Text Retrieval Systems In Information Management

This Book Aims At Helping The Reader Develop A Clear Under- Standing Of Text Retrieval Systems, Including Its Nature And Characteristics; Steps To Be Followed In Developing A Text Retrieval System; Software Packages Available For The Purpose; Guidelines For Choosing An Appropriate Software, And So On. To Make The Text Suitable For All Kinds Of Readers, Chapters And The Basics Of Database Technology, Database Management, And File Structures Appropriate For Text Retrieval Systems Have Been Provided. This Book Also Discusses The Major Features Of Library Management Systems (Lmss), The Software Packages Used For Automating Library House-Keeping Operations.The Trend Is To Developing Systems Which Can Provide The Actual Information Sought By The Use Rather Than Reference To The Information Sources Or Part Of The Text Where The Search Term Appears. Such Systems Apply Expert Systems And Natural Language Processing Techniques, And Are Called Knowledge-Based Systems (Kbss). This Book Describes Features Of These Systems And Mentions Some Of The Applications Of Kbss In Library And Information Activities.

What is metadata and what do I need to know about it? These are two key questions for the information professional operating in the digital age as more and more information resources are available in electronic format. This is a thought-provoking introduction to metadata written by one of its leading advocates. It assesses the current theory and practice of metadata and examines key developments - including global initiatives and multilingual issues - in terms of both policy and technology. Subjects discussed include: What is metadata? definitions and concepts Retrieval environments: web; library catalogues; documents and records management; GIS; e-Learning Using metadata to enhance retrieval: pointing to content; subject retrieval; language control and indexing Information management issues: interoperability; information security; authority control; authentication and legal admissibility of evidence; records management and document lifecyc≤ preservation issues Application of metadata to information management: document and records management; content management systems for the internet Managing metadata: how to develop a schema Standards development: Dublin Core; UK Government metadata standards (eGIF); IFLA FRBR Model for cataloguing resources Looking forward: the semantic web; the Web Ontology Working Group. Readership: This book will be essential reading for network-oriented librarians and information workers in all sectors and for LIS students. In addition, it will provide useful background reading for computer staff supporting information services. Publishers, policy makers and practitioners in other curatorial traditions such as museums work or archiving will also find much of relevance.

Aims to make accessible the process of information retrieval of texts or abstracts. Although a knowledge of a higher order language (PASCAL, C, or BASIC) and elementary algebra is useful, the general meaning can be suitably appreciated with only slight knowledge of information technology.

Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. This has led to an increasing demand for powerful software tools to help people analyze and manage vast amounts of text data effectively and efficiently. Unlike data generated by a computer system or sensors, text data are usually generated directly by humans, and are accompanied by semantically rich content. As such, text data are especially valuable for discovering knowledge about human opinions and preferences, in addition to many other kinds of knowledge that we encode in text. In contrast to structured data, which conform to well-defined schemas (thus are relatively easy for computers to handle), text has less explicit structure, requiring computer processing toward understanding of the content encoded in text. The current technology of natural language processing has not yet reached a point to enable a computer to precisely understand natural language text, but a wide range of statistical and heuristic approaches to analysis and management of text data have been developed over the past few decades. They are usually very robust and can be applied to analyze and manage text data in any natural language, and about any topic. This book provides a systematic introduction to all these approaches, with an emphasis on covering the most useful knowledge and skills required to build a variety of practically useful text information systems. The focus is on text mining applications that can help users analyze patterns in text data to extract and reveal useful knowledge. Information retrieval systems, including search engines and recommender systems, are also covered as supporting technology for text mining applications. The book covers the major concepts, techniques, and ideas in text data mining and information retrieval from a practical viewpoint, and includes many hands-on exercises designed with a companion software toolkit (i.e., MeTA) to help readers learn how to apply techniques of text mining and information retrieval to real-world text data and how to experiment with and improve some of the algorithms for interesting application tasks. The book can be used as a textbook for a computer science undergraduate course or a reference book for practitioners working on relevant problems in analyzing and managing text data.

This will be the third edition of the highly successful 'Text Information Retrieval Systems'. The book's purpose is to teach people who will be searching or designing text retrieval systems how the systems work. For designers, it covers problems they will face and reviews currently available solutions to provide a basis for more advanced study. For the searcher its purpose is to describe why such systems work as they do. The book is primarily about computer-based retrieval systems, but the principles apply to nonmechanized ones as well. The book covers the nature of information, how it is orga.

Papers of the Institute of Information Scientists Text Retrieval Conferences: The Users Perspective (1988) and Text Management (1989). No index. Acidic paper. Annotation copyrighted by Book News, Inc., Portland, OR

In recent years, there have been several attempts to define a logic for information retrieval (IR). The aim was to provide a rich and uniform representation of information and its semantics with the goal of improving retrieval effectiveness. The basis of a logical model for IR is the assumption that queries and documents can be represented effectively by logical formulae. To retrieve a document, an IR system has to infer the formula representing the query from the formula representing the document. This logical interpretation of query and document emphasizes that relevance in IR is an inference process. The use of logic to build IR models enables one to obtain models that are more general than earlier well-known IR models. Indeed, some logical models are able to represent within a uniform framework various features of IR systems such as hypermedia links, multimedia data, and user's knowledge. Logic also provides a common approach to the integration of IR systems with logical database systems. Finally, logic makes it possible to reason about an IR model and its properties. This latter possibility is becoming increasingly more important since conventional evaluation methods, although good indicators of the effectiveness of IR systems, often give results which cannot be predicted, or for that matter satisfactorily explained. However, logic by itself cannot fully model IR. The success or the failure of the inference of the query formula from the document formula is not enough to model relevance in IR. It is necessary to take into account the uncertainty inherent in such an inference process. In 1986, Van Rijsbergen proposed the uncertainty logical principle to model relevance as an uncertain inference process. When proposing the principle, Van Rijsbergen was not specific about which logic and which uncertainty theory to use. As a consequence, various logics and uncertainty theories have been proposed and investigated. The choice of an appropriate logic and uncertainty mechanism has been a main research theme in logical IR modeling leading to a number of logical IR models over the years. Information Retrieval: Uncertainty and Logics contains a collection of exciting papers proposing, developing and implementing logical IR models. This book is appropriate for use as a text for a graduate-level course on Information Retrieval or Database Systems, and as a reference for researchers and practitioners in industry.