Model and Development of Non‐Predefined Query in Information Retrieval System

Model and Development of Non‐Predefined Query in Information Retrieval System.

Abstract

The amount of information available today is extremely large. The increasing need for easier and faster information discovery demands optimal information retrieval techniques.

A measure of performance of any information retrieval system is based on the effectiveness and efficiency of retrieval. While some techniques rely on algorithms that improve search, others aim at increasing user’s ability to formulate search queries.

Here we present a non‐ predefined query model for information retrieval system based on a relational database.

Table Of Contents

Abstract…………………….. ii

Dedication…………… iii

Acknowledgement………….. iv

Table of contents…………. v

List of figures…………vii

List of tables…………vii

1. Introduction…………………….. 1
   • Research context………………….. 2
   • Problem statement…………… 2
   • Research objectives……….. 3
   • Research methodology…………. 3
   • Organization of work…………. 3
2. Literature review…………….. 4
   • Information Retrieval……4
   • Data or information retrieval?…… 8
   • IR Models 10
   • Boolean Retrieval……… 11
   • Data warehousing…… 13
   • Human-Computer Interface…… 14
     • User interfaces for search by Marti Hearst 14
     • Designing the User Interface by Ben Shneiderman 15
     • Models of Interaction……………………… 16
     • Design of Search Interfaces………………………….. 17
   • Related Works……………………….. 18
     1. David, D. Bueno, P. Kislin, Case-Based Reasoning, User model and IRS.18

3. Research proposal……………. 21
   • Information Retrieval System……… 21
   • Non-predefined Query Model…………… 22
   • Interface for IR…………… 24
   • Design Specifications………….. 26
4. Case study…………….. 32
   • Employee Records……… 32
   • Data Modeling…………………………… 32
     • Entity-Relation (ER) Model………………. 33
     • Graph of Relations……………………… 34
     • Dictionary of Attributes…………………….. 35
     • Dictionary for Query….. 35
   • Cross Tabulation……………. 38
5. Conclusion………………………….. 43
   • Contributions……………. 43
   • Perspectives……… 43

References…44

Introduction

Background Of Study

Information retrieval is obtaining information by searching a repository for items that match user’s information need.

According to Losee (1998), retrieval systems often order documents in a manner consistent with the assumptions of Boolean logic, by retrieving, for example, documents that have the terms dogs and cats, and by not retrieving documents without one of these terms.

Systems consistent with the probabilistic model of retrieval locate documents based on a query list of terms, such as {dogs, cats}, or may accept as input a natural language query, such as I want information on dogs and cats.

A probabilistic system then ranks documents for retrieval by assigning a numeric value to each document, based on the weights for query terms and the frequencies of term occurrences in documents.

We want to know how to “best” formulate a query, and our ultimate interest in measures of human utility: how satisfied is each user with the results the system gives for each information need that they pose? Manning et al (2009).

Most everyday users of IR systems expect IR systems to do ranked retrieval, unfortunately relevance ranking is often not critical in Boolean systems.

On the other hand, most IR systems rank documents by their estimation of the usefulness of a document for a user query, and there is little or nothing a user can do about it.

However, many power users still use Boolean systems as they feel more in control of the retrieval process.

References

Olubunmi Christianah Akintade, Case based reasoning applied to Information Retrieval System. M.Sc. thesis, 2007.

Ricardo Baeza‐Yates, Berthier Ribeiro‐Neto. Modern Information Retrieval: The Concepts and Technology behind Search Second edition, 1999

Chowdhury, Basic concepts of information retrieval systems, 2004.

Amos David, David Bueno, Philippe Kislin, Case‐Based Reasoning, User model and IRS Marti Hearst , User Interfaces for Search, 2009.

Birger Hjørland, Classical Databases and Knowledge Organization: A Case for Boolean Retrieval and Human Decision‐Making During Searches. Journal of The Association For Information Science And Technology, 2014.