• Information Organization and Retrieval (IOR)

    Welcome to Information Organization and Retrieval

     
    The digital age has transformed how knowledge is stored, organized, and accessed. Information Organization and Retrieval (IOR) is at the heart of this transformation, ensuring that information is not just available but also discoverable, meaningful, and usable.
    This course is structured into three major chapters that guide you from the foundations of IOR to advanced digital retrieval systems.
    Information Organization and Retrieval (IOR) is the foundation of modern knowledge management. It ensures that information is not only collected but also organized and made accessible for learning, research, and decision-making. This course is divided into three main chapters: Introduction, Metadata Standards, and Digital Web-Based Information Retrieval.
    Chapter 1: Introduction
    This chapter provides an overview of the meaning and importance of organizing information. It explores the relationship between data, information, and knowledge, the historical development of cataloguing and classification, and the role of IOR in supporting learning and research. The introduction establishes the background upon which modern information systems are built.
    Chapter 2: Metadata Standards
    Metadata, often described as “data about data,” provides structure and meaning to information. This chapter focuses on:
    • Types of metadata: descriptive, structural, and administrative
    • Major standards such as Dublin Core, MARC, MODS, and RDF
    • The importance of metadata for interoperability and linked data
    • Practical applications in libraries, archives, and digital repositories
    Metadata acts as the backbone of information systems, ensuring that digital resources are searchable and retrievable.
    Chapter 3: Digital Web-Based Information Retrieval
    With the rise of the Internet, retrieval systems play a vital role in finding relevant information. This chapter examines:
    • Search engines and academic databases
    • Retrieval models such as Boolean, vector space, and probabilistic models
    • Web crawlers and indexing
    • Emerging trends such as semantic search and AI-powered retrieval systems
    • Challenges such as information overload, ranking, and bias in retrieval
    This chapter connects theory to real-world applications, demonstrating how retrieval systems influence everyday access to knowledge.
    Resources
    • Metadata tools: Dublin Core Generator, Open Metadata Registry
    • Reference managers: Zotero, Mendeley
    • Digital libraries: DOAJ, Project Gutenberg
    • Search tools: Google Scholar, Scopus, Semantic Scholar

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