The document discusses automatic classification in knowledge organization, highlighting its historical significance and integration with artificial intelligence for improved subject identification. It covers challenges in classification techniques, including semantic indexing and the application of facet classification principles. The conclusion emphasizes advancements in text categorization technologies, achieving classification accuracy of nearly 90 percent.

1. AUTOMATIC CLASSIFICATION
Presented by Abdul Moid

2. Introduction
 oldest and most prominent knowledge
organisation tools
 magic technology
 Projects
-DDC,UDC,LCC

3. Concep
t
 construction of a call number by computer
 artificial intelligence
 able to identify the subject and sub-subjects of the
document
 doubt about the capability of computers for
classification
 similar automatic production of title indexes or
keyword enhanced indexes
 attempts to design a powerful automatic

4.  Class Coverage and Characteristics
 Source of Knowledge
 Developing background knowledge
 Integration of multiple sources
Challenges

5. Classification Techniques/Models
 facet formula
 based on postulates and principles
 Analysing the title
 finding noun phrases,
 picking up isolate numbers,
 symbols,
 basic subject notation from the knowledge base,
etc.

6. Semantic Indexing Techniques
and Classification Models

7. Conclusion
Panigrahi
 representational model of Analytico Synthetic
Kim and Lee
 the facet classification principles
And Wang
 DDC
 state-of-the-art text categorization technologies
 classification accuracy of nearly 90 per cent is
achieved

8. THANK
YOU