dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Open data are an open window to the world, accessible to the greatest number of users. Retrieving information and knowledge comes with significant challenges when trying to avoid transforming this opportunity into a disorganized and indigestible mass of data through a scattergun approach. This is why, in parallel with the technological challenges, we are particularly attentive to the needs of users as varied as a scholar or scientist, company director, policy maker, student or simply a citizen who does research for his or her own pleasure. The aim of the GOTRIPLE platform (developed by the TRIPLE project, https://www. gotriple. eu/) is to make it much easier for scientists, citizens and business organizations to access scientific publications, data, data processing platforms and data processing services and therefore to benefit from Open Science.
The platform, for which development process is on-going, will provide linked exploration thanks to aggregators such as  (1)  the ISIDORE search engine (a large-scale discovery service, developed by Huma-Num since 2009 (https://isidore. science/) and (2) a variety of connected innovative tools, which include visualizations, a web annotation service, a trust building system, a crowdfunding tool and a recommender system. Through a user-centric approach and a set of methodologies that will be described in Section 3, our main objective is to enable researchers to discover and reuse SSH data macro-typologies, related not only to publications, but also to people (researchers) and projects. The TRIPLE solution supports Open Science principles, especially Open Access and Findable, Accessible, Interoperable and Re-usable (FAIR) data.
Results Although TRIPLE is still at the early stage of the development, thanks to a consortium of experts in their fields (researchers, data engineers, and staff from small and medium enterprises in the field of Information and Communications Technology), the project has already achieved some tangible results, communicated via deliverables and other working papers. Since the platform will not be completed until 2023, the results presented here are preliminary. The first tasks have already yielded results confirming the need for a new discovery platform dedicated to Social Sciences and Humanities. Described here are the three preliminary results: Result 1: Definition of the main features of the GOTRIPLE platform: It aims at meeting the needs of researchers and other stakeholders by allowing researchers to make their way through millions of documents and bring together members of the scientific community from different fields, countries and communities in research projects to foster collaboration across the frontiers of countries and disciplines and increase the impact of research in societal issues.
go-fair. org/fair-principles/) The first of the FAIR principles, Findability, is at the heart of the building of a discovery platform. Technically, findability is supported by the use of Persistent Identifiers (PIDs), either harvested from the providers' repositories or generated by the platform, for each searchable element; PIDs are registered in the metadata record, while rich minimal metadata facilitate data discovery thanks to the establishment of a TRIPLE model using schema. org. Concerning Accessibility, while all the previous findability features will be part of the search interface, data and metadata will be also accessible through free, open and documented protocols, namely: OAI-PMH (Open Archives Initiative Protocol for Metadata Harvesting), SPARQL (Protocol and RDF Query Language) endpoint, and APIs (Application Programming Interface).
Concerning interoperability, GOTRIPLE will tag variable-level information in the most relevant open standards for SSH i. e. , in the Data Documentation Initiative (DDI), Text Encoding Initiative (TEI), Metadata Encoding and Transmission Standard (METS), and Metadata Object Description Schema (MODS). Metadata records produced by GOTRIPLE will be published using the following standard vocabularies: Component MetaData Infrastructure, Dublin Core Metadata Element Set and DCMI (Dublin Core Metadata Initiative) Metadata Terms. Moreover, metadata records published in RDF (Resource Description Framework) will use the following linked open data vocabularies: the Data Catalog Vocabulary (DCAT), Open Digital Rights Language (ODRL), DDI-RDF Discovery Vocabulary (Disco). Lastly, TRIPLE will ensure the reusability of all the content that the project will create: the project grants Open Access to all project results, which will be published in Open Access journals (Gold road) and, when relevant, deposited in Open Access repositories (Green road).
All data and metadata (with the exclusion of the user research data) will be available in Open Access with open licenses allowing reuse. Furthermore, TRIPLE is working closely with the data providers in order to have a consistent licensing policy both for data and for metadata. The authentication portal is compliant with the other OPERAS services, with EGI services (as it relies on EGI check-in) and with the European Open Science Cloud (EOSC) AAI: Such EOSC Federation Services include, but are not limited to, the Authentication and Authorization Infrastructure Authorization (AAI), the Helpdesk, the Accounting Service and the Monitoring Service. Some, such as the Accounting Service, probably have little to do with TRIPLE's aim, but others, especially the Helpdesk or the AAI can be important additions to our platform.
The essence of this documentation was then transferred to a summary table and analyzed through qualitative content analysis, with a focus on offered platform features and functions, organizational insights, strengths and weaknesses as well as insights into usability and user experience. To complement the vantage points gained from the web-based competitor analysis, an interview study with general Open Science experts (3 participants) and executives from existing scholarly communication platforms (6 participants) was conducted. The qualitative interviews were designed as guideline-based expert interviews and evaluated through qualitative content analysis. Since TRIPLE's discovery tool aims at enabling users to find Open Access research data in the social sciences and humanities (SSH), we additionally analyzed the competitors with respect to access modalities and open content.
Advance Approach for Metadata Enrichment The GOTRIPLE platform needs to handle various kinds of data from different resources and repositories. To have a clear overview of the types of data described and standards used in the platform, methods have been developed for data access and exploitation, guidelines for intellectual property rights, ethics and privacy and disclosure risk management as well as data curation and preservation. All the data aggregated in the GOTRIPLE platform follows a process of standardization, classification and indexing. The data must be organized in such a way as to meet the needs of researchers. In order to make them accessible via a search engine, the core of the platform is trained to identify keywords, titles and descriptions in each of the 27 identified disciplines (MORESS categories, developed by the Mapping of Research in European Social Sciences and Humanities project, https://cordis.
europa. eu/project/id/HPSE-CT-2002-60060/fr) and in the nine languages. Around a hundred documents are therefore stored per discipline and language containing at least the following three metadata: Abstract, title and keywords. The collected metadata are enriched using controlled vocabularies to improve their quality and their discoverability by using training machine learning algorithms based on scholarly publications (journals, books, articles) and metadata. This first process will then lead to the definition of a TRIPLE data model with links and description of the different relations between metadata. As shown in Figure  2 , the enrichment consists of three different actions: • classification based on a training scholarly article database and using advanced methods based on statistics and language analysis; • normalization using thesauri; • semantic annotations with a disambiguation tool using thesauri and the Wikidata database.
It defines the process of collecting metadata until their exposition in the TRIPLE database through a two-fold approach: (1) Metadata provision by processing chains of aggregation platforms and (2) Semantic enrichment and resource linking by the TRIPLE pipeline. A delivery platform will be the communication interface between both processes (Figure  12 ). As a first phase, metadata are collected by aggregation platforms which are part or out of the consortium and dropped on the delivery platform. To collect and expose their metadata, these platforms use generic processing chains called BUILD. In accordance with the TRIPLE recommendations and with their agreement, the BUILD chains will deliver selected metadata on a delivery platform, under the monitoring of a scientific advisory committee. This implies that the project creates a model, called the TRIPLE data model, that the aggregation platforms might align with to be compliant with the discovery platform.
To start the project, the ISIDORE platform, developed by the coordinator of the project, had been chosen to be the first source of metadata, by using its processing chain "BUILD-I" (Figure  13 ). In the long run, to reach a satisfying level of exhaustivity, other BUILD chains will be added to cover the maximum of resources available in the whole SSH community worldwide. In a second phase, by a connection to the delivery platform, the TRIPLE pipeline will be able to collect, enrich and link the metadata corresponding to the three types of resources targeted by the project. The semantic enrichment will imply the creation of a TRIPLE thesaurus to align the vocabularies in the nine languages. The enriched and linked metadata will be then both stored in a tripleStore and indexed in the TRIPLE database and available through REST APIs (Representational State Transfer) for the Innovative Services to run their tools or for data providers to retrieve improved metadata.
The Kd values were calculated using the BIAevaluation software (Biacore) by fitting the data on antibody-concentration dependence of the fluorescent signal to Equation 1: R = ! "#$ • ' ( ) * ' (1) where the response, R, is the mean fluorescent signal of the tested IgG minus the mean fluorescent signal of a negative control antibody (rituximab, an anti-human CD20 IgG), C is the antibody concentration and Rmax is the fluorescent signal at infinite concentration. Kd values were calculated by fitting to the pooled data from triplicate experiments. Statistical methods developed for V gene usage comparisons. Pairwise comparisons of matrices of same dimension (corresponding to usage of VH genes, VL genes, or VH-VL combinations), as well as metric Multidimensional Scaling (MDS), were performed using Comat, an R tool available at https://gitlab.
ACKNOWLEDGEMENTS We would like to thank  Matthew Holsti  and  Will Somers  ( Pfizer ,  BioMedicine Design ), Cambridge, MA,  USA ) for advice on the technology development and critical reading of the manuscript;  Robert Nicol  ( Whitehead Institute, MIT Center for Genome Research, Cambridge, MA, USA ) for advice on barcoded sequencing; at  Institut Pasteur, Paris, France ,  Hugo Mouquet  for advice on antibody sequence selection,  Friederike Jönsson  for advice on flow cytometry analysis;  Tim Kirk  and  Sophie Foulon  ( ESPCI Paris, Paris, France ) for their help developing the barcoded hydrogel beads;  Samantha N. Stewart  ( HiFiBio Inc, Boston, MA ) for her help producing and analysing recombinant antibodies; the  Institut Pierre-Gilles de Gennes  ( IPGG, Paris, France ) for use of clean room facilities and the laser engraver (CII08, Axyslaser).
This work was supported by the  French Agence Nationale de la Recherche  ( ANR-14-CE16-0011  project  DROPmAbs ), by the  Centre d'Innovation et Recherche Technologique (Citech)  through the " Institut Carnot Pasteur Microbes & Santé " ( ANR 16 CARN 0023-01 ), by  BPI France  (OSIRIS and CELLIGO projects) and by the  French "Investissements d'Avenir" program  via the  CELLIGO  project and grant agreements  ANR-10-NANO-02 ,  ANR-10-IDEX-0001-02   PSL ,  ANR-10-LABX-31  and  ANR-10-EQPX-34. NGS was performed by the  ICGex NGS platform of the Institut Curie (Paris)  and the  Institut de Biologie Intégrative de la Cellule  ( I2BC ) platform ( Gif-sur-Yvette ) supported by the grants  ANR-10-EQPX-03  and  ANR-10-INBS-09-08  ( France Génomique Consortium ), by the  Canceropole Ile-de-France  and by the  SiRIC-Curie program -SiRIC Grant INCa-DGOS-4654.
CC acknowledges financial support from  CONCYTEC, Peru. PCH is a scholar in the  Pasteur -Paris University (PPU ) International PhD program and was also supported by the  'Fondation pour la Recherche Médicale ' (FRM;  FDT201904008240 ). KE acknowledges financial support from the  'Swiss National Science Foundation ' and the 'The  Branco Weiss Fellowship -Society in Science'.
Entity alignment (EA) finds equivalent entities that are located in different knowledge graphs (KGs), which is an essential step to enhance the quality of KGs, and hence of significance to downstream applications (e. g. , question answering and recommendation). Recent years have witnessed a rapid increase of EA approaches, yet the relative performance of them remains unclear, partly due to the incomplete empirical evaluations, as well as the fact that comparisons were carried out under different settings (i. e. , datasets, information used as input, etc. ). In this paper, we fill in the gap by conducting a comprehensive evaluation and detailed analysis of state-of-the-art EA approaches. We first propose a general EA framework that encompasses all the current methods, and then group existing methods into three major categories.
By dividing them into three groups and conducting detailed analysis on both intraand inter-group evaluations, we are able to better position these approaches and assess their effectiveness. (2) Comprehensive evaluation on representative datasets. To evaluate the performance of EA systems, several datasets have been constructed, which can be broadly categorized into cross-lingual benchmarks, represented by DBP15K [53], and mono-lingual benchmarks, represented by DWY100K  [54]. A very recent study  [24]  points out that KGs in previous datasets are much denser than real-life ones, and consequently it creates the SRPRS dataset with entity degrees following normal distribution. Despite of a wide choice of datasets, existing studies merely report their results on one or two specific datasets, making it difficult to assess their effectiveness in a wide range of possible scenarios, e.
g. , cross-lingual/mono-lingual, dense/normal, and large-scale/medium-scale KGs. In response, this study conducts a comprehensive experimental evaluation on all the representative datasets (i. e. , DBP15K, DWY100K and SRPRS), which comprise nine KG pairs, and analyzes in depth in terms of effectiveness, efficiency and robustness. (3) New dataset for real-life challenges. Existing EA datasets contain, for each entity in source KG, exactly one corresponding entity in the target KG. This, however, is an unrealistic scenario.
In practice, a KG is usually constructed from one single data source, and hence, it is unlikely to reach full coverage of the domain  [46]. To increase its completeness, a prevalent approach is to integrate knowledge from other KGs, which may contain extra or complementary information. For instance, a general KG may only involve basic information about a scientist, whereas more specifics (e. g. , biography and publication lists) can be found in scientific domain KGs. In order to consolidate knowledge among KGs, one pivotal step is to align equivalent entities in different KGs, which is termed entity alignment (EA)  [7] ,  [25]   1. In general, current EA approaches mainly tackle the problem by assuming that equivalent entities in different KGs possess similar neighboring structure, and employing representation learning methods to embed entities as data points in a lowdimensional feature space.
By performing effective (entity) embedding, pair-wise dissimilarity of entities can be easily evaluated as the distance between data points, in order to determine whether two entities match. While the direction is rapidly progressing (e. g. , over twenty papers have been published in the last three years), there is no systematic and comprehensive comparison of these solutions. In In real life, KGs contain entities that other KGs do not contain. For instance, when aligning YAGO 4 and IMDB, only 1% of entities in YAGO 4 are related to movies, while the other 99% of entities in YAGO 4 necessarily have no match in IMDB. These unmatchable entities would increase the difficulty of EA. Besides, we observe that the KGs in existing datasets use identical naming systems, and the baseline approach that relies on the string similarity between entity names can achieve 100% accuracy on all mono-lingual datasets.
Nevertheless, in real-life KGs, an entity is often identified by an incomprehensible id, and associated with one or several human-readable names. Therefore, different entities might share the same name. This obviously poses a problem for EA, as there is no guarantee that an entity with the name "Paris" in the source KG is the same as an entity with the name "Paris" in the target KG-simply because one might be the city in France and the other one a city in Texas. We thus consider that the existing datasets for EA are an oversimplification of the real-life challenges, disregarding the fundamental issues of unmatchable entities and ambiguous entity names. As a remedy, we propose a new dataset that mirrors these difficulties.
Entity resolution on KGs. Some ER approaches are designed to handle KGs and deal exclusively with binary relationships, i. e. , graph-shaped data. These approaches are also frequently referred to as instance/ontology matching methods  [49] ,  [50]. The graph-shaped data comes with its own challenges: (1) the textual descriptive information about entities is often less present, or reduced to its bare minimum in the form of an entity name; and (2) KGs operate under the Open World Assumption, in which the attributes of an entity may be absent in the KG although they are present in reality. This distinguishes KGs from classical databases, where all fields of a record are usually assumed to be present; and (3) KGs have additional predefined semantics. In the simplest case, these take the form of a taxonomy of classes.
Existing benchmarks. To evaluate the effectiveness of EA solutions, several synthetic datasets (e. g. , DBP15K and DWY100K) have been constructed by using the existing interlanguage and reference links in DBpedia. More detailed statistics of these datasets can be found in Section 4. 2. Notably, the Ontology Alignment Evaluation Initiative (OAEI) promoted the Knowledge Graph track  4. In contrast to existing EA benchmarks, where merely instance-level information is provided, KGs in these datasets contain both schema and instance information, which can be unfair for evaluating current EA approaches that do not assume the availability of ontology information. Hence, they are not presented in this article.
Extra Information Module Although the embedding learning, alignment and prediction modules can already constitute a basic EA framework, there is still room for improvement. In this subsection, we introduce the methods used in the extra information module. A common method is the bootstrapping strategy (also frequently called the iterative training or self-learning strategy), which iteratively labels likely EA pairs as the training set for the next round and thus progressively improves the alignment results. Several methods have been devised, and the main difference lies in the selection of confident EA pairs. ITransE adopts a threshold-based strategy, while BootEA, NAEA and TransEdge formulate the selection as a maximum likelihood matching process under a 1-to-1 mapping constraint. Some methods use multi-type literal information to provide a more comprehensive view for alignment.
AttrE and MultiKE). There is a growing tendency towards the use of entity names. GM-Align, RDGCN and HGCN use entity names as the input features for learning entity embeddings, while CEA exploits the semantic and string-level aspects of entity names as individual features. 5. In this work, we use the distance between entity embeddings and the similarity between entity embeddings interchangeably. Besides, KDCoE and the description-enhanced version of HMAN encode entity descriptions into vector representations, which are considered as new features for alignment. It is worth noting that multi-type information is not always available. Besides, since EA underlines the use of graph structure for alignment, the majority of existing EA datasets contain very limited textual information, which restrains the applicability of some approaches such as KDCoE, MultiKE and AttrE.
Datasets. We adopt three frequently utilized and representative datasets, including nine KG pairs, for evaluation: DBP15K  [53]. This dataset consists of three multilingual KG pairs extracted from DBpedia: English to Chinese (DBP15K ZH-EN ), English to Japanese (DBP15K JA-EN ) and English to French (DBP15K FR-EN ). Each KG pair contains 15 thousand inter-language links as gold standards. DWY100K  [54]. This dataset comprises two mono-lingual KG pairs, DWY100K DBP-WD and DWY100K DBP-YG , which are extracted from DBpedia, Wikidata and YAGO 3. Each KG pair contains 100,000 entity pairs. The extraction process follows DBP15K, whereas the inter-language links are replaced with the reference links connecting these KGs. SRPRS. Guo et al. [24]  point out that KGs in previous EA datasets, e. g. , DBP15K and DWY100K, are too dense and the degree distributions deviate from real-life KGs.
Therefore, they establish a new EA benchmark that follows real-life distribution by using the reference links in DBpedia. The final evaluation benchmark consists of cross-lingual (SRPRS EN-FR , SRPRS EN-DE ) and mono-lingual KG pairs (SRPRS DBP-WD , SRPRS DBP-YG ), where EN, FR, DE, DBP, WD, and YG represent DBpedia (English), DBpedia (French), DBpedia (German), DBpedia, Wikidata and YAGO 3, respectively. Each KG pair contains 15,000 entity pairs. A summary of the datasets can be found in Table  2. In each KG pair, there are relational triples, cross-KG entity pairs (gold standards, in which 30% are seed entity pairs and used for training), and attribute triples. Degree distribution. To gain insight into the datasets, we show the degree distributions of entities in these datasets in Figure  3.
The degree of an entity is defined as the number of triples an entity is involved in. Higher degree implies richer neighboring structure. In each dataset, since the degree distributions of different KG pairs are very similar, we merely present the distribution of one KG pair in Figure  3  in the interest of space. The (a) series of sub-figures corresponds to DBP15K. It is evident that the entities with degree of 1 account for the largest share, and with the increase of degree values, the number of entities fluctuates while it generally exhibits a downward trend. Noteworthily, the curve of the coverage approximates a straight line, as the number of entities changes subtly when the degree increases from 2 to 10. The (b) series of sub-figures corresponds to DWY100K. The structure of the KGs in this dataset is very different from (a), as there are no entities with degree of 1 or 2.