dataesr/scientific-paragraphs-categorization huggingface dataset:
Objective and procedure The simulation study serves to develop the flexible model evaluation strategy and has two steps. First, data were simulated for different conditions to compare the fit indicators (corrections of T and fit indices) and their dependency on misspecification-unrelated factors. For these conditions, we secondly estimated the flexible cutoff values for T and its corrections as well as a comprehensive set of fit indices. Our data was obtained through a Monte Carlo simulation based on multivariate random number generators, using R and the simulateData function in package lavaan. A population model for CFA was initially defined by setting all latent variable correlations to. 3 and all factor loadings between latent variables and items to equal, standardized values. The population model does not contain misspecifications and is thus a correct model.
To illustrate the manipulation procedure, Fig. 1  depicts a model of four latent variables with three items each. This procedure led to a dataset of 2,304,000 cases. Additionally, fourteen relevant fit indicators are extracted directly or calculated. To derive the flexible cutoff values for the second step, the respective 576 conditions were simulated with 1,000 replications per model type (no errors in the measurement and structural model). Empirical quantiles of type 8  (Hyndman and Fan, 1996)  from definition 8 were extracted for a width of p =. 05.
Overall precision of the fit indicators under the different research conditions Having determined their sensitivity to data and model characteristics, we next address the question of whether conceptual considerations should guide the researcher's choice of fit indicator(s) and cutoffs. Supported by our review of the TFSC literature and the great variety in the field, we propose that different indicators should be consulted. For developing feasible and simple guidelines, we detailed the three critical questions that scholars face when estimating a model and investigating its fit. Our notion of a flexible model evaluation strategy posits that the selection of the optimal fit indicator(s) is contingent on the research purpose (established vs. novel model), the specification purpose (CFA vs. SEM), and the certainty of the evidence (sample size).
That is, GFI is the lone goodness-of-fit index not based on T. Additionally present in the main script are the Tucker Lewis Index (TLI) and the Comparative Fit Index (CFI,  Hu and Bentler, 1998, p. Finally, the Normed Fit Index (NFI) is simply  (Hu and Bentler, 1998, p. 428) : NFI = T B -.
With the development and improvement of modern surveying and remote-sensing technology, data in the fields of surveying and remote sensing have grown rapidly. Due to the characteristics of large-scale, heterogeneous and diverse surveys and the loose organization of surveying and remote-sensing data, effectively obtaining information and knowledge from data can be difficult. Therefore, this paper proposes a method of using ontology for heterogeneous data integration. Based on the heterogeneous, decentralized, and dynamic updates of large surveying and remote-sensing data, this paper constructs a knowledge graph for surveying and remote-sensing applications. First, data are extracted. Second, using the ontology editing tool Protégé, a knowledge graph mode level is constructed. Then, using a relational database, data are stored, and a D2RQ tool maps the data from the mode level's ontology to the data layer.
The relationship is mainly a hierarchical relationship between the upper and lower positions. Entity filling is mainly obtained from structured data sources, and the ontology database is filled in a bottom-up manner. A domain ontology is a specialized ontology that describes concepts in a specific domain (such as remote sensing, meteorology, environment, etc. ) and the relationship between concepts. The domain ontology of surveying and remote sensing, as a kind of unique ontology, can clearly describe the relationship between concepts belonging to and.
The relationship is mainly a hierarchical relationship between the upper and lower positions. Entity filling is mainly obtained from structured data sources, and the ontology database is filled in a bottom-up manner. A domain ontology is a specialized ontology that describes concepts in a specific domain (such as remote sensing, meteorology, environment, etc. ) and the relationship between concepts. The domain ontology of surveying and remote sensing, as a kind of unique ontology, can clearly describe the relationship between concepts belonging to and concepts in this domain. The basic principles of ontology construction are clarity, objectivity, consistency, minimum coding deviation, and minimum ontology constraints. Ontology construction uses a seven-step method. The steps of this method are as follows: (1) determine the professional field and category of the ontology; (2) examine the possibility of reusing existing ontology; (3) list the important terms in the ontology; (4) define the hierarchical relationship between classes; (5) define the properties of the class;  (6)  define the constraints between the properties; and (7) create an instance  [10].
Data Layer Construction The data layer construction process is based on unstructured data and uses manual, automatic, or semiautomatic techniques to extract knowledge from the data and store it in the database. Data acquisition is a key step in the construction of the data layer. The core of knowledge acquisition is how to automatically obtain knowledge elements of structured information, such as entities, relationships, and properties, from unstructured and semi-structured data sources. Typically, automatic or semi-automatic machine learning technology is used to extract entities, relationships, attributes, and other information about the knowledge graph from open multisource data  [11]. Knowledge acquisition includes the extraction of entities, relationships, properties, etc. Entity acquisition is the process of automatically identifying called entities (knowledge points, type names, etc.
Data Acquisition The data used in this article are from surveying and remote sensing. The data source is a textbook related to the field of surveying  [13]  and remote sensing  [14]. DeepDive (http: //deepdive. stanford. edu/, accessed on 6 May 2021) was used to extract semi-structured table data from unstructured text data  [15]. DeepDive can extract structured data from unstructured data and perform a series of data processing steps to build a knowledge base and extract relationships. It is very good at handling data sources in different formats. DeepDive has good database support, supports PubMed and other database data sources, and is a data source that has been processed in natural language. DeepDive has established a framework to standardize the construction process of the knowledge base system and allows users to design their own extractors and markers according to the knowledge base that needs to be constructed.
The relationship extraction process of DeepDive is shown in Figure  3. The DeepDive based domain text knowledge extraction method includes the following steps  [16] : (1) Data processing. First, the original corpus will be loaded. The natural language processing (NLP) tag is added. A set of candidate relationships and the sparse feature representation of each candidate relationship are extracted. (2) Remote supervision of data and rules, and then various strategies will be used to supervise the data set so that we can use machine learning to learn the weight of the mode. (3) Learning and inference: mode specification. Then, the advanced configuration of the mode will be specified. (4) Error analysis and debugging. Finally, we will show how to use DeepDive's tags, error analysis and debugging tools.
DeepDive has established a framework to standardize the construction process of the knowledge base system and allows users to design their own extractors and markers according to the knowledge base that needs to be constructed. The relationship extraction process of DeepDive is shown in Figure  3. The DeepDive based domain text knowledge extraction method includes the following steps  [18] : (1) Data processing. First, the original corpus will be loaded. The natural language processing (NLP) tag is added. A set of candidate relationships and the sparse feature representation of each candidate relationship are extracted. (2) Remote supervision of data and rules, and then various strategies will be used to supervise the data set so that we can use machine learning to learn the weight of the mode.
(3) Learning and inference: mode specification. Then, the advanced configuration of the mode will be specified. (4) Error analysis and debugging. Finally, we will show how to use DeepDive's tags, error analysis and debugging tools. (2) Data processing. This part is divided into four steps: 1 Loading the original input data. First, we convert the target data into an electronic text format. Only the two fields of document id and document content are reserved. We store the cleaned data in a comma-separated values (CSV) format file. Then import the original text data into the associated database. We set the data format of the document storage in the app. ddlog file. There are two text fields in the articles table, articles (id text, content text). Then, we put the compressed file of the original data into the specified input folder and run the "DeepDive compile" command.
Then, we execute the "DeepDive do articles" command, which imports the original data into the articles table of the associated database. At this time, we can query the imported raw data by executing the query command. 2 Adding NLP markups. We use the CoreNLP natural language processing system to add annotations to the original data. The steps of NLP are: first, the input original article is divided into sentences. The sentence is divided into words, and the part-of-speech tags, standard forms, dependency analysis and entity recognition tags of the words in the sentence are obtained. After NLP, some commonly used entities (person names, place names, etc. ) can be marked. It is also necessary to ensure further entity identification of the data processed by NLP. The input are the data in the sentences table.
The output are the marked data. Finally, we import the final marked data into the sentences_new table in the database. 3  Extracting candidate relation mentions. DeepDive proposes corresponding input and output interfaces, allowing users to design their entity or relationship extractors. Generally speaking, the SQL(Structured Query Language) statement is used as the input interface to extract data from the database; The output is the corresponding table in the database. We perform entity extraction on the data after entity recognition, and then establish the corresponding database table structure. 4 Extracting features for each candidate. First, we extract the feature description and store the feature in the func_feature table. The purpose is to use certain attributes or characteristics to represent each candidate pair.
3 Specifying connections between variables. We can use learning weights or given weights to specify the dependencies between predictors. In the experiment, we specify two such rules, which have fixed (given) weights. First, we define the asymmetric connection, that is, if the model considers that a person mentions p1 and another person mentions p2 as a spouse relationship in the sentence, then it should also consider the opposite. The model should be strongly biased towards everyone mentioning a sign of marriage. Instead, we use negative weights for this operation. 4 Finally, we want to perform learning and inference using the specified model. This will build a model based on the data in the database, learn the weights, infer the expected or marginal probabilities of the variables in the model and then load it back into the database.
1) or (0. 9, 1. 0). weights to specify the dependencies between predictors. In the experiment, we specify two such rules, which have fixed (given) weights. First, we define the asymmetric connection, that is, if the model considers that a person mentions p1 and another person mentions p2 as a spouse relationship in the sentence, then it should also consider the opposite. The model should be strongly biased towards everyone mentioning a sign of marriage. Instead, we use negative weights for this operation. ④ Finally, we want to perform learning and inference using the specified model. This will build a model based on the data in the database, learn the weights, infer the expected or marginal probabilities of the variables in the model and then load it back into the database. In this way, we can see the probability of the has_spouse variable inferred by DeepDive.
Data Storage The data of the knowledge graph is usually expressed in the form of triples, representing the relationship between entities or between entities and attribute values. In this article, the relational database MySQL and graph database Neo4j are used. The purpose of using MySQL is to realize the mapping between data and ontology and then to use existing tools to realize data query and reasoning. The purpose of using Neo4j is to update and search data. Neo4j can directly display the query results in the form of graphs to realize the function of knowledge visualization. Neo4j has local storage and data processing functions that are different from general databases, which can ensure high readability and integrity of data. The extracted data are processed and stored in three tables, called "knowledge", "knowledge_to_genre", and "genre", which are imported into a MySQL database.
Ontology Storage The ontology storage mode in this article is the Relational Database Management System (RDBMS). The principle of RDBMS is to map the ontology to one or more tables, and then divide it into several modes according to the mapping mode, such as horizontal storage, decomposition storage, vertical storage and hybrid storage. As the storage mechanism and data management capabilities of relational databases are relatively mature, relational database storage is widely used in many storage methods. This article uses the and web ontology language (OWL) to describe ontologies  [22]. RDF is used to represent any resource information and describes resources through the mode of attribute-attribute value  [23]. OWL is used to express the relationship between classes, the constraints of the set cardinality, the equality relationship, the attribute type, and the characteristics of the attribute  [24].
Compared with other ontology description languages, OWL has better description ability and more description vocabulary. The description vocabulary expands the reasoning and query capabilities of the ontology. The ontology is constructed and stored using the ontology editing tool Protégé. It cannot only construct and operate the ontology but also visually display the generated ontology, including the display of the hierarchical relationship between ontology concepts, as well as the visual display of ontology conceptual entities, entity relationships, and entity attributes. When the ontology is created using the ontology description language OWL and Protégé, semiautomatic construction can be achieved. Protégé can also reason about the ontology based on the hierarchical relationship of the ontology, with the help of Jena's query mode, and can also realize the editing operation of the ontology in the RDF and OWL languages  [25].
Ontology and Database Mapping Two standards to convert the structured data of the relational database into RDF format data have been developed by the RDB2RDF studio of W3C. The process is applied by point "electromagnetic wave" can also be found in the class "remote sensing". Finally, class properties (data properties) are created and are similar to object properties. Concurrently, Protégé also has a visual display function to show the structure of the ontology. The ontology structure is shown below (Figure  7 ).
Ontology Storage The ontology storage mode in this article is the Relational Database Management System (RDBMS). The principle of RDBMS is to map the ontology to one or more tables, and then divide it into several modes according to the mapping mode, such as horizontal storage, decomposition storage, vertical storage and hybrid storage. As the storage mechanism and data management capabilities of relational databases are relatively mature, relational database storage is widely used in many storage methods. This article uses the RDF and web ontology language (OWL) to describe ontologies  [22]. RDF is used to represent any resource information and describes resources through the mode of attribute-attribute value  [23]. OWL is used to express the relationship between classes, the constraints of the set cardinality, the equality relationship, the attribute type, and the characteristics of the attribute  [24].
Ontology Storage The ontology storage mode in this article is the Relational Database Management System (RDBMS). The principle of RDBMS is to map the ontology to one or more tables, and then divide it into several modes according to the mapping mode, such as horizontal storage, decomposition storage, vertical storage and hybrid storage. As the storage mechanism and data management capabilities of relational databases are relatively mature, relational database storage is widely used in many storage methods. This article uses the RDF and web ontology language (OWL) to describe ontologies  [20]. RDF is used to represent any resource information and describes resources through the mode of attribute-attribute value  [21]. OWL is used to express the relationship between classes, the constraints of the set cardinality, the equality relationship, the attribute type, and the characteristics of the attribute  [22].
Compared with other ontology description languages, OWL has better description ability and more description vocabulary. The description vocabulary expands the reasoning and query capabilities of the ontology. The ontology is constructed and stored using the ontology editing tool Protégé. It cannot only construct and operate the ontology but also visually display the generated ontology, including the display of the hierarchical relationship between ontology concepts, as well as the visual display of ontology conceptual entities, entity relationships, and entity attributes. When the ontology is created using the ontology description language OWL and Protégé, semiautomatic construction can be achieved. Protégé can also reason about the ontology based on the hierarchical relationship of the ontology, with the help of Jena's query mode, and can also realize the editing operation of the ontology in the RDF and OWL languages  [23].
Ontology and Database Mapping Two standards to convert the structured data of the relational database into RDF format data have been developed by the RDB2RDF studio of W3C. The process is applied by the D2RQ tool  [24]. The first standard is direct mapping, which is defined by the rule that tables in the database are classes in the associated ontology. For example, there are 3 tables for the data stored in MySQL. After mapping, the ontology has 2 classes instead of the 3 classes previously defined. The columns of tables are attributes, and the rows are instances. The content in each cell of tables is text. If a certain column in the cell is a foreign key, then it will not be able to map the data in the database to the defined ontology. In this case, the shortcomings of direct mapping are marked.
In response to this defect, the RDB2RDF studio proposed R2RML and allows users to flexibly edit and set mapping rules. This mapping also provides the ability to view existing relational data in the RDF data mode, which is represented by mapping the structure selected by the customer and the target vocabulary. R2RML mapping is an RDF graph and is recorded in Turtle syntax. R2RML supports different types of mapping implementations. The processor can provide virtual SPARQL protocol and resource description framework query language (SPARQL) endpoints, generate RDF dumps, or provide link data interfaces on the mapped relational data  [25]. The specific mapping diagram is shown below (Figure  8 ). The first standard is direct mapping, which is defined by the rule that tables in th database are classes in the associated ontology.
For example, there are 3 tables for the dat stored in MySQL. After mapping, the ontology has 2 classes instead of the 3 classes pre viously defined. The columns of tables are attributes, and the rows are instances. The con tent in each cell of tables is text. If a certain column in the cell is a foreign key, then it wi not be able to map the data in the database to the defined ontology. In this case, the shor comings of direct mapping are marked. In response to this defect, the RDB2RDF studi proposed R2RML and allows users to flexibly edit and set mapping rules. This mappin also provides the ability to view existing relational data in the RDF data mode, which represented by mapping the structure selected by the customer and the target vocabulary R2RML mapping is an RDF graph and is recorded in Turtle syntax.
Query and Reasoning This article describes how to use D2RQ to setup a SPARQL service endpoint and us query operations in the browser. A SPARQL endpoint provides a service that is complian with the SPARQL protocol. Through the default or defined mapping file, RDF data can b queried in the rules of RDF. In other words, to complete the final query, D2RQ convert the SPARQL query into an SQL statement based on the mapping file and then returns th result to the user  [28]. The following steps are executed: (1) start the D2R server; (2) enter the browser t start the SPARQL endpoint; and (3) enter the SPARQL statement to execute the query Concurrently, operations can also be queried by writing Python scripts. The third-part library SPARQL Wrapper of Python can easily interact with endpoints. Using D2RQ to open the endpoint service has two disadvantages: it does not suppo publishing RDF data directly to the network through the endpoint and does not suppo the inference.
To solve these problems, certain components of Fuseki, Jena, and the tupl database (TDB) in Apache Jena are investigated experimentally. Fuseki is a SPARQ server provided by Apache Jena, which is primarily run as a web application or as a embedded server. Jena provides resource description framework schema (RDFS). In th case of a single machine, storage layer technology can provide high RDF storage perfo mance  [29]. Next, we must define reasoning rules and conduct knowledge reasoning.
Query and Reasoning This article describes how to use D2RQ to setup a SPARQL service endpoint and use query operations in the browser. A SPARQL endpoint provides a service that is compliant with the SPARQL protocol. Through the default or defined mapping file, RDF data can be queried in the rules of RDF. In other words, to complete the final query, D2RQ converts the SPARQL query into an SQL statement based on the mapping file and then returns the result to the user  [26]. The following steps are executed: (1) start the D2R server; (2) enter the browser to start the SPARQL endpoint; and (3) enter the SPARQL statement to execute the query. Concurrently, operations can also be queried by writing Python scripts. The third-party library SPARQL Wrapper of Python can easily interact with endpoints.
Using D2RQ to open the endpoint service has two disadvantages: it does not support publishing RDF data directly to the network through the endpoint and does not support the inference. To solve these problems, certain components of Fuseki, Jena, and the tuple database (TDB) in Apache Jena are investigated experimentally. Fuseki is a SPARQL server provided by Apache Jena, which is primarily run as a web application or as an embedded server. Jena provides resource description framework schema (RDFS). In the case of a single machine, storage layer technology can provide high RDF storage performance  [27]. Next, we must define reasoning rules and conduct knowledge reasoning.