Vitalii Velychko

Vitalii Velychko, Svitlana Voinova, Valery Granyak et al.

The monograph summarizes and analyzes the current state of development of computer and mathematical simulation and modeling, the automation of management processes, the use of information technologies in education, the design of information systems and software complexes, the development of computer telecommunication networks and technologies most areas that are united by the term Industry 4.0

Oleksandr Palagin, Vitalii Velychko, Kyrylo Malakhov et al.

We design a new technique for the distributional semantic modeling with a neural network-based approach to learn distributed term representations (or term embeddings) - term vector space models as a result, inspired by the recent ontology-related approach (using different types of contextual knowledge such as syntactic knowledge, terminological knowledge, semantic knowledge, etc.) to the identification of terms (term extraction) and relations between them (relation extraction) called semantic pre-processing technology - SPT. Our method relies on automatic term extraction from the natural language texts and subsequent formation of the problem-oriented or application-oriented (also deeply annotated) text corpora where the fundamental entity is the term (includes non-compositional and compositional terms). This gives us an opportunity to changeover from distributed word representations (or word embeddings) to distributed term representations (or term embeddings). This transition will allow to generate more accurate semantic maps of different subject domains (also, of relations between input terms - it is useful to explore clusters and oppositions, or to test your hypotheses about them). The semantic map can be represented as a graph using Vec2graph - a Python library for visualizing word embeddings (term embeddings in our case) as dynamic and interactive graphs. The Vec2graph library coupled with term embeddings will not only improve accuracy in solving standard NLP tasks, but also update the conventional concept of automated ontology development. The main practical result of our work is the development kit (set of toolkits represented as web service APIs and web application), which provides all necessary routines for the basic linguistic pre-processing and the semantic pre-processing of the natural language texts in Ukrainian for future training of term vector space models.

Kyrylo Malakhov, Oleksandr Kurgaev, Vitalii Velychko

The given paper presents an overview of modern RESTful API description languages (belongs to interface description languages set) - OpenAPI, RAML, WADL, Slate - designed to provide a structured description of a RESTful web APIs (that is useful both to a human and for automated machine processing), with related RESTful web API modeling frameworks. We propose an example of the schema model of web API of the service for pre-trained distributional semantic models (word embeddings) processing. This service is a part of the Personal Research Information System services ecosystem - the Research and Development Workstation Environment class system for supporting research in the field of ontology engineering: the automated building of applied ontology in an arbitrary domain area as a main feature; scientific and technical creativity: the automated preparation of application documents for patenting inventions in Ukraine. It also presents a quick look at the relationship of Service-Oriented Architecture and Web services as well as REST fundamentals and RESTful web services; RESTful API creation process.