dataesr/scientific-paragraphs-categorization huggingface dataset:
Second, we argue that the enormous amounts of data collected instantaneously in global business activities have the potential to be transformative in improving the human condition and provide solutions to many global and local issues concerned with a fair and equitable distribution of resources, wealth generation, and opportunity. However, the data need to be standardized through standards such as the W3C  [40]. This is because data utilization requires sources to be linked or interoperable to be accessible by end-users. This standardization and interoperability can be addressed by developing the knowledge graphs used by organizations such as Google, Uber, Amazon, Facebook, and Netflix. A novel approach is to create a Decentralized Knowledge Graph (DKG), which has been made possible by combining graph technology and blockchain technology.
Blockchain provides a consensus layer that verifies the authenticity of the data entering the DKG. It is gated by an identifier of source (i. e. , data provenance) and conforms to W3C standards. Smaller digital technology firms such as Slovenia-based OriginTrail have developed an opensource ecosystem comprising tools and protocols that connect blockchain to blockchain and blockchain to legacy. While OriginTrail has numerous case studies  [41, 42]  they have migrated towards implementing a DKG with full compliance to W3C and GS1 standards in supply chains. A significant development is the transition from stove-piped data held in organizational siloes to interoperable Web3-based ecosystems where multiple datasets can be discovered, queried, and integrated. Thus, this paper developed from the need to understand how the World Wide Web is currently using data and to define how the semantic web can use it.
Data Collection A preliminary search was conducted in the Scopus database on 1 June 2021, using the following search terms: interoperab* and "semantic web". In comparison to other academic databases (e. g. , Web of Science), Scopus is well-known for its extensive coverage  [55]  and intuitive tools that enable researchers to efficiently obtain and compile references from a sample of documents  [56]. Additionally, Scopus was chosen for its dependability and the volume of scholarly publications it indexes, including journals published by prestigious publishers such as Elsevier, Springer, Emerald Insight, IEEE; and, Taylor and Francis. To begin, the title, abstract, and keywords fields were filled with the search query. Three thousand six hundred and forty-one (3641) publications were returned as the initial result.
The returned dataset was filtered to exclude publications from the incomplete year 2021 and include only English-language documents to ensure a consistent set of publications. Additionally, duplicated publications or publications that lacked bibliographic data (e. g. , abstracts, keywords, or authors' names) were omitted. This search yielded 3511 publications extracted from Scopus for additional analysis. These publications' references and citations were saved in CSV and txt formats suitable for direct text mining.
Initial Statistics for the Analysis Regarding the initial statistics, we began by analyzing the evolution of research at the intersection of interoperability and the semantic web by plotting the selected set of publications according to their annual distribution. Following  [36] , we used several quantitative indicators to determine the impact and quality of research, including publications per author, citations per publication, and geographic origin of publication. To make data entry and processing more manageable, we analyzed the bibliometric data using the software package BibExcel. The strength of BibExcel comes from its compatibility with a wide variety of academic databases, including Scopus, and visualization tools, such as VOSviewer and Gephi. Authors who receive many citations are considered influential in their respective research fields.
Network Analysis Following an analysis of descriptive statistics, the intellectual landscape of research at the intersection of interoperability and the semantic web was represented by networks of various entities, including countries, academic institutions, and authors. VOSviewer was used to create these networks from the extracted bibliographic sources. Additionally, we conducted an in-depth analysis of the trends and relationships between the publications in our sample. We performed network analysis on bibliometric data and visualized the network structure of interoperability and the semantic web using the visual tool Gephi. The strength and significance of a network connection between two publications can be explained by the co-occurrence of keywords in the same publication or the co-citation of the same publication  [58].
Since a highly cited paper might not be highly prestigious, prestige is a critical impact indicator  [62]. In this regard, PageRank was introduced to measure both the citability and prestige of a paper  [63]. In this study, we identified the leading ten papers from each cluster by their PageRank measure to ensure the analysis of high-quality papers in each cluster. Gephi is used to generate the co-citation network from the BibExcel data. We used a procedure to generate the co-citation network cluster. For example, prior to using Gephi, bibliometric data were pre-processed, and a citation frequency threshold of 50 was established. Caution was exercised in this step, as a low threshold value may produce an excessive number of clusters, while a high threshold value may result in over-filtering.
If two keywords appear more frequently in the same publications, they have a stronger relationship. By creating the keyword co-occurrence network, we hoped to analyze the core content of the keywords used and better understand the research structure at the intersection of interoperability and the semantic web. VOSviewer was used to visualize the co-word network because it is highly recommended and compatible with the BiBExcel software package. The node's size corresponds to the frequency of each keyword, while the thickness of the edges indicates the frequency with which each pair of keywords appears in publications. This approach was used in the current study to describe and analyze the keyword co-occurrence network. Visualizing the associations between keywords made it possible to deduce the topics related to interoperability and the semantic web.
To generate the keyword co-occurrence network, the data were loaded into VOSviewer, and the density-based spatial clustering using the full-counting method was employed  [82, 83]. Additionally, the threshold of keyword occurrences was set at four. As a result, six clusters with different colors were obtained, as depicted in Figure  5. Each node in the figure corresponds to a keyword, and the node size reflects the co-occurrence frequency of the keyword. The density determines the distance between two nodes, and the higher this density, the shorter the distance between two keywords. The details regarding each cluster are presented in Table  8. From Figure  5 , it is evident that the largest cluster is the red one. This cluster focuses on the fundamental building blocks of the semantic web, including ontologies and languages.
The suggested approach helps to enhance data sharing and integration between BIM and GIS. It ensures the seamless integration of buildingand construction-related data via a new ontology based on the EXPRESS schema. More recently, ref. [118]  reviewed the integration of BIM and IoT devices and emphasized that this combination can realize the maximum potential of using semantic web technologies for the real-time monitoring and assessment of building performance. Even though the extant literature has highlighted the potential of integrating BIM and semantic web technologies to meet the needs for storing, exchanging, and utilizing heterogeneous datasets, a particular focus on how to address information overload in IoT deployments and standardize construction process data, prefabrication data from suppliers, and tracking data from the IoT to comply with local codes and regulations in the construction industry is required  [118].
Furthermore, more research is needed to understand the role of semantic web technologies in serving as a critical foundation for building digital twins for the sustainability assessment of constructions  [119]. It is vital to look into more practical use cases for the research into the integrated use of BIM and the semantic web in building lifecycles  [39]. Information integration and management require standards to minimize the cost and time of sorting large and heterogeneous datasets and ensure the effective development of IoT-and BIM-enabled smart environments  [118].
Unlike other semantic web service interoperability initiatives, this project is realized using a new type of XML schema derived from an ontology, aiming to define the biological intent and syntax of the data passed into and out of a service. The authors of  [121]  present Semantic Automated Discovery and Integration (SADI), a lightweight approach to simplify the discovery and consumption of semantic web services in bioinformatics and other scientific domains based on foundational web standards. The suggested approach enables bioinformatics software development with new interoperable and integrative behaviors. It helps accurately model the services and the end-user needs for the automated discovery of key services, the integration of the resulting data, and the automation of service pipelines.
Semantic Web Languages The last cluster is concerned with semantic web languages such as RDF. Only four publications belong to this cluster. In the first study, ref. [134]  presents RDF123. This highly flexible open-source tool translates spreadsheet data to RDF and enables users to create their mapping intuitively and obtain much richer spreadsheet semantics. The authors of  [135]  propose a new approach to create ontologies based on table analysis. Later, ref. [136]  described how to automatically infer the intended meaning of tables and represent them in an RDF-linked data format, thereby enhancing search, interoperability, and integration. The techniques implemented on tables are supported by a new Semantic Message-Passing Algorithm, which utilizes linked open data knowledge to enhance available messagepassing schemes.
Opportunities for future research on semantic web languages exist, and scholars should propose affordable mechanisms for assessing the interoperability of the semantic web and increasing the filtering of extraneous RDF data  [137]. In the future, it may be interesting to develop systems that can boost RDF applications in different areas, such as supply chain management  [138]. Moreover, additional experiments with real datasets are recommended to reduce the evaluation cost of the spatial component in RFD queries  [139]. Based on the results of this section and the clusters from bibliographic coupling (Table  10 ), important research gaps related to semantic interoperability need further discussions and investigations (see Table  11 ).
Discussion, Limitations, and Future Research Given the exponential growth of the literature on the semantic web and interoperability and the dearth of comprehensive and systematic analyses of this subject, there is a pressing need to map the current state of this knowledge. Thus, this study was motivated by a desire to visualize and map the structure and scope of research at the intersection of the semantic web and interoperability, thereby tracing the evolution of this field of knowledge and revealing its intellectual structure. We analyzed 3511 publications selected from the Scopus database over two decades using bibliometrics as a proven and objective methodological approach. We obtained objective results by combining several bibliometric techniques. This is in contrast to subjective techniques, as argued by  [181] : "an arbitrary selection of evidence is often not fully representative of the state of the existing knowledge, and the selection of some studies over others ultimately leads to what is known in statistic analysis as a sample selection bias".
We present a broad picture of the research on the semantic web and interoperability using concepts from systematic network analysis, thereby overcoming the inherent problems with conventional methodological approaches, which frequently ignore the dynamics and relationships between authors, publications, and the outlets in which research works are published. Similarly, ref. [182]  asserts that this type of relationship is critical for comprehending the intellectual foundation and evolution of knowledge over time in a scientific field. There is still much to do in obtaining a functional semantic web that will operate across all knowledge. The schematic of the ontology pyramid demonstrates the current state of the art. By examining all studies on the semantic web and interoperability that have been indexed in the Scopus database since 2000, this article conducted the domain's first comprehensive bibliometric analysis, as far as we are aware.
With a wide array of sensors, and strong communications infrastructures, it is possible to gather numeric, text, and video-based data in large volumes relatively cheaply. This offers businesses and other organizations the possibility of greater insights into areas such as business performance and customer experience. However, this vision is difficult to realize, often because of the semantic and interoperability challenges between different sensors and systems discussed in this paper, which is why researchers have strongly focused on this topic. Despite the contribution of this study, several limitations exist. In contrast to  [25] , which used several data sources, we used a single database. As a result, in comparison to other databases such as Web of Science (WoS) and Google Scholar, citations and relatedness between publications will be more conservative.
As a result, future studies may include additional databases to ascertain the validity of our findings. Nonetheless, it is worth noting that the vast majority of publications indexed in databases such as WoS and Scopus are dual-indexed in these two databases. Second, we limited our search to publications written in English. As a result, the scope of coverage may be reduced; consequently, future studies could consider publications in other languages, particularly the languages of the most productive countries (Germany and Italy), in order to assess the generalizability of this study's findings across languages. Third, the study's search query may have missed potentially relevant studies. Researchers may wish to extend the search strings to include a broader list of keywords related to the semantic web and interoperability to address this shortcoming.
I. INTRODUCTION Over time, the semantic Web has developed significantly, relying on a series of W3C standards, the foremost of which are RDF (Resource Description Framework), SPARQL (SPARQL Protocol and RDF Query Language) and OWL (Web Ontology Language). As a result, the use cases have diversified widely in the domains of academia, research and industry. The benefits of the semantic Web are diverse and revolve around three axes  [1] : interoperability between systems, notably through the addition of standards allowing the creation and distribution of knowledge graphs through the Web. These graphs are linked together when they describe the same resources or use the same vocabularies. Linked data is therefore one of the most important outcome of semantic Web technologies. The Linked Open Data (LOD) is a set of RDF knowledge graphs published and freely accessible on the Web; they are of various natures relating to many domains such as social networks, science, media, and publications.
The LOD-Cloud catalog  1  reports that the growth in the number of integrated datasets has been steadily increasing with very significant increases between 2014 and 2017. Ontologies are the keystone of the semantic Web as they not only enable this linkage of knowledge graphs over the Web but also logical inference over knowledge graphs, i. e. , the automatic deduction of implicit statements in a graph based on the axioms of ontologies. An ontology has several definitions from both a philosophical and a computational perspective  [2] : "An ontology is a formal and explicit specification of a shared conceptualization" is a concise definition highlighting its purpose. An ontology is composed of a set of classes and a set of relations representing the concepts of the domain knowledge, a set of individuals that are instances of classes, and a set of axioms involving classes, relations and individuals and capturing domain knowledge.
In this paper, we present three novel contributions to overcome these limitations: A. a multi-threading system to parallelize OWL axioms assessment, especially the computation of exceptions to axioms, B. an extension of the original heuristic to avoid redundant computation, with an explanation of the computational problem, C. an optimization of SPARQL query chunking relying on an extension of SPARQL 1. 1 Federated Query to automatically iterate a SPARQL federated query service call. In order to assess these contributions, we tested the integrity of the results and analyzed their impact in term of CPU time saving, using the previous work  [12]  as a benchmark. The source code of the project is available on GitHub. 2  This paper is organized as follows: In Section II we summarize the principles of the approach to extract OWL axioms from RDF data to the improvement of which we contribute.
II. PRELIMINARIES A. OWL SubClassOf Axioms Six categories of OWL axioms can be distinguished: Class expression axioms (SubClassOf, DisjointClasses,. ), Object property expression axioms (SubObjectPropertyOf, DisjointObjectProperties,. ), Data property expression axioms (SubDataPropertyOf, DisjointDataProperties,. ), Datatype definition axioms (DatatypeDefinition), Keys axioms (HasKey) and Assertion axioms (ClassAssertion, Object-PropertyAssertion). In this paper, we focus on the SubClassOf class expression axiom  [13]. Its OWL functional-style syntax is SubClassOf(C D), where C and D are OWL classes occurring in an RDF graph, e. g. dbo:Organisation, dbo:Work, dbo:Plant in the DBpedia knowledge graph with dbo the prefix denoting the DBpedia ontology namespace. Using the SHOIQ description logic syntax, the notation C ⊑ D highlights the inclusion of C into D, i.
The advantage of this operator is twofold: on the one hand, it makes it easier to code the iteration and chunking of SPARQL queries; on the other hand, the iteration and chunking are delegated to the SPARQL engine, which in general is more effective at performing them than the user code calling the engine several times. The algorithm of this operator is described in Algorithm 1. It is implemented into the Corese 3 semantic Web factory  [16]. By using this novel loop+page operator, we propose another algorithm to compute the exceptions to a SubClassOf sol ← sol ∪ res 10: end for 11: return sol <C> <D>axiom. Both the query to retrieve the classes potentially containing exceptions (Figure  6 ) and the query to retrieve the exceptions (Figure  7 ) are SPARQL federated queries using the parameters loop and limit in the URL of the remote query service in the SERVICE clause.
IV. EXPERIMENTS In order to evaluate our contributions, we carried out the scoring of 722 candidate axioms against an RDF dataset extracted from DBpedia 3. 9 comprising 463,343,966 triples and 532 OWL classes. This experiment has two objectives: on the one hand, to show that our optimizations give results that are faithful to the initial results achieved with the original heuristic presented in  [12] , and, on the other hand, to highlight the computation time savings obtained. The experiments were performed on a server equipped with an Intel(R) Xeon(R) CPU E5-2637 v2 processor at 3. 50GHz clock speed, with 172 GB of RAM, 1 TB of disk space running under the Ubuntu 18. 04. 2 LTS 64-bit operating system. As it can be seen in Fig. 8 , when using our optimization, the computation time is significantly reduced, with a maximum computation time reduced from 71,699 to 489 minutes.
3 https://github. com/Wimmics/corese Algorithm 1 Iterate and page a SPARQL query 1: sol ← {} 2: q ← the body of a SPARQL query 3: for i=$start; i<=$until; i++ do 4: q ← q+LOOP=$true&LIMIT=$limit&OFFSET=i*$limit 5: res ← eval(SERVICE url{q}) 6:if len(res) == 0 then.
The calibrators were also inspected for signatures of binarity to ensure only single stars were observed. In order to improve the (u, v)-coverage, we grouped the individual measurements into epochs. However, this proved to be difficult due to the rapidly changing orbit of the system. Based on the literature RV orbital period of ∼183 days  (Pogodin et al. 2006) , a variation of about one degree in position angle (PA) per day is to be expected. Considering that the PA uncertainties in our binary model fits are on the same order of magnitude as this (see Table  3 ), each epoch should include data from at most.
We used two fitting approaches: ORBITX code. This code 6  (Tokovinin 1992) fits orbits using both astrometric and RV data simultaneously. We modified the ORBITX code to account for uncertainties in both ρ and θ, a feature absent from the original code, which only uses uncertainties on ρ. Grid-search algorithm. We used the grid-search algorithm developed by  Kraus et al. (2009)  to construct a grid of orbital solutions in the P, T 0 , and e parameter space, where the remaining elements a, i, Ω, ω are determined from the Thiele-Innes elements. We explored the parameter space around P = 0. 480. 1. 100 yr, T 0 = 2019. 5. 2020. 5 yr (step sizes of 0. 001 yr), and e = 0. 100. 0. 600 with a step size of 0. 001, and selected the solution with the lowest combined residuals in RV and astrometry. We then repeated the process with smaller step-sizes around the initial solution (a factor of ten for all parameters, =0.
7a). The expected Silicate dust sublimation radius (R s ) can be estimated with R s = 1. 1 Q R L 1000 L 1/2 T s 1500 K -2 au, ( 4 ) where L is the bolometric luminosity of the star(s) irradiating the disc and T s is the dust sublimation temperature A146, page 9 of 24 Notes. Column (2) gives the RV fit obtained by  Pogodin et al. (2006). Columns (  3 ) and (  4 ) give the best-fit orbital solution including both RV data and the astrometry data, using the ORBITX and the grid-search methods, respectively. These solutions were derived using the background geometric model described in Sect. 3. 1. 1. Grid-search method ORBITX method Interferometric data Fig. 7. Astrometric orbit solutions derived using both the ORBITX code (blue line) and the grid-search code (red line). The primary star is kept fixed at the origin, and the x and y axes show displacement in right ascension and declination, respectively.
Acknowledgements. We acknowledge support from an  STFC  studentship (No. 109106G ), an  STFC   Consolidated Grant  ( ST/V000721/1 ), and  European Research Council   Starting Grant  " ImagePlanetFormDiscs " (Grant Agreement No. 639889 ) and  Consolidated Grant  " Gaia-BIFROST"  (Grant Agreement No. 101003096 ). Travel support was provided by  STFC PATT  grant  ST/S005293/1. MIRC-X received funding from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation programme  (Grant No. 639889 ). JDM acknowledges funding for the development of  MIRC-X  ( NASA-XRP NNX16AD43G ,  NSF-AST 1909165 ) and  MYSTIC  ( NSF-ATI 1506540 ,  NSF-AST 1909165 ). This research has made use of the VizieR catalogue access tool,  CDS, Strasbourg, France. The original description of the VizieR service was published in Ochsenbein et al.