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 nonpredefined query model for information retrieval system based on a relational database.

CHAPTER ONE

INTRODUCTION

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. It is correct that the set of retrieved documents are not ranked in Boolean searches. However, the cost of a ranked set is a set that is not fully controlled or understood by the user. In Boolean searches, the user obtains well‐defined search sets, which is a clear advantage if searching is considered a learning process. The well‐defined set provides better feedback and therefore allows modified search profiles. Hjørland (2014). Conventional IR systems are built on the Boolean model while most IR systems rely on sophisticated algorithms for better ranking. Most of the materials for ranking are usually documents of an unstructured nature (usually text). Today, research in the field IR are split among various activities and between (a) optimizing algorithms for ranked systems and (b) extending the Boolean model to increase the selection power of users. Researchers who are working on the storage side of the information retrieval system are engaged in designing sophisticated methods for identification and representation of the various bibliographic elements essential for documents, automatic content analysis, text processing and so on. On the other hand, researchers working on the retrieval side are attempting to develop sophisticated searching techniques, user interfaces, and various techniques for producing output for local as well as remote users. Chowdhury (2004).

Department: Computer Science (M.Sc Thesis)

Format: MS Word

Chapters: 1 - 5, Preliminary Pages, Abstract, References, Appendix.

No. of Pages: 52

Price: 20,000 NGN

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