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Abstract

Content

Introduction

The volume of information in the world is increasing annually by 30% [1]. This trend is also no exception for electronic documents, reports, articles and teaching aids, which are used to varying degrees during the educational process.

In their professional activities, teachers are engaged in the preparation of lecture courses, the selection of various materials and sources, as well as the preparation of methodological instructions. Students, in turn, also carry out an extensive search for material for high-quality performance of laboratory, coursework and practical work. In addition, both students and teachers carry out the selection of material for their scientific activities.

This entire array of data is stored separately, in various formats, and varying degrees of structuredness:

Different approaches to storing data and useful information from them, inevitably leads to a repeated search for existing materials or their leakage.

1. Relevance of the topic

Currently, a large amount of knowledge and information resources has been accumulated, formed as a result of scientific and technical events (STE).

However, as experience shows, they are poorly systematized, poorly structured, scattered across various libraries, resources and archives, which significantly limits access to them. Moreover, for technical, historical and other reasons, thematically related data are stored in different formats under the control of different data storage and processing systems. This leads to the fact that publications and other diverse collections of data, even those that are located on the same physical server, are mostly disparate autonomous information resources and have different logical entrances. The lack of unified access and connectivity to information resources leads to incomplete consideration and accounting of existing data and knowledge. [2]

A logical solution to this problem is the creation of a unified knowledge base that will allow at any time to find the required information based on the user's request, however, this solution has a significant drawback – the relational and hierarchical approach to building such a system provides for storing only information, but not the meaning and data connection between yourself. To solve this problem, one should move to a higher level of information presentation – the semantic level, it will allow taking into account the meaning (content) of information resources, extracting knowledge important for the user from them. To implement the knowledge base of the semantic level, an ontology should be used.

The created ontology is only a framework, in order for it to provide information – it must be filled in (add Individuals and indicate their properties) and edit if necessary. There are many ontology editors that, in theory, can solve these problems, but in practice they have drawbacks, which will be described later.

The combination of these factors indicates that the creation of an information system for the formation of a knowledge base of scientific and technical activities is an urgent task.

2. Purpose and objectives of the study, planned results

Purpose: to simplify the preservation of knowledge obtained as a result of scientific and technical activities.

To achieve this goal, a number of tasks should be completed:

As a result of the master's work, it is planned to create an ontology that can satisfy the subject area of ??the university and reporting documentation for scientific and technical activities. It is also planned to create an editor, which in a convenient form will allow changing the structure of the ontology and filling it with data in an automatic and semi-automatic mode.

A short algorithm of the planned system operation is shown in the animation below.

Algorithm of the planned system

Picture 1 – Algorithm of the planned system
(animation: 11 frames, 3 cycles of repetition, 145 kilobytes)

3. Research and development overview

3.1 Review of international and national sources

The use of ontologies in various information systems (IS) has been widely studied. Let us consider typical tasks that are most common in the design of ontology-based ISs.

Paper [3] describes the theoretical advantages and various features of the knowledge-based framework, which is based on the knowledge entered into the system by an expert. The problem of maximizing the efficiency of a chemical automation production line and minimizing costs by optimizing production plans, preventing dangerous situations and failures is considered.

There are also many ontology-based health information systems. For example, a context-oriented access control framework that allows SitBAC to be represented and implemented as a knowledge model along with an appropriate inference method using SWRL and OWL [4].

Article [5] discusses the design, development and validation of an iosC3-ontology system that will enable intelligent observation and treatment of critical patients with acute cardiac disorders in the intensive care unit (ICU). This system analyzes the patient's condition and gives recommendations for treatment that will help to achieve the fastest possible recovery.

Reference [6] described an ontology-based expression interpretation mechanism for intelligent conversational interfaces. The goal of this approach is to offer a system that is capable of performing tasks through an interface that is convenient and comfortable for both experienced and less experienced users. The ontology in this mechanism is used for semantic and syntactic interpretation.

At the moment, in almost every organization, financial analysis is the basis for assessing and understanding the results of business activities and determining how well the business is functioning. In work [7] develops an ontological model for the financial information of the organization, based on the analysis of the semantics of the financial reporting of the enterprise. By combining the association rule mining algorithm, the financial domain ontology model and the Zscope model, a new business intelligence model has been developed for predicting bankruptcy.

Ontologies are also used in education and e-learning. In recent years, with the widespread use of metadata and the emergence of the semantic web, this vision is gradually becoming a reality. These systems are based on strong relationships defined in the metadata of learning objects that allow them to be combined with other learning objects to form a holistic educational program [8]. In adaptive educational systems available to students, the integration of these systems turns from an interesting research problem into an important practical problem, the solution of which is based on ontologies and metadata.

Article [9] presents the TSH ontological model, it was developed using a modular approach and implemented in OWL using Protege2000 in order to use the full potential of ontologies for describing the domain to provide an effective basis for developing, configuring and running software applications.

The work of S. Nirenburg, V. Raskin [10] discusses the possibility of using ontologies for information retrieval of knowledge, in knowledge extraction systems, as well as classification of knowledge, etc. In ontological semantics Sergey Nirenburg and Viktor Raskin introduce an integrated approach to processing the meaning of text using a computer.

Several ontology-based approaches have been proposed in the past to support knowledge engineers and architects in knowledge management [11], [12]. Akerman and Tyree [11] offer an ontology-based approach to support software development. However, this ontology is not publicly available for reuse; in addition, the authors do not provide detailed information about the number of people and their relationships in the ontology.

Ameller and Franch [12] present an ontology for representing knowledge called Arteon. This ontology aims to provide the building blocks of architectural views, frameworks, and elements for constructing the structural aspects of software architecture. It should be noted that the authors consider the population of individuals as a part of their future work and describe only concepts, within the framework of this ontology, these factors do not allow using this ontology to the full at the current moment.

In her seminal work, Ms Kruchten [13] proposes an ontology to add as a first class architectural knowledge management concepts, and introduces a taxonomy of architectural solutions, its attributes and its links to concepts such as requirements, defects, design and implementation elements. The advantage of this ontology is that it preserves complex graphs of interrelated design decisions and supports use cases such as guidelines to support software architects in decision making. [14]

3.2 Review of local sources

DonNTU students are also dealing with the use of ontologies, we will consider abstracts of masters of past years.

The work Development of a knowledge base of an intelligent system of access to educational and methodological information within the university [15] describes the important role of the knowledge base, as in various companies, and in the university. The characteristic features of knowledge representation are given. On the basis of his analysis, the author indicates and describes the most frequently used and popular models of knowledge representation. An overview of approaches for designing knowledge bases has also been performed. The author concludes that the software implementation of these approaches will allow differentiated, and, therefore, efficiently, to organize the process of automated learning, access to information and its structuring.

In the work Models and algorithms for updating the knowledge of expert systems based on the ontological approach [16] the author describes the problem of keeping the expert system up to date. To solve this problem, it is planned to introduce an ontology, due to which the system will be able to receive knowledge from the Internet. The classification of ontologies according to various features and their areas of application is given. The process of developing an expert system and introducing an ontology into it is described. The author concludes that the introduction of an ontology into an expert system will save both experts and end users a huge amount of time on updating knowledge, as well as funds for attracting specialists, and will provide more accurate results of work.

The work Investigation of the possibility of building an ontology editor for synthesizing Internet sites [17] describes the classification of ontologies into two types - semantic and pragmatic. There are three types of classes of domain experts who play the role of knowledge engineers. The process of generating html-text is shown when the end user passes OR-nodes in the process of synthesizing the required object. The process of building an ontology according to the IDEF5 methodology is described. The author makes the assumption that the creation of the planned tool shell should significantly reduce the required level of knowledge of the subject area of ??both the end user and the expert himself, since it will be enough to describe the connections between objects, to present variants of the structures of relations in the form of an AND-OR tree and to set the relationship of compatibility – incompatibility between various combinations of values ??of OR-nodes.

The work Development of a web-oriented recruiting system based on an ontological model of knowledge representation [18] describes the reasons for the increased interest in ontologies, studies the ways of their construction and applications. The author argues that after the creation of a hierarchical knowledge base, the implementation of the search for suitable candidates according to the requirements put forward by the employer comes down to finding the distance from the vacancy to the applicants. In the future, the author plans that the development of the ontology will be aimed at introducing descriptive logics that will allow logical inference based on the available data in the knowledge base. Also, a mechanism for queries to the knowledge base in the SPARQL language will be developed, allowing you to select the most suitable candidates.

The work Development of an ontological model for semantic information retrieval in an electronic library [19] indicates that many problems associated with information retrieval remain unresolved. The scheme of semantic information retrieval is given and described. The description of the algorithm of the morphological analyzer has also been performed. The author claims that the use of ontologies will make it possible to present a natural language text in such a way that it becomes suitable for automatic processing.

Conclusions

This master's work is devoted to the problem of storing data extracted from documents of scientific and technical events. The analysis of international and national, as well as local works on the application of ontologies is carried out. Specification languages ??and ontology development tools are considered. In the future, work will be focused on the creation of an ontology that can satisfy the subject area of ??the university and reporting documentation for scientific and technical activities. It is also planned to create an editor, which in a convenient form will allow changing the structure of the ontology and filling it with data in automatic and semi-automatic modes.

When writing this essay, the master's work has not yet been completed. Final completion: June 2021. The full text of the work and materials on the topic can be obtained from the author or his manager after that date.

List of sources

  1. Information explosion [Electronic resource]: Wikipedia. Free encyclopedia. - Access mode: https://ru.wikipedia.org/wiki/Èíôîðìàöèîííûé_âçðûâ
  2. Andrievskaya N.K. Basic principles and approaches in developing a professional knowledge management system for university employees Journal: INFORMATICS AND CYBERNETICS. 2019. No. 4 (18). P. 49-56.
  3. Legat C. Model-based Knowledge Extraction for Automated Monitoring and Control [Text] / C. Legat, J. Neidig, M. Roshchin // Preprints of the 18th IFAC World Congress. – 2011.
  4. Beimel D. Using OWL and SWRL to represent and reason with situation-based access control policies [Text] / D. Beimel, M. Peleg // Data & Knowledge Engineering. – 2011. – Ne 70.
  5. The iOSC3 System: Using Ontologies and SWRL Rules for Intelligent Supervision and Care of Patients with Acute Cardiac Disorders [Text] / M. Martinez-Romero, J. Vazquez-Naya, J. Pereira, M. Pereira, A. Pazos, G. Bafios // Computational and Mathematical Methods in Medicine. – 2013.
  6. Paraiso E. An Ontology-Based Utterance Interpretation in the Context of Intelligent Assistance [Text] / E. Paraiso, J. Barthes // Programa de Pes-Graduacao em Informatica. – 2007.
  7. A Business Intelligence Model to Predict Bankruptcy using Financial Domain Ontology with Association Rule Mining Algorithm [Text] / A. Martin, M. Manjula, Dr. V. P. Venkatesan // IJCSI. – 2011.
  8. EAn Ontology-based Planning System for e-Course Generation [Text] / Kontopoulos, D. Vrakas, F. Kokkoras, N. Bassiliades, I. Viahavas // Expert Systems with Applications. – 2008. – Ne 35 (1-2).
  9. Latfi F. Ontology-Based Management of the Telehealth Smart Home, Dedicated to Elderly in Loss of Cognitive Autonomy [Text] / F. Latfi, B. Lefebvre, C. Descheneaux // Proceeding of the OWLED. – 2007.
  10. Nirenburg S. Ontological Semantics MIT Press [Text] / S. Nirenburg, V. Raskin. – 2004. – 339 p.
  11. Akerman, A. Using ontology to support development of software architectures [Text] / A. Akerman, and J. Tyree // IBM Systems Journal. – 2006. – (45:4), IBM. – Pp. 813–825.
  12. Ameller D. Ontology-based architectural knowledge representation: structural elements module [Text] / Ameller, D., and Franch, X. // International Conference on Advanced Information Systems Engineering. – 2011. – Pp. 296–301.
  13. Kruchten, P. An ontology of architectural design decisions in software intensive systems [Text] / P. Kruchten // 2nd Groningen workshop on software variability. – 2004. – Pp. 54–61.
  14. Andrievskaya N.K. Ontological approach in data processing systems of scientific and scientific-educational organizations Journal: PROBLEMS OF ARTIFICIAL INTELLIGENCE
  15. Vorona S.P. Development of a knowledge base of an intelligent system of access to educational and methodological information within the university Personal site on the portal of masters of DonNTU, 2019
  16. Bilyk N.O. Models and algorithms for updating the knowledge of expert systems based on the ontological approach Personal site on the portal of masters of DonNTU, 2013
  17. A.V. Mashichev Investigation of the possibility of building an ontology editor for the synthesis of Internet sites Personal site on the portal of masters of DonNTU, 2013
  18. Ryabinin V.A. Development of a knowledge base and web application for preparation and processing of exam tasks Personal site on the portal of masters of DonNTU, 2013
  19. Bazhanova A.I. Development of an ontological model for semantic information retrieval in an electronic library Personal site on the portal of masters of DonNTU, 2011