INTRODUCTION:

1. The main aim of this course is to present the scientific support in the field of information search and retrieval.
2. This course explores the fundamental relationship between information retrieval, hypermedia architectures, and semantic models, thus deploying and testing several important retrieval models such as vector space, Boolean and query expansion.
3. It discusses implementation and evaluation issues of new algorithms like clustering, pattern searching, and stemming with advanced data/file structures, indirectly facilitating a platform to implement comprehensive catalogue of information search tools while designing an e-commerce web site

OBJECTIVES:

• Demonstrate genesis and diversity of information retrieval situations for text and hyper media
• Describe hands-on experience store, and retrieve information from www using semantic approaches
• Demonstrate the usage of different data/file structures in building computational search engines.
• Analyze the performance of information retrieval using advanced techniques such as classification, clustering, and filtering over multimedia
• Analyze ranked retrieval of a very large number of documents with hyperlinks between them.
• Demonstrate Information visualization technologies like Cognition and perception in the Internet or Web search engine

APPLICATIONS:

• Digital libraries
• Blog search
• Musical Retrieval
• Search engine search
• Video Retrieval

PREREQUISITES:

• Students must have the minimal concept of Data Base Management Systems
• They must also have the concept of different types of algorithms used for searching data
• They must also have the minimal knowledge of Natural language such as thesaurus, synonyms etc. to understand the concept of retrieving the textual information because text is the main data type used in Information Retrieval Systems

 OUTCOMES:

At the end of the course, the student should be able to:

CO1: Describe the objectives of information retrieval systems

CO2: Describe models like vector-space, probabilistic and language models to identify the similarity of query and document

CO3: Implement clustering algorithms like hierarchical agglomerative clustering and k-means algorithm.

CO4: Understand relevance feedback in vector space model and probabilistic model.

CO5: Illustrate how N-grams are used for detection and correction of spelling errors.

UNIT – I

Introduction to Information Retrieval Systems: Definition of Information Retrieval System, Objectives of Information Retrieval Systems, Functional Overview, Relationship to Database Management Systems, Digital Libraries and Data Warehouses Information Retrieval System Capabilities: Search Capabilities, Browse Capabilities, Miscellaneous Capabilities

UNIT – II

Cataloging and Indexing: History and Objectives of Indexing, Indexing Process, Automatic Indexing, Information Extraction Data Structure: Introduction to Data Structure, Stemming Algorithms, Inverted File Structure, N- Gram Data Structures, PAT Data Structure, Signature File Structure, Hypertext and XML Data Structures, Hidden Markov Models

UNIT – III

Automatic Indexing: Classes of Automatic Indexing, Statistical Indexing, Natural Language, Concept Indexing, Hypertext LinkagesDocument and Term Clustering: Introduction to Clustering, Thesaurus Generation, Item Clustering, Hierarchy of Clusters

UNIT – IV

User Search Techniques: Search Statements and Binding, Similarity Measures and Ranking, Relevance Feedback, Selective Dissemination of Information Search, Weighted Searches of Boolean Systems, Searching the INTERNET and Hypertext Information Visualization: Introduction to Information Visualization, Cognition and Perception, Information Visualization Technologies

UNIT – V

Text Search Algorithms: Introduction to Text Search Techniques, Software Text Search Algorithms, Hardware Text Search Systems Multimedia Information Retrieval: Spoken Language Audio Retrieval, Non-Speech Audio Retrieval, Graph Retrieval, Imagery Retrieval, Video Retrieval