Constructive-Synthesizing Modelling of Ontological Document Management Support for the Railway Train Speed Restrictions

Purpose. During the development of railway ontologies, it is necessary to take into account both the data of information systems and regulatory support to check their consistency. To do this, data integration is performed. The purpose of the work is to formalize the methods for integrating heterogeneous sources of information and ontology formation. Methodology. Constructive-synthesizing modelling of ontology formation and its resources was developed. Findings. Ontology formation formalization has been performed, which allows expanding the possibilities of automating the integration and coordination of data using ontologies. In the future, it is planned to expand the structural system for the formation of ontologies based on textual sources of railway regulatory documentation and information systems. Originality. The authors laid the foundations of using constructive-synthesizing modelling in the railway transport ontological domain to form the structure and data of the railway train speed restriction warning tables (database and csv format), their transformation into a common tabular format, vocabulary, rules and ontology individuals, as well as ontology population. Ontology learning methods have been developed to integrate data from heterogeneous sources. Practical value. The developed methods make it possible to integrate heterogeneous data sources (the structure of the table of the railway train management rules, the form and application for issuing a warning), which are railway domain-specific. It allows forming an ontology from its data sources (database and csv formats) to schema and individuals. Integration and consistency of information system data and regulatory documentation is one of the aspects of increasing the level of train traffic safety

Literature review on the information system for digitization of royal history and Waqf

There has been a significant increase in the study of the history and culture of historical artifacts, whether they take the form of cultural heritage or Waqf. A literature review of web-based information systems was conducted for digitizing historical preservation and Waqf. Papers were sourced from various databases, including Publish or Perish, which produced 1043 journals, 370 articles, and 673 items from reputable sources, Google Scholar, and Crossref, respectively. The focus of the literature review was the information system for digitizing history and Waqf and integrating ontology databases. This literature review study aims to trace the evolution of study objects related to history and endowments. The results showed that most studies emphasized the user-understanding aspect of digitization, while the technical aspect was focused on using cutting-edge technology, such as 3D and virtual reality

Methodology for Automatic Ontology Generation Using Database Schema Information

An ontology is a model language that supports the functions to integrate conceptually distributed domain knowledge and infer relationships among the concepts. Ontologies are developed based on the target domain knowledge. As a result, methodologies to automatically generate an ontology from metadata that characterize the domain knowledge are becoming important. However, existing methodologies to automatically generate an ontology using metadata are required to generate the domain metadata in a predetermined template, and it is difficult to manage data that are increased on the ontology itself when the domain OWL (Ontology Web Language) individuals are continuously increased. The database schema has a feature of domain knowledge and provides structural functions to efficiently process the knowledge-based data. In this paper, we propose a methodology to automatically generate ontologies and manage the OWL individual through an interaction of the database and the ontology. We describe the automatic ontology generation process with example schema and demonstrate the effectiveness of the automatically generated ontology by comparing it with existing ontologies using the ontology quality score