1. T.Sharon - A.Frank 1 Internet Resources Discovery (IRD) Classic Information Retrieval (IR)

2. 2 T.Sharon - A.Frank Classical IR  Deals with Textual Information Retrieval.  Exists for a few decades, mostly for text repositories.  Pushed strongly with the development of the WWW for search engines.

3. 3 T.Sharon - A.Frank IR Topics and their Relationships Retrieved Models & Evaluation Improvements on Retrieval Efficient Processing Bibliographic Systems The Web Digital Libraries Interfaces & Visualization Multimedia Modeling & Searching TEXTUAL IR HUMAN COMPUTER- INTERACTION FOR IR APPLICATIONS FOR IR IR Vocabulary: http://www.cs.jhu.edu/~weiss/glossary.htmlhttp://www.cs.jhu.edu/~weiss/glossary.html

4. 4 T.Sharon - A.Frank Basic Architecture of an IR System DocumentsQueries Document Representation Query Representation Comparison

5. 5 T.Sharon - A.Frank Interaction of the User with the IR System Retrieval Browsing database

6. 6 T.Sharon - A.Frank What is a Query? Input: –query terms/words, should appear in the text –possibly conditions between them Output: –relevant documents –possibly ranked

7. 7 T.Sharon - A.Frank Information Retrieval Systems Generic information retrieval system select and return to the user desired documents from a large set of documents in accordance with criteria specified by the user. Retrieval Functions –document search (ad-hoc) the selection of documents from an existing collection of documents. –document routing (filtering) the dissemination of incoming documents to appropriate users on the basis of user interest profiles.

8. 8 T.Sharon - A.Frank The Process of Retrieving Information Text Databases Index DB Manager Module Indexing Text Operations Query Operations Searching Ranking User Interface Text Logical view Inverted file User feedback Retrieved docs User need

9. 9 T.Sharon - A.Frank IR Ranking Ranking algorithms –The central problem regarding IR systems is the issue of predicting which documents are relevant and which are not. –Ranking algorithms are at the core of IR systems. –A ranking algorithm operates on basic premises regarding document relevance according to distinct IR model.

10. 10 T.Sharon - A.Frank A Taxonomy of IR Models UserTaskUserTask Retrieval: Search Routing Browsing Boolean Vector Probabilistic Non-Overlapping Lists Proximal Nodes Classic Models Structured Models Flat Structure Guided Hypertext Browsing Fuzzy Extended Boolean Set Theoretic Generalized Vector Latent Semantic Index Neural Networks Algebraic Inference Network Belief Network Probabilistic

11. 11 T.Sharon - A.Frank Retrieval Models Associations Full Text + Structure Full TextIndex Terms Structured Classic: Set Theoretic Algebraic Probabilistic Retrieval Structure Guided Hypertext Flat Hypertext Flat Browsing Logical View of Documents USERTASKUSERTASK

12. 12 T.Sharon - A.Frank Query Language (1) Keyword-based Querying –Single-word Queries –Context Queries Phrase Proximity –Boolean Queries –Natural Language

13. 13 T.Sharon - A.Frank Query Language (2) Pattern Matching –Words –Prefixes –Suffixes –Substring –Ranges –Allowing errors –Regular expressions

14. 14 T.Sharon - A.Frank Query Language (3) Structural Queries –Form-like fixed structures –Hypertext structure –Hierarchical structure

15. 15 T.Sharon - A.Frank Structural Queries (a)form-like fixed structure, (b) hypertext structure, and (c) hierarchical structure

16. 16 T.Sharon - A.Frank Hierarchical Structure An example of a hierarchical structure: the page of a book, its schematic view, and a parsed query to retrieve the figure