Search (5 results, page 1 of 1)

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Content

Inhalt: The importance of information need -- The history of information need -- The framework for our discussion -- Modeling the user in information search -- Information seeking's conceptualization of information need during information search -- Information use -- Adaptation : internal information flows and knowledge generation -- A theory of information need -- How information need works -- The user's situation in the pre-focus search -- The situation of user's information need in pre-focus information search -- The selection concept -- A review of the user's pre-focus information search -- How information need works in a focusing search -- Circles 1 to 5 : how information need works -- Corroborating research -- Applying information need -- The astrolabe : an information system for stage 3 information exploration -- Conclusion.

LCSH

Information retrieval
Information storage and retrieval systems

RSWK

Informationsverhalten / Information Retrieval / Informationstheorie

Subject

Informationsverhalten / Information Retrieval / Informationstheorie
Information retrieval
Information storage and retrieval systems

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Content

Inhalt: Basic concepts -- What is human information interaction? -- Theoretical constructs and models in information seeking behavior -- The information need -- The search strategy -- Two generations of research -- In-context -- Theoretical traditions in human information behavior -- Interlude : models and their contribution to design -- Human information behavior and information retrieval : is collaboration possible? -- Cognitive work analysis : dimensions for analysis -- Cognitive work analysis : harnessing complexity -- Enhancing the impact of research in human information interaction.

LCSH

Information storage and retrieval systems
Information retrieval

RSWK

Anthropologie / Information Retrieval / Informationsverhalten (BVB)
Informationsverhalten / Information Retrieval / Mensch-Maschine-Kommunikation

Subject

Anthropologie / Information Retrieval / Informationsverhalten (BVB)
Informationsverhalten / Information Retrieval / Mensch-Maschine-Kommunikation
Information storage and retrieval systems
Information retrieval

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Abstract

Recent technological progress in computer science, Web technologies, and constantly evolving information available on the Internet has drastically changed the landscape of search and access to information. Web search has significantly evolved in recent years. In the beginning, web search engines such as Google and Yahoo! were only providing search service over text documents. Aggregated search was one of the first steps to go beyond text search, and was the beginning of a new era for information seeking and retrieval. These days, new web search engines support aggregated search over a number of vertices, and blend different types of documents (e.g., images, videos) in their search results. New search engines employ advanced techniques involving machine learning, computational linguistics and psychology, user interaction and modeling, information visualization, Web engineering, artificial intelligence, distributed systems, social networks, statistical analysis, semantic analysis, and technologies over query sessions. Documents no longer exist on their own; they are connected to other documents, they are associated with users and their position in a social network, and they can be mapped onto a variety of ontologies. Similarly, retrieval tasks have become more interactive and are solidly embedded in a user's geospatial, social, and historical context. It is conjectured that new breakthroughs in information retrieval will not come from smarter algorithms that better exploit existing information sources, but from new retrieval algorithms that can intelligently use and combine new sources of contextual metadata.
With the rapid growth of web-based applications, such as search engines, Facebook, and Twitter, the development of effective and personalized information retrieval techniques and of user interfaces is essential. The amount of shared information and of social networks has also considerably grown, requiring metadata for new sources of information, like Wikipedia and ODP. These metadata have to provide classification information for a wide range of topics, as well as for social networking sites like Twitter, and Facebook, each of which provides additional preferences, tagging information and social contexts. Due to the explosion of social networks and other metadata sources, it is an opportune time to identify ways to exploit such metadata in IR tasks such as user modeling, query understanding, and personalization, to name a few. Although the use of traditional metadata such as html text, web page titles, and anchor text is fairly well-understood, the use of category information, user behavior data, and geographical information is just beginning to be studied. This book is intended for scientists and decision-makers who wish to gain working knowledge about search engines in order to evaluate available solutions and to dialogue with software and data providers.

Content

Enthält die Beiträge: Das, A., A. Jain: Indexing the World Wide Web: the journey so far. Ke, W.: Decentralized search and the clustering paradox in large scale information networks. Roux, M.: Metadata for search engines: what can be learned from e-Sciences? Fluhr, C.: Crosslingual access to photo databases. Djioua, B., J.-P. Desclés u. M. Alrahabi: Searching and mining with semantic categories. Ghorbel, H., A. Bahri u. R. Bouaziz: Fuzzy ontologies building platform for Semantic Web: FOB platform. Lassalle, E., E. Lassalle: Semantic models in information retrieval. Berry, M.W., R. Esau u. B. Kiefer: The use of text mining techniques in electronic discovery for legal matters. Sleem-Amer, M., I. Bigorgne u. S. Brizard u.a.: Intelligent semantic search engines for opinion and sentiment mining. Hoeber, O.: Human-centred Web search.
Vert, S.: Extensions of Web browsers useful to knowledge workers. Chen, L.-C.: Next generation search engine for the result clustering technology. Biskri, I., L. Rompré: Using association rules for query reformulation. Habernal, I., M. Konopík u. O. Rohlík: Question answering. Grau, B.: Finding answers to questions, in text collections or Web, in open domain or specialty domains. Berri, J., R. Benlamri: Context-aware mobile search engine. Bouidghaghen, O., L. Tamine: Spatio-temporal based personalization for mobile search. Chaudiron, S., M. Ihadjadene: Studying Web search engines from a user perspective: key concepts and main approaches. Karaman, F.: Artificial intelligence enabled search engines (AIESE) and the implications. Lewandowski, D.: A framework for evaluating the retrieval effectiveness of search engines.

LCSH

Information retrieval
Information retrieval / Research
Information storage and retrieval systems / Research

Subject

Information retrieval
Information retrieval / Research
Information storage and retrieval systems / Research

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RSWK

Information Retrieval

Subject

Information Retrieval

Theme

Semantisches Umfeld in Indexierung u. Retrieval

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Series

Synthesis lectures on information concepts, retrieval, and services; 61

Theme

Semantisches Umfeld in Indexierung u. Retrieval