification, aims at identifying the true entity among the remaining K candidates, based on a new graph-based algorithm. We include an experimental evaluation of our method with several datasets, with an analysis of the impact of parameter K, the ontology parameter T , and a detailed comparison with existing approaches. The implementation used for experiments is available at our repository 2. We do not include in this paper work on Fine-grained named entity recognition  (Ling and Weld, 2012).
Embeddings and deep architectures Recent advances in neural networks conception suggested to use word embeddings and convolutional neural networks to solve the named entity linking problem. (Sun et al. , 2015)  proposed to maximize a corrupted cosine similarity between a query, its annotated gold entity and a false entity. An example of learning representations for entities using a neural architecture is achieved in  (Yamada et al. , 2017) , a linking system based on the similarity of average of pre-trained entity embeddings has been proposed  (Yamada et al. , 2016) , with a O(QE 2 ) complexity. Finally other architectures have been proposed  (Sil et al. , 2018; Raiman and Raiman, 2018) , the latter using a fine-grained ontology type system and reaching promising results on several datasets.
Datasets, entity types, and ontology: We used CONLL and NIST TAC-KBP 2009-2010 as datasets. Each query contains its gold entity id and type. TAC-KBP: the corresponding knowledge base is composed of 818741 entities. TAC09 contains 1675 test queries, and TAC10 1074 for train and 1020 for test. CONLL/AIDA is composed of 22516 queries for training and 4379 queries for test. The other methods, mainly deep learning (DL) in Table  1  use millions of training examples from Wikipedia's anchor links and corresponding entities. In our method, we did not use this additional training data, but only those provided by the original challenges. Also, we considered a more recent Knowledge base (Wikipedia 2016 dump with 2880838 entities) since the original Wikipedia 2010 dump is not available anymore. The ontology we considered is available on DBPedia 1.
We provide the script that builds the complete knowledge base and ontology in our repository. To generate fine-grained ontology knowledge bases, we describe the procedure (along with the code) in our repository. We must remind that our method does not include fined-grained entity recognition from the queries: we suppose this given as input in the data. For the implementation of graph kernels, we used the GraKeL software library  (Siglidis et al. , 2018).
Results: We compare our methods with most performing baselines. Table  1  sums up our experimental results (averaged P @1 is also referred as accuracy  (Sun et al. , 2015) ). We included standard deviation of the accuracy, but could not include psignificance of our method, due to the difficulty to reproduce other baselines experiments (no source code is publicly available, or filtering method is not detailed). Our method yields remarkable accuracy on TAC09 dataset, CONLL/AIDA and TAC10 datasets. It performs better than any existing graph-based methods, outperforms all existing methods on two NIST TAC09 and TAC10, and is competitive with state-of-the arts methods on CONLL/AIDA. We also report impact of parameter K on average precision P @1 (accuracy). Results are in Fig. 5. Low values of K, corresponding to limited exploration, leading to decreasing accuracy.
Conclusion In this paper, we proposed a new methodology concerning the problem of named entity linking. First, we presented an entity filtering algorithm to return entity candidates that improves over trivial association rules. Then, each entity candidate is matched with a new representation built on a subgraph centered on their node. These representations use information contained in the ontology of the knowledge base. Finally, we used standard supervised learning to identify entities in the top candidates from filtering. We showed experimentally with standard datasets that named entity linking systematically improves over filtering using graph-based identification (for 2 ≤ K ≤ 10), up to 3%. Our experiments show that our method is competitive with state-of-the-art, and is stable with respect to K and T , has a linear complexity and reasonable experimental computing time.
Large scale technologies produce massive amounts of experimental data that need to be investigated. To improve their biological interpretation we have developed ClueGO, a Cytoscape App that selects representative Gene Onology terms and pathways for one or multiple lists of genes/proteins and visualizes them into functionally organized networks. Because of its reliability, userfriendliness and support of many species ClueGO gained a large community of users. To further allow scientists programmatic access to ClueGO with R, Python, JavaScript etc. , we implemented the cyREST API into ClueGO. In this article we describe this novel, complementary way of accessing ClueGO via REST, and provide R and Phyton examples to demonstrate how ClueGO workflows can be integrated into bioinformatic analysis pipelines.
Introduction High-throughput technologies produce large amounts of experimental data that need to be investigated to gain insights into biological processes. Systemic approaches for data integration, analysis and visualization were developed to reflect not only individual biological components, but also their interactions in pathways and networks. Software tools that perform such type of analyses in an automatic way are more and more needed. Cytoscape  (Shannon et al. , 2003)  is a major computational platform to visualize and analyze networks. Cytoscape Automation (https://github. com/cytoscape/cytoscape-automation) enables scientific workflows written in many languages and scales Cytoscape to large datasets and pipelines. The Cytoscape programmatic interface for this is CyREST  (Ono et al.
, 2015). We have contributed to the Cytoscape App collection  (Saito et al. , 2012)  with ClueGO  (Bindea et al. , 2009)  and CluePedia  (Bindea et al. , 2013)  apps that are broadly used by the scientific community to enhance the interpretation of biological data  (Mlecnik et al. , 2018). Within ClueGO, representative gene ontology (GO) terms  (Ashburner et al. , 2000)  as well as KEGG  (Kanehisa et al. , 2002) , WikiPathways  (Pico et al. , 2008)  and Reactome  (Croft et al. , 2011)  pathways are integrated into a functionally organized network. Furthermore, ClueGO can compare the biological role of several lists of genes/proteins. To perform multiple analyses in complex workflows can be time consuming and prone to errors. We have thus enabled the cyREST Application Programming Interface (API) in ClueGO, to allow scientists programmatic access to functional analyses.
ClueGO functional analysis via cyREST The Cytoscape App Manager (http://cytoscape. org/) allows the automatic download of the latest version of ClueGO from the App Store V C The Author(s) 2019. Published by Oxford University Press. (Lotia et al. , 2013). The yFiles Layout Algorithms App should be installed as well. ClueGO is written in Java programing language implementing the OSGi interface of Cytoscape. Starting with version 2. 5. 0 ClueGO implements the cyREST core plugin API and provides programmatic access to its functionality (Fig. 1A ). ClueGO features REST enabled can be explored in the cyREST API Swagger (Fig. 1B ), accessible via the Cytoscape menu, Help ! Automation ! CyREST API. ClueGO can be hence accessed with both the graphical user interface of Cytoscape as well as programmatical through cyREST (Fig.
ClueGO analysis steps The four main steps of a typical ClueGO analysis and additional optional REST enabled features are shown in Supplementary Table  S1. The ClueGO-REST enabled functions are accessed by a URL that is built up by the host address (e. g. localhost), the port (e. g. 1234) and the feature requested by the user. The HTTP request return types are then encoded as JSON, tab delimited text or binary data. The selection of an organism is the first step. Human and mouse data sources are included by default in ClueGO and more than 200 other organisms are available for download. The organism to analyze has to be set e. g. human:/v1/apps/cluego/cluego-manager/organisms/set-organism/'Homo Sapiens'. The second step requires the upload of one or several lists of genes/proteins to analyze.
ClueGO automatically recognizes multiple identifier types based on information from NCBI (NCBI Resource Coordinators, 2018), UniProtKB (The UniProt Consortium, 2017) and Ensembl  (Aken et al. , 2017)  databases. Different colors and shapes are automatically attributed to the clusters, to visualize them on the network. In the third step representative GO terms and pathways are selected using predefined filters based on the number of associated genes found from the uploaded list, their percentage from the total number of genes of the term or the GO tree level. Additionally, GO terms can be selected based on particular evidence codes of the geneterm associations. The significance of the pathways and their similarity in terms of associated genes are automatically mapped on the network and illustrated in different interchangeable visual styles.
R and Python ClueGO analysis examples with one or two lists of genes are provided as Supplementary Material. B and NK cell genes  (Critchley-Thorne et al. , 2007; Edgar et al. , 2002)  were analysed, and results are illustrated as a network of pathways showing either functional groups (Supplementary Fig. S1 ) or the origin of the genes in the two lists (Supplementary Fig. S2 ). All available ClueGO-REST endpoints are illustrated, including optional features.
Funding This work was supported by grants from  INSERM ('Integrative Cancer Immunology', 'Cancer et Environnement', 'Heterogeneity of Colorectal and Liver Cancer' (HETCOLI) , the  National Cancer Institute of France (INCa) ,  Canceropole Ile de France ,  MedImmune ,  AstraZeneca , the Transcan ERAnet European Project [Grant number  TRANS201401218 ],  La Ligue  contre le Cancer, the  Qatar National Research Fund (QNRF) ,  Cancer research for personalized medicine (CARPEM) ,  Paris Alliance of Cancer Research Institutes (PACRI)  and  LabEx Immuno-Oncology.
Both the chains of inference and the ArSol database containing the field records that provide evidence for the initial propositions are visualized in a diagram structure. We rely on CIDOC CRM entities for ensuring the semantic interoperability of such publications within the Linked Open Data. Inference chains are mapped to CRMinf and ArSol records are mapped to CRM, CRMSci and CRMArcheo. Moreover, as part of the work carried out by the French Huma-Num MASA Consortium, a project is underway to allow the building of logicist publications starting from a graphical interface for describing the structure and content of propositions.
The excavation revealed an occupation from the 7th to the 19th century (Zadora-Rio,  Galinié et al. 1992 , Zadora-Rio, Galinié et al. 1995 and Zadora-Rio, Galinié et al. 2001). The field recording was based on the model used in the Tours excavations, inspired itself by the recording system developed during the 1960s in Great Britain, especially by Philip Barker for the excavations at Wroxeter and Hen Domen and by Martin Biddle and Birthe Kjølbye Biddle for the urban excavations at Winchester  (Galinié 2013). The computerization of the data was introduced in Rigny in 1990 in the form of a relational database, which constituted the first version of the ArSol database, developed since then by the Laboratoire Archéologie et Territoires, and upgraded by the addition of a second module for the processing of ceramics  (Galinié et al.
thearkeotekjournal. org), created by  Valentine Roux in 2007  for the publication of papers and reference data in the archaeology of techniques. Since 2011, thanks to the development of new web technologies, the SCD format has been entirely reprogrammed in XML TEI (Text Encoding Initiative) by the Pôle du Document Numérique (Digital Document Centre) of the Maison de la Recherche et des Sciences de l'Homme of Caen. It is in this XML TEI format, which offers new possibilities for navigation between text, logicist schematizations and online databases, that the publication of the Rigny excavation, initially prepared for publication in the Référentiels collection, is currently being processed (Zadora-Rio,  Galinié et al. , forthcoming).
The contribution of information technology to the accessibility of excavation data It is well known that excavation is an irreversible process and does not allow the experiment to be repeated: once the excavation is finished, the field recording becomes the archaeologist's primary source. In the 1980s, the development of computers allowed the development and even the multiplication of numerous databases for the recording of field data. The widespread use of the Internet and the improvement of information systems now enable access to these field databases and thus the possibility for any researcher to check the recorded data. Thus, in 1996, the United Kingdom set up the Archaeology Data Service (ADS, http://archaeologydataservice. ac. uk), York University's digital resource center providing access to all digitized archaeological records (databases, graphic documents, photographs, specialists reports, grey literature, etc.
). In addition, the journal Internet Archaeology (http://intarch. ac. uk/) has set up online publications with hypertext links refering to the data used in the text, even if the general design of the papers remains traditional. In France, the digitization and online availability of field data is a later phenomenon. Among the early, local initiatives is the database of the Laboratoire Archéologie et territoires in Tours: ArSol (Archives du Sol, http://arsol. univtours. fr), which is on-line since 2014 (Fig. 3 ). Since 2013, the MASA Consortium (Mémoire des Archéologues et des Sites Archéologiques, https://masa. hypotheses. org), belonging to the Very Large Research Infrastructure Huma-Num (https://www. huma-num. fr), has set itself the objective of assisting archaeologists in digitizing and making available their excavation archives by applying good practices such as the FAIR principles.
Towards semantic interoperability: mapping logicist publications to CIDOC CRM Using web technologies, and among them the XML TEI, we provide archeologists with a quick and easy access to the content of a logicist publication, the underlying reasoning being a part of this content. But our goal is also to provide to web applications the ability to deal with such knowledge. For the same purpose, we already extended the ArSol online web publication with a CIDOC CRM based SPARQL querying API  (Marlet, Curet et al. , 2016). As Gardin has pointed out, the logicist schematizations can be compared to knowledge-based systems combining the data (stored in a Fact base) with rules of reasoning (stored in a Rule base) through an inference engine  (Gardin et al. 1987 :27-55; Gardin 1987 :7). Due to the formalized structure of the logicist schematizations, the inference rules can be processed with the same tools as those used for the datasets.
Thus, by mapping the logicist propositions with entities of the CIDOC CRM and in particular with those of the CRMinf extension 1  , we can ensure a semantic interoperability of this publication within the Linked Open Data. To this end, we propose to map the Rigny publication to the CRMinf, the extension of the CIDOC CRM intended to be used as a global schema for integrating metadata about argumentation and inferencemaking, in the following way: (i) The field records of the ArSol database are mapped to the basic CIDOC CRM plus CRMsci and CRMarcheo extensions. Thus field data are potentially interoperable and can be available both as such and as evidence supporting scientific reasoning. (ii) Inference chains are mapped to the CRMinf extension (embedded in the CRMsci). According to the principles presented in Section 4, the initial propositions {P0} have been typed according to whether they are observation data, comparative data, or reference data corresponding to the background knowledge.
Conclusion The logicist framework set up by Jean-Claude Gardin in the 1970s was intended to provide a solution to overcome the growing imbalance between the flood of publications and our assimilation capacities, but it is only recently that the developments in computing and the web, the XML format and its widespread use, plus the semantic level, has allowed its implementation in publishing workflows. Thanks to the formal organization of arguments in logicist publications, they can be processed as datasets, that can be made available in the Linked Open Data, making them potentially interoperable. The presented current models and mappings are experimental and need to be applied to other logicist publications in order to assess their validity and their semantic interoperability efficiency. Moreover, the innovative aspects of Rigny's publication and, hopefully, of the forthcoming Iceramm's publication, need to be discussed before our propositions can be implemented for further excavation publications.
In this case, indirect measures are generally used. For insect species that disperse by flying, mark-release-recapture experiments and flight mill experiments are commonly used  (Turchin 1998 , Martí-Campoy 2016). However, each method has its own bias: data recorded on flight mills represent artificial flight performance and usually overestimate dispersal capacities while data recorded in mark-releaserecapture (hereafter MRR) experiments represent interception distances and are limited by the number of traps and the distance between the release point and the furthest trap, thus resulting in underestimation of flight performance. Therefore, precisely estimating the dispersal capabilities of insects is often challenging. One of the biggest threats of pine forests across the world is the pine wood nematode (PWN), Bursaphelenchus xylophilus, as it can potentially kill a pine tree within a few weeks  (Suzuki 2002 , Webster & Mota 2008 , Vicente et al.
Calibration of the dispersal kernel with flight mill data The individual-based model describes the dispersal of immature and mature M. galloprovincialis beetles based on a several parameters (Table  1 ). The shape of the dispersal kernel (negative exponential model; see SM1) was fitted to the flight performance of both mature beetles (35 mature males and 26 mature females) and immature beetles recorded on flight mill (49 immature males and 49 immature females)  (David et al. , 2014 (David et al. , , 2015)). The beetles used in these experiments were collected in South-Western France, in a pine forest. Each immature beetle was tested during 10 minutes while each mature beetle was tested during 2 hours each week until its death. Hereafter, we present first the model parametrization for mature beetles, and then for immature beetles as the latter was derived from the former.
Dispersal kernel of mature beetles (age ≥ 20 days) Following the flight mill experiment with mature beetles, 77% of adults flew at least once and only these individuals were considered hereafter. Among these fliers, 61 % of flight mill trials showed flight activity long enough (30 s) to be considered dispersal flights (derived from  David et al. , 2014). In the simulation model, we considered that the daily probability of a mature beetle flying was pfm = 0. 61. Then, we considered a negative exponential kernel ( such as = 1 ) to determine the probability to disperse at a given distance (in meters) during one day  (Klein et al. 2006 ): = - (Eq. 1) where α is the mean daily dispersal distance (in meters). This function was fitted to the flight mill data  (David et al. , 2014). For that, we calculated the number of flight distances within intervals of 500 m.
2012 ). The number of species for which the dispersal capabilities have been recorded with different methods is relatively low and these measures generally differed (e. g. , from tens of kilometers versus hundreds of meters for M. galloprovincialis). However, it is not possible to compare directly these distances since they represent different measures of the dispersal distance. Data from flight mill experiments may provide overestimated flight performance but they allowed determining the dispersal kernel, i. e. the shape of distances distribution and the proportion of individuals able to fly long distances (e. g. , those mainly contributing to the population range expansion). Data from mark-release-recapture experiments provided an interception distance. Although they were based on individuals' dispersal in the field, the traps potentially captured individuals that could be able to disperse further.
Discussion Until now, there was no study comparing dispersal measures based on the same source of population. In this study, insects tested on the flight mill and in the mark-release-recapture experiments all originated from a maritime pine dominated forest of south-western France. Therefore, variability in dispersal distances may not be attributed to intra-specific variability. The dispersal model that we developed reveals that the distances recorded in the two types of experiment were actually in good agreement. The estimate of the daily mean dispersal distance (α = 2. 0 km) was consistent with the flight distance data estimated on the flight mill once per week (1. 93 km for females and 2. 14 km for males;  David et al. , 2014)  and with the distance flown between the release point and the most distant trap (1.
Acknowledgments We are grateful to  Inge van Hader ,  Elorri Segura ,  Fabrice Vétillard  and  Nils Préat  for their invaluable help with field work. We sincerely thank  Philippe Castagnone-Sereno ,  Manuel Mota ,  Géraldine Roux ,  Corinne Sarniguet ,  Xavier Tassus  for fruitful discussions in the working group on pine wood nematode of  ANSES.
Performance evaluation with sumo The professional transportation simulation tool named SUMO is chosen in this work to simulate the proposed algorithm, since it offers an open source package, which is deeply portable, applicable for the simulation, management and monitoring of each microscopic user (e. g. vehicle or passenger) in a transportation network  [14]. Moreover, SUMO supports an interface en Python, named Traci, which allows users to modify travel strategies during runtime  [15]. Therefore, a scenario with a specific road network is created, as shown in Fig. 1. There are 5 bus stops and three bus lines. A passenger leaving the starting location at time t wants to find an optimal path to the destination location.