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Course Syllabus

Магистратура 2020/2021

Семантические технологии

Статус: Курс обязательный (Информационная аналитика в управлении предприятием)
Направление: 38.04.05. Бизнес-информатика
Кто читает: Кафедра информационных технологий в бизнесе (Пермь)
Где читается: Факультет экономики, менеджмента и бизнес-информатики
Когда читается: 1-й курс, 3, 4 модуль
Формат изучения: с онлайн-курсом
Преподаватели: Ланин Вячеслав Владимирович
Прогр. обучения: Информационная аналитика в управлении предприятием
Язык: английский
Кредиты: 7
Контактные часы: 80

Course Syllabus

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Abstract

The present program of educational discipline establishes requirements to educational results and learning outcomes of the student and determines the content and types of training sessions and reporting. The program is intended for the teachers conducting discipline "Semantic Technologies", educational assistants and students of a direction of preparation 38.04.05 Business informatics, studying under the educational program "Information analytics in enterprise management".
Learning Objectives

Learning Objectives

  • Development of students' skills in applying modern technologies of intellectual data processing on the basis of their semantic interpretation
Expected Learning Outcomes

Expected Learning Outcomes

  • The student is ready for oral and written communication both in Russian and foreign languages in order to achieve goals within professional and scientific environment
  • The student is able to apply a logical model of knowledge representation.
  • The student is able to apply the production model of knowledge representation.
  • The student is able to use frames to represent knowledge.
  • The student is able to apply semantic networks to represent knowledge.
  • The student is able to use information retrieval thesauruses and ontologies to process information.
  • The student knows and is able to use natural language processing technology.
  • The student knows and is able to use natural language processing technologies and tools.
  • The student knows and is able to use Semantic Web technologies.
  • conducts professional, including research activities in the international environment
Course Contents

Course Contents

  • Theme 1: Models of knowledge presentation, overview
    General scheme of knowledge representation models. Brief historical information on the development of models. Examples of knowledge-based systems.
  • Theme 2: Logical model of knowledge representation
    Types of logic models, general terms and definitions. Formal (Aristotleva) logic: names, statements, proof and rebuttal procedures. Mathematical implementation of formal logic. Methods of automatic theorem proof (calculation of predicates). Fuzzy sets.
  • Theme 3: Production model of knowledge representation
    Description of the subject area by rules and facts. Methods of full search in width and depth. Heuristic methods of searching in the state space. Solution of problems by the method of division into subtasks. Presentation of the problem as an OR graph. Product system management.
  • Theme 4: Frames for the presentation of knowledge
    Presentation of knowledge about the object by means of frames, examples. Practical implementation of the frame model. Analysis of spatial scenes. Understanding the meaning of the sentences.
  • Theme 5: Semantic networks for knowledge representation
    Types of knots and types of relationships. "Surface" and "depth" of knowledge as the main differences between the model of semantic networks and the production model. The subject areas where semantic networks have become widespread.
  • Theme 6: Ontology and thesauruses
    Definition of concepts: ontology, concept, attitude, axioms. Examples of ontologies. Problems solved on the basis of ontological approach: information search. Integration of heterogeneous data sources. Types of ontologies: upper level, subject areas, applied ontologies. Lexical ontologies. Examples of ontologies. Top-level ontologies: distinguishing features, tasks to be solved. SUMO and Sowa's ontology ontologies. CYC Ontology. Basic principles of development, creation and use of traditional information-search thesauruses. Examples of thesauruses. Basic principles of development, creation and use of traditional information-search thesauruses. Examples of thesauruses.
  • Theme 7. Computer linguistics
    Tasks of computer linguistics. Features of FSL system: levels and communications. Modeling in computer linguistics. Linguistic resources. Computer linguistics applications.
  • Theme 8: Stages of text analysis
    A betrayal. Morphological analysis. Surface syntactic analysis. In-depth syntactic analysis. Surface semantic analysis. In-depth semantic analysis. Pragmatic analysis. Identification of text structures.
  • Theme 9: Tools for developing automated text processing applications
    Linguistic processing software. Presentation of linguistic data: approaches to data representation, linguistic markup, linguistic annotations. Architecture of instrumental NFR systems. Component organization of ENE-systems, text processing processes. Systems of processing of ENE-texts: systems on the basis of marking, systems on the basis of annotations, systems of integration of surface and deep processing.
  • Theme 10. Introduction to Semantic Web technology
    volution of web technologies. Shortcomings of the traditional Web. The concept of Semantic Web. Multilevel representation. Main tendencies of development of Internet technologies. Semantic Web applications. Electronic commerce, auctions. Information collection and management. Personal assistants. Scientific and educational information environments. Electronic tourism. E-government. Bioinformatics. Semantic Grid. Business process management.
  • Theme 11. Basic technologies of Semantic Web
    Semantic Web SPARQL query language. Simple queries. Thermas, literals, variables. List of predicates-objects. Anonymous nodes. RDF collection. Samples of triplets. Sample solutions. Multiple comparisons. Working with RDF literals. Comparison of RDF literals. Limitations of values. Samples of graphs. Combination of samples. RDF data sets. RDF data set queries. Description of RDF datasets. Solutions and result forms. Selection of variables. Building the resulting graph. Resource descriptions. Explicit IRI. Resource identification. Functions and operators of SPARQL. Software tools for query implementation. Using SPARQL and Jena. Examples of query implementation. Description of resources in RDF language. OWL ontology description language. Standard metadata views. FOAF technology.
  • Theme 12. Agents in Semantic Web
    Intelligent agents and multi-agent technologies. Data processing algorithms in Semantic Web. Semantic Web services. The concept of Semantic Web services. Ontologies of web services modeling. Service description: profile, process model, interaction (grounding). Stages of work with web services: annotation, detection, handling, composition, monitoring of service performance. Specifications for semantic web services: WSMO, WSML, WSMX, OWL-S, SWSF, IRS-III, WSDL-S. Methods, algorithms and tools to detect and compose web services. Examples of service descriptions. Options for using discovery and service compositions in an enterprise B2B system.
Assessment Elements

Assessment Elements

  • non-blocking Lab work 1
  • non-blocking Lab work 2
  • non-blocking Lab work 3
  • non-blocking Lab work 4
  • non-blocking Lab work 5
  • non-blocking Lab work 6
  • non-blocking Lab work 7 (independent work)
  • non-blocking Exam
    Оценка за экзамен выставляется как среднее арифметическое по 7-ми выполненным лабораторным работам. An exam assessment is arithmetic average of 7 completed laboratory work.
  • non-blocking Evaluation for taking online courses
  • non-blocking Lab work 1
  • non-blocking Lab work 2
  • non-blocking Lab work 3
  • non-blocking Lab work 4
  • non-blocking Lab work 5
  • non-blocking Lab work 6
  • non-blocking Lab work 7 (independent work)
  • non-blocking Exam
    Оценка за экзамен выставляется как среднее арифметическое по 7-ми выполненным лабораторным работам. An exam assessment is arithmetic average of 7 completed laboratory work.
  • non-blocking Evaluation for taking online courses
Interim Assessment

Interim Assessment

  • Interim assessment (4 module)
    0.075 * Evaluation for taking online courses + 0.4 * Exam + 0.075 * Lab work 1 + 0.075 * Lab work 2 + 0.075 * Lab work 3 + 0.075 * Lab work 4 + 0.075 * Lab work 5 + 0.075 * Lab work 6 + 0.075 * Lab work 7 (independent work)
Bibliography

Bibliography

Recommended Core Bibliography

  • Загорулько Ю. А., Загорулько Г. Б. - ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ. ИНЖЕНЕРИЯ ЗНАНИЙ. Учебное пособие для вузов - М.:Издательство Юрайт - 2019 - 93с. - ISBN: 978-5-534-07198-6 - Текст электронный // ЭБС ЮРАЙТ - URL: https://urait.ru/book/iskusstvennyy-intellekt-inzheneriya-znaniy-442134

Recommended Additional Bibliography

  • Антониоу Г., Грос П., Хармелен ван Ф. - Семантический веб - Издательство "ДМК Пресс" - 2016 - 240с. - ISBN: 978-5-97060-333-8 - Текст электронный // ЭБС ЛАНЬ - URL: https://e.lanbook.com/book/69963
  • Назаров Д. М., Конышева Л. К. - ИНТЕЛЛЕКТУАЛЬНЫЕ СИСТЕМЫ: ОСНОВЫ ТЕОРИИ НЕЧЕТКИХ МНОЖЕСТВ 3-е изд., испр. и доп. Учебное пособие для академического бакалавриата - М.:Издательство Юрайт - 2019 - 186с. - ISBN: 978-5-534-07496-3 - Текст электронный // ЭБС ЮРАЙТ - URL: https://urait.ru/book/intellektualnye-sistemy-osnovy-teorii-nechetkih-mnozhestv-423214