Concepts aren't always named

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Concepts aren't always named

◄ Logical positivism

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A case of professional, social and automatic subject indexing ►

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Why are Knowledge Organisation Systems Important?

Learning Objectives and Methods

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Unit I – Knowledge organisation: what is it?

1.1.1 Core Concepts and Origins of Knowledge Organisation and Knowledge Organisation Systems
1.1.2 KOS and Information Retrieval
1.1.3 Types of Knowledge Organisation Systems I
1.1.4 Types of Knowledge Organisation Systems II
1.1.5 Exercises

Subject Headings

Searching for Macedonia

Unit II - Knowledge organisation in action

2.1 KO in digital humanities

2.1.1 Identifying relevant concepts
2.1.2 ICONCLASS
2.1.3 UNESCO Thesaurus
2.1.4 Folksonomies and social tagging

2.2 Practical exercises

2.3.1 Exercise: searching digital historical newspapers with KOS
2.3.2 Exercise: subject indexing of performing arts
2.3.3 Exercise: subject indexing of images

Unit III – Going automatic

3.1 How automatic methods work

3.1.1 Why automatic methods?
3.1.2 Terminology
3.1.3 Basic principles
3.1.4 Common approaches

Text pre-processing

Which approach would you choose when adding to an existing collection?

Which approach would you choose when an independent collection?

3.2 Automation and evaluation challenges

3.2.1 Automation challenges
3.2.2 Evaluation challenges
3.2.3 Application in operative systems

Logical positivism

Concepts aren't always named

A case of professional, social and automatic subject indexing

Unit IV - The big picture

4.1 Combining KO approaches

4.1.1 Pros and cons of manual, automatic and social KO
4.1.2 Case study 1: Enhancing social tagging with KOS and automatic suggestions
4.1.3 Case study 2: Searching for (LGBTQ+) fiction
4.1.4 Assignment (for flipped-classroom use)

4.2 KO at the search interface

4.2.1 The role of the search interface
4.2.2 Case study of search functionalities
4.2.3 Implications of identified KO complexities

Combining KO approaches

The role of the search interface

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