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Advanced Topics in Information Retrieval / edited by Massimo Melucci, Ricardo Baeza-Yates
Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: The Information Retrieval Series ; 33 | SpringerLink BücherPublisher: Berlin, Heidelberg : Springer-Verlag Berlin Heidelberg, 2011Description: Online-Ressource (XXX, 306p. 87 illus, digital)ISBN:- 9783642209468
- 025.04
- 025.042/5
- QA75.5-76.95
- QA76.9.D3
Contents:
Summary: Information retrieval is the science concerned with the effective and efficient retrieval of documents starting from their semantic content. It is employed to fulfill some information need from a large number of digital documents. Given the ever-growing amount of documents available and the heterogeneous data structures used for storage, information retrieval has recently faced and tackled novel applications.In this book, Melucci and Baeza-Yates present a wide-spectrum illustration of recent research results in advanced areas related to information retrieval. Readers will find chapters on e.g. aggregated search, digital advertising, digital libraries, discovery of spam and opinions, information retrieval in context, multimedia resource discovery, quantum mechanics applied to information retrieval, scalability challenges in web search engines, and interactive information retrieval evaluation. All chapters are written by well-known researchers, are completely self-contained and comprehensive, and are complemented by an integrated bibliography and subject index.With this selection, the editors provide the most up-to-date survey of topics usually not addressed in depth in traditional (text)books on information retrieval. The presentation is intended for a wide audience of people interested in information retrieval: undergraduate and graduate students, post-doctoral researchers, lecturers, and industrial researchers.PPN: PPN: 1650970587Package identifier: Produktsigel: ZDB-2-SCS
Advanced Topics in Information Retrieval; Preface; About ESSIR; Contents; Contributors; Abbreviations; List of Figures; List of Tables; Chapter 1: Digital Libraries; 1.1 Introduction; 1.2 Digital Libraries in the Beginning; 1.3 Digital Library Systems; 1.3.1 User Interface to Digital Library Systems; 1.3.2 Significative Examples of Digital Library Systems; 1.3.2.1 DelosDLMS; 1.3.2.2 The European Library; 1.3.2.3 Europeana; 1.4 The Digital Library Manifesto and the DELOS Digital Library Reference Model
1.5 Digital Library Systems Become User-Centered Systems: Adding Advanced Annotation Functions to Digital Library Systems1.6 Interoperability between Digital Library Systems; 1.7 Evaluation of Digital Libraries; 1.8 Conclusions; Chapter 2: Scalability Challenges in Web Search Engines; 2.1 Introduction; 2.2 Components; 2.3 Objectives; 2.4 Parameters; 2.5 Scalability Issues; 2.5.1 Single-Node System; 2.5.1.1 Single-Node Crawling; 2.5.1.2 Single-Node Indexing; 2.5.1.3 Single-Node Query Processing; 2.5.2 Multi-Node Cluster; 2.5.2.1 Multi-Node Crawling; 2.5.2.2 Multi-Node Indexing
2.5.2.3 Multi-Node Query Processing2.5.3 Multi-Cluster Site; 2.5.3.1 Multi-Cluster Crawling; 2.5.3.2 Multi-Cluster Indexing; 2.5.3.3 Multi-Cluster Query Processing; 2.5.4 Multi-Site Engine; 2.5.4.1 Multi-Site Crawling; 2.5.4.2 Multi-Site Indexing; 2.5.4.3 Multi-Site Query Processing; 2.6 Open Problems; 2.6.1 Crawling; 2.6.2 Indexing; 2.6.3 Query Processing; 2.7 Conclusions; Chapter 3: Spam, Opinions, and Other Relationships: Towards a Comprehensive View of the Web Knowledge Discovery; 3.1 Introduction; 3.2 Basics and Terminology; 3.2.1 From Information Retrieval to Knowledge Discovery
3.2.2 Knowledge Discovery Phases3.2.3 Modeling Tasks and Model/Pattern Structures; 3.2.4 Learning Cycles and Knowledge Discovery; 3.2.5 Web Mining: Content, Structure and Usage; 3.3 A Short Overview of Web Content Mining on Text; 3.3.1 Application and Data Understanding; 3.3.1.1 Spam; 3.3.1.2 Opinions; 3.3.1.3 Relations; 3.3.1.4 Web Documents in General; 3.3.2 Data Preparation; 3.3.3 Modeling; 3.3.3.1 Classification; 3.3.3.2 Spam-Detection Modeling; 3.3.3.3 Opinion-Mining Modeling; 3.3.3.4 Relation-Mining Modeling; 3.3.4 Evaluation and Deployment; 3.4 New Challenges: Web Mining and Privacy
3.5 Conclusions3.5.1 Context; 3.5.2 Learning Cycles; 3.5.3 Definitional Power and Viewpoints; 3.5.4 Tools and Access; Chapter 4: The User in Interactive Information Retrieval Evaluation; 4.1 Introduction; 4.2 Research Frameworks, Models and Other Central Concepts; 4.2.1 Research Design and IIR Research Setting Types; 4.2.2 Central IIR Components; 4.2.3 The Integrated Cognitive Research Framework for IR; 4.3 IR Interaction-Research Designs with Test Persons; 4.3.1 Search Job Design, Simulated Task Situations, Test Persons and Evaluation Measures; Search Job Design; Simulated Search Jobs
Number of Test Persons and Search Jobs
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