Recent technological progress in computer science, Web technologies, and the constantly evolving information available on the Internet has drastically changed the landscape of search and access to information. Current search engines employ advanced techniques involving machine learning, social networks, and semantic analysis. Next Generation Search Engines: Advanced Models for Information Retrieval is intended for scientists and decision-makers who wish to gain working knowledge about search in order to evaluate available solutions and to dialogue with software and data providers. The book aims to provide readers with a better idea of the new trends in applied research.

This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. Search Engines: Information Retrieval in Practice is ideal for introductory information retrieval courses at the undergraduate and graduate level in computer science, information science and computer engineering departments. It is also a valuable tool for search engine and information retrieval professionals. Written by a leader in the field of information retrieval, Search Engines: Information Retrieval in Practice , is designed to give undergraduate students the understanding and tools they need to evaluate, compare and modify search engines. Coverage of the underlying IR and mathematical models reinforce key concepts. The book’s numerous programming exercises make extensive use of Galago, a Java-based open source search engine.

This research volume presents a sample of recent contributions related to the issue of quality-assessment for Web Based information in the context of information access, retrieval, and filtering systems. The advent of the Web and the uncontrolled process of documents' generation have raised the problem of declining quality assessment to information on the Web, by considering both the nature of documents (texts, images, video, sounds, and so on), the genre of documents ( news, geographic information, ontologies, medical records, products records, and so on), the reputation of information sources and sites, and, last but not least the actions performed on documents (content indexing, retrieval and ranking, collaborative filtering, and so on). The volume constitutes a compendium of both heterogeneous approaches and sample applications focusing specific aspects of the quality assessment for Web-based information for researchers, PhD students and practitioners carrying out their research activity in the field of Web information retrieval and filtering, Web information mining, information quality representation and management.

This book is an essential reference to cutting-edge issues and future directions in information retrieval Information retrieval (IR) can be defined as the process of representing, managing, searching, retrieving, and presenting information. Good IR involves understanding information needs and interests, developing an effective search technique, system, presentation, distribution and delivery. The increased use of the Web and wider availability of information in this environment led to the development of Web search engines. This change has brought fresh challenges to a wider variety of users’ needs, tasks, and types of information. Today, search engines are seen in enterprises, on laptops, in individual websites, in library catalogues, and elsewhere. Information Retrieval: Searching in the 21st Century focuses on core concepts, and current trends in the field. This book focuses on: Information Retrieval Models User-centred Evaluation of Information Retrieval Systems Multimedia Resource Discovery Image Users’ Needs and Searching Behaviour Web Information Retrieval Mobile Search Context and Information Retrieval Text Categorisation and Genre in Information Retrieval Semantic Search The Role of Natural Language Processing in Information Retrieval: Search for Meaning and Structure Cross-language Information Retrieval Performance Issues in Parallel Computing for Information Retrieval This book is an invaluable reference for graduate students on IR courses or courses in related disciplines (e.g. computer science, information science, human-computer interaction, and knowledge management), academic and industrial researchers, and industrial personnel tracking information search technology developments to understand the business implications. Intermediate-advanced level undergraduate students on IR or related courses will also find this text insightful. Chapters are supplemented with exercises to stimulate further thinking.

Algorithms for Automating Open Source Intelligence (OSINT) presents information on the gathering of information and extraction of actionable intelligence from openly available sources, including news broadcasts, public repositories, and more recently, social media. As OSINT has applications in crime fighting, state-based intelligence, and social research, this book provides recent advances in text mining, web crawling, and other algorithms that have led to advances in methods that can largely automate this process. The book is beneficial to both practitioners and academic researchers, with discussions of the latest advances in applications, a coherent set of methods and processes for automating OSINT, and interdisciplinary perspectives on the key problems identified within each discipline. Drawing upon years of practical experience and using numerous examples, editors Robert Layton, Paul Watters, and a distinguished list of contributors discuss Evidence Accumulation Strategies for OSINT, Named Entity Resolution in Social Media, Analyzing Social Media Campaigns for Group Size Estimation, Surveys and qualitative techniques in OSINT, and Geospatial reasoning of open data. Presents a coherent set of methods and processes for automating OSINT Focuses on algorithms and applications allowing the practitioner to get up and running quickly Includes fully developed case studies on the digital underground and predicting crime through OSINT Discusses the ethical considerations when using publicly available online data