Search (94 results, page 1 of 5)

  • × theme_ss:"Suchmaschinen"
  • × year_i:[2010 TO 2020}
  1. Chen, L.-C.: Next generation search engine for the result clustering technology (2012) 0.02 
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    Abstract
    Result clustering has recently attracted a lot of attention to provide the users with a succinct overview of relevant search results than traditional search engines. This chapter proposes a mixed clustering method to organize all returned search results into a hierarchical tree structure. The clustering method accomplishes two main tasks, one is label construction and the other is tree building. This chapter uses precision to measure the quality of clustering results. According to the results of experiments, the author preliminarily concluded that the performance of the system is better than many other well-known commercial and academic systems. This chapter makes several contributions. First, it presents a high performance system based on the clustering method. Second, it develops a divisive hierarchical clustering algorithm to organize all returned snippets into hierarchical tree structure. Third, it performs a wide range of experimental analyses to show that almost all commercial systems are significantly better than most current academic systems.
    Date
    17. 4.2012 15:22:11
    Source
    Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a
  2. Gossen, T.: Search engines for children : search user interfaces and information-seeking behaviour (2016) 0.01 
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    Abstract
    The doctoral thesis of Tatiana Gossen formulates criteria and guidelines on how to design the user interfaces of search engines for children. In her work, the author identifies the conceptual challenges based on own and previous user studies and addresses the changing characteristics of the users by providing a means of adaptation. Additionally, a novel type of search result visualisation for children with cartoon style characters is developed taking children's preference for visual information into account.
    Content
    Inhalt: Acknowledgments; Abstract; Zusammenfassung; Contents; List of Figures; List of Tables; List of Acronyms; Chapter 1 Introduction ; 1.1 Research Questions; 1.2 Thesis Outline; Part I Fundamentals ; Chapter 2 Information Retrieval for Young Users ; 2.1 Basics of Information Retrieval; 2.1.1 Architecture of an IR System; 2.1.2 Relevance Ranking; 2.1.3 Search User Interfaces; 2.1.4 Targeted Search Engines; 2.2 Aspects of Child Development Relevant for Information Retrieval Tasks; 2.2.1 Human Cognitive Development; 2.2.2 Information Processing Theory; 2.2.3 Psychosocial Development 2.3 User Studies and Evaluation2.3.1 Methods in User Studies; 2.3.2 Types of Evaluation; 2.3.3 Evaluation with Children; 2.4 Discussion; Chapter 3 State of the Art ; 3.1 Children's Information-Seeking Behaviour; 3.1.1 Querying Behaviour; 3.1.2 Search Strategy; 3.1.3 Navigation Style; 3.1.4 User Interface; 3.1.5 Relevance Judgement; 3.2 Existing Algorithms and User Interface Concepts for Children; 3.2.1 Query; 3.2.2 Content; 3.2.3 Ranking; 3.2.4 Search Result Visualisation; 3.3 Existing Information Retrieval Systems for Children; 3.3.1 Digital Book Libraries; 3.3.2 Web Search Engines 3.4 Summary and DiscussionPart II Studying Open Issues ; Chapter 4 Usability of Existing Search Engines for Young Users ; 4.1 Assessment Criteria; 4.1.1 Criteria for Matching the Motor Skills; 4.1.2 Criteria for Matching the Cognitive Skills; 4.2 Results; 4.2.1 Conformance with Motor Skills; 4.2.2 Conformance with the Cognitive Skills; 4.2.3 Presentation of Search Results; 4.2.4 Browsing versus Searching; 4.2.5 Navigational Style; 4.3 Summary and Discussion; Chapter 5 Large-scale Analysis of Children's Queries and Search Interactions; 5.1 Dataset; 5.2 Results; 5.3 Summary and Discussion Chapter 6 Differences in Usability and Perception of Targeted Web Search Engines between Children and Adults 6.1 Related Work; 6.2 User Study; 6.3 Study Results; 6.4 Summary and Discussion; Part III Tackling the Challenges ; Chapter 7 Search User Interface Design for Children ; 7.1 Conceptual Challenges and Possible Solutions; 7.2 Knowledge Journey Design; 7.3 Evaluation; 7.3.1 Study Design; 7.3.2 Study Results; 7.4 Voice-Controlled Search: Initial Study; 7.4.1 User Study; 7.5 Summary and Discussion; Chapter 8 Addressing User Diversity ; 8.1 Evolving Search User Interface 8.1.1 Mapping Function8.1.2 Evolving Skills; 8.1.3 Detection of User Abilities; 8.1.4 Design Concepts; 8.2 Adaptation of a Search User Interface towards User Needs; 8.2.1 Design & Implementation; 8.2.2 Search Input; 8.2.3 Result Output; 8.2.4 General Properties; 8.2.5 Configuration and Further Details; 8.3 Evaluation; 8.3.1 Study Design; 8.3.2 Study Results; 8.3.3 Preferred UI Settings; 8.3.4 User satisfaction; 8.4 Knowledge Journey Exhibit; 8.4.1 Hardware; 8.4.2 Frontend; 8.4.3 Backend; 8.5 Summary and Discussion; Chapter 9 Supporting Visual Searchers in Processing Search Results 9.1 Related Work
    Date
    1. 2.2016 18:25:22
    LCSH
    Information storage and retrieval
    Subject
    Information storage and retrieval
  3. Chaudiron, S.; Ihadjadene, M.: Studying Web search engines from a user perspective : key concepts and main approaches (2012) 0.01 
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    Abstract
    This chapter shows that the wider use of Web search engines, reconsidering the theoretical and methodological frameworks to grasp new information practices. Beginning with an overview of the recent challenges implied by the dynamic nature of the Web, this chapter then traces the information behavior related concepts in order to present the different approaches from the user perspective. The authors pay special attention to the concept of "information practice" and other related concepts such as "use", "activity", and "behavior" largely used in the literature but not always strictly defined. The authors provide an overview of user-oriented studies that are meaningful to understand the different contexts of use of electronic information access systems, focusing on five approaches: the system-oriented approaches, the theories of information seeking, the cognitive and psychological approaches, the management science approaches, and the marketing approaches. Future directions of work are then shaped, including social searching and the ethical, cultural, and political dimensions of Web search engines. The authors conclude considering the importance of Critical theory to better understand the role of Web Search engines in our modern society.
    Date
    20. 4.2012 13:22:37
    Source
    Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a
  4. Horch, A.; Kett, H.; Weisbecker, A.: Semantische Suchsysteme für das Internet : Architekturen und Komponenten semantischer Suchmaschinen (2013) 0.01 
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    RSWK
    Suchmaschine / Semantic Web / Information Retrieval
    Suchmaschine / Information Retrieval / Ranking / Datenstruktur / Kontextbezogenes System
    Subject
    Suchmaschine / Semantic Web / Information Retrieval
    Suchmaschine / Information Retrieval / Ranking / Datenstruktur / Kontextbezogenes System
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  5. Bouidghaghen, O.; Tamine, L.: Spatio-temporal based personalization for mobile search (2012) 0.01 
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    Abstract
    The explosion of the information available on the Internet has made traditional information retrieval systems, characterized by one size fits all approaches, less effective. Indeed, users are overwhelmed by the information delivered by such systems in response to their queries, particularly when the latter are ambiguous. In order to tackle this problem, the state-of-the-art reveals that there is a growing interest towards contextual information retrieval (CIR) which relies on various sources of evidence issued from the user's search background and environment, in order to improve the retrieval accuracy. This chapter focuses on mobile context, highlights challenges they present for IR, and gives an overview of CIR approaches applied in this environment. Then, the authors present an approach to personalize search results for mobile users by exploiting both cognitive and spatio-temporal contexts. The experimental evaluation undertaken in front of Yahoo search shows that the approach improves the quality of top search result lists and enhances search result precision.
    Date
    20. 4.2012 13:19:22
    Source
    Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a
  6. Hoeber, O.: Human-centred Web search (2012) 0.01 
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    Abstract
    People commonly experience difficulties when searching the Web, arising from an incomplete knowledge regarding their information needs, an inability to formulate accurate queries, and a low tolerance for considering the relevance of the search results. While simple and easy to use interfaces have made Web search universally accessible, they provide little assistance for people to overcome the difficulties they experience when their information needs are more complex than simple fact-verification. In human-centred Web search, the purpose of the search engine expands from a simple information retrieval engine to a decision support system. People are empowered to take an active role in the search process, with the search engine supporting them in developing a deeper understanding of their information needs, assisting them in crafting and refining their queries, and aiding them in evaluating and exploring the search results. In this chapter, recent research in this domain is outlined and discussed.
    Source
    Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a
  7. Thelwall, M.: Assessing web search engines : a webometric approach (2011) 0.01 
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    Abstract
    Information Retrieval (IR) research typically evaluates search systems in terms of the standard precision, recall and F-measures to weight the relative importance of precision and recall (e.g. van Rijsbergen, 1979). All of these assess the extent to which the system returns good matches for a query. In contrast, webometric measures are designed specifically for web search engines and are designed to monitor changes in results over time and various aspects of the internal logic of the way in which search engine select the results to be returned. This chapter introduces a range of webometric measurements and illustrates them with case studies of Google, Bing and Yahoo! This is a very fertile area for simple and complex new investigations into search engine results.
    Source
    Innovations in information retrieval: perspectives for theory and practice. Eds.: A. Foster, u. P. Rafferty
  8. White, R.W.: Interactions with search systems (2016) 0.01 
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    Abstract
    Information seeking is a fundamental human activity. In the modern world, it is frequently conducted through interactions with search systems. The retrieval and comprehension of information returned by these systems is a key part of decision making and action in a broad range of settings. Advances in data availability coupled with new interaction paradigms, and mobile and cloud computing capabilities, have created a broad range of new opportunities for information access and use. In this comprehensive book for professionals, researchers, and students involved in search system design and evaluation, search expert Ryen White discusses how search systems can capitalize on new capabilities and how next-generation systems must support higher order search activities such as task completion, learning, and decision making. He outlines the implications of these changes for the evolution of search evaluation, as well as challenges that extend beyond search systems in areas such as privacy and societal benefit.
    RSWK
    Information Retrieval
    Subject
    Information Retrieval
  9. Fu, T.; Abbasi, A.; Chen, H.: ¬A focused crawler for Dark Web forums (2010) 0.01 
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    Abstract
    The unprecedented growth of the Internet has given rise to the Dark Web, the problematic facet of the Web associated with cybercrime, hate, and extremism. Despite the need for tools to collect and analyze Dark Web forums, the covert nature of this part of the Internet makes traditional Web crawling techniques insufficient for capturing such content. In this study, we propose a novel crawling system designed to collect Dark Web forum content. The system uses a human-assisted accessibility approach to gain access to Dark Web forums. Several URL ordering features and techniques enable efficient extraction of forum postings. The system also includes an incremental crawler coupled with a recall-improvement mechanism intended to facilitate enhanced retrieval and updating of collected content. Experiments conducted to evaluate the effectiveness of the human-assisted accessibility approach and the recall-improvement-based, incremental-update procedure yielded favorable results. The human-assisted approach significantly improved access to Dark Web forums while the incremental crawler with recall improvement also outperformed standard periodic- and incremental-update approaches. Using the system, we were able to collect over 100 Dark Web forums from three regions. A case study encompassing link and content analysis of collected forums was used to illustrate the value and importance of gathering and analyzing content from such online communities.
    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.6, S.1213-1231
  10. Croft, W.B.; Metzler, D.; Strohman, T.: Search engines : information retrieval in practice (2010) 0.01 
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    Abstract
    For introductory information retrieval courses at the undergraduate and graduate level in computer science, information science and computer engineering departments. 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. SUPPLEMENTS / Extensive lecture slides (in PDF and PPT format) / Solutions to selected end of chapter problems (Instructors only) / Test collections for exercises / Galago search engine
    LCSH
    Information retrieval
    Information Storage and Retrieval
    RSWK
    Suchmaschine / Information Retrieval
    Subject
    Suchmaschine / Information Retrieval
    Information retrieval
    Information Storage and Retrieval
  11. Rieh, S.Y.; Kim, Y.-M.; Markey, K.: Amount of invested mental effort (AIME) in online searching (2012) 0.01 
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    Abstract
    This research investigates how people's perceptions of information retrieval (IR) systems, their perceptions of search tasks, and their perceptions of self-efficacy influence the amount of invested mental effort (AIME) they put into using two different IR systems: a Web search engine and a library system. It also explores the impact of mental effort on an end user's search experience. To assess AIME in online searching, two experiments were conducted using these methods: Experiment 1 relied on self-reports and Experiment 2 employed the dual-task technique. In both experiments, data were collected through search transaction logs, a pre-search background questionnaire, a post-search questionnaire and an interview. Important findings are these: (1) subjects invested greater mental effort searching a library system than searching the Web; (2) subjects put little effort into Web searching because of their high sense of self-efficacy in their searching ability and their perception of the easiness of the Web; (3) subjects did not recognize that putting mental effort into searching was something needed to improve the search results; and (4) data collected from multiple sources proved to be effective for assessing mental effort in online searching.
    Source
    Information processing and management. 48(2012) no.6, S.1136-1150
  12. Hogan, A.; Harth, A.; Umbrich, J.; Kinsella, S.; Polleres, A.; Decker, S.: Searching and browsing Linked Data with SWSE : the Semantic Web Search Engine (2011) 0.01 
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    Abstract
    In this paper, we discuss the architecture and implementation of the Semantic Web Search Engine (SWSE). Following traditional search engine architecture, SWSE consists of crawling, data enhancing, indexing and a user interface for search, browsing and retrieval of information; unlike traditional search engines, SWSE operates over RDF Web data - loosely also known as Linked Data - which implies unique challenges for the system design, architecture, algorithms, implementation and user interface. In particular, many challenges exist in adopting Semantic Web technologies for Web data: the unique challenges of the Web - in terms of scale, unreliability, inconsistency and noise - are largely overlooked by the current Semantic Web standards. Herein, we describe the current SWSE system, initially detailing the architecture and later elaborating upon the function, design, implementation and performance of each individual component. In so doing, we also give an insight into how current Semantic Web standards can be tailored, in a best-effort manner, for use on Web data. Throughout, we offer evaluation and complementary argumentation to support our design choices, and also offer discussion on future directions and open research questions. Later, we also provide candid discussion relating to the difficulties currently faced in bringing such a search engine into the mainstream, and lessons learnt from roughly six years working on the Semantic Web Search Engine project.
  13. Fluhr, C.: Crosslingual access to photo databases (2012) 0.01 
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    Date
    17. 4.2012 14:25:22
    Source
    Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a
  14. Lewandowski, D.; Sünkler, S.: What does Google recommend when you want to compare insurance offerings? (2019) 0.01 
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    Abstract
    Purpose The purpose of this paper is to describe a new method to improve the analysis of search engine results by considering the provider level as well as the domain level. This approach is tested by conducting a study using queries on the topic of insurance comparisons. Design/methodology/approach The authors conducted an empirical study that analyses the results of search queries aimed at comparing insurance companies. The authors used a self-developed software system that automatically queries commercial search engines and automatically extracts the content of the returned result pages for further data analysis. The data analysis was carried out using the KNIME Analytics Platform. Findings Google's top search results are served by only a few providers that frequently appear in these results. The authors show that some providers operate several domains on the same topic and that these domains appear for the same queries in the result lists. Research limitations/implications The authors demonstrate the feasibility of this approach and draw conclusions for further investigations from the empirical study. However, the study is a limited use case based on a limited number of search queries. Originality/value The proposed method allows large-scale analysis of the composition of the top results from commercial search engines. It allows using valid empirical data to determine what users actually see on the search engine result pages.
    Date
    20. 1.2015 18:30:22
    Footnote
    Beitrag in einem Special Issue: Information Science in the German-speaking Countries
    Source
    Aslib journal of information management. 71(2019) no.3, S.310-324
  15. Ke, W.: Decentralized search and the clustering paradox in large scale information networks (2012) 0.01 
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    Abstract
    Amid the rapid growth of information today is the increasing challenge for people to navigate its magnitude. Dynamics and heterogeneity of large information spaces such as the Web raise important questions about information retrieval in these environments. Collection of all information in advance and centralization of IR operations are extremely difficult, if not impossible, because systems are dynamic and information is distributed. The chapter discusses some of the key issues facing classic information retrieval models and presents a decentralized, organic view of information systems pertaining to search in large scale networks. It focuses on the impact of network structure on search performance and discusses a phenomenon we refer to as the Clustering Paradox, in which the topology of interconnected systems imposes a scalability limit.
    Source
    Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a
  16. Lewandowski, D.; Spree, U.: Ranking of Wikipedia articles in search engines revisited : fair ranking for reasonable quality? (2011) 0.01 
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    Abstract
    This paper aims to review the fiercely discussed question of whether the ranking of Wikipedia articles in search engines is justified by the quality of the articles. After an overview of current research on information quality in Wikipedia, a summary of the extended discussion on the quality of encyclopedic entries in general is given. On this basis, a heuristic method for evaluating Wikipedia entries is developed and applied to Wikipedia articles that scored highly in a search engine retrieval effectiveness test and compared with the relevance judgment of jurors. In all search engines tested, Wikipedia results are unanimously judged better by the jurors than other results on the corresponding results position. Relevance judgments often roughly correspond with the results from the heuristic evaluation. Cases in which high relevance judgments are not in accordance with the comparatively low score from the heuristic evaluation are interpreted as an indicator of a high degree of trust in Wikipedia. One of the systemic shortcomings of Wikipedia lies in its necessarily incoherent user model. A further tuning of the suggested criteria catalog, for instance, the different weighing of the supplied criteria, could serve as a starting point for a user model differentiated evaluation of Wikipedia articles. Approved methods of quality evaluation of reference works are applied to Wikipedia articles and integrated with the question of search engine evaluation.
    Date
    30. 9.2012 19:27:22
    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.1, S.117-132
  17. Werner, K.: das Confirmation/Disconfirmation-Paradigma der Kundenzufriedenheit im Kontext des Information Retrieval : Größere Zufriedenheit durch bessere Suchmaschinen? (2010) 0.01 
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    Abstract
    In der vorgestellten Studie aus dem Bereich des interaktiven Information Retrieval wurde erstmals die Erwartungshaltung von Suchmaschinennutzern als mögliche Determinante der Benutzerzufriedenheit untersucht. Das experimentelle Untersuchungsdesign basiert auf einem betriebswirtschaftlichen Modell, das die Entstehung von Kundenzufriedenheit durch die Bestätigung bzw. Nicht-Bestätigung von Erwartungen erklärt. Ein zentrales Ergebnis dieser Studie ist, das bei der Messung von Benutzerzufriedenheit besonders auf den Messzeitpunkt zu achten ist. Des Weiteren konnte ein von der Systemgüte abhängiger Adaptionseffekt hinsichtlich der Relevanzbewertung der Benutzer nachgewiesen werden.
    Source
    Information - Wissenschaft und Praxis. 61(2010) H.6/7, S.385-396
  18. Johnson, F.; Rowley, J.; Sbaffi, L.: Exploring information interactions in the context of Google (2016) 0.01 
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    Abstract
    The study sets out to explore the factors that influence the evaluation of information and the judgments made in the process of finding useful information in web search contexts. Based on a diary study of 2 assigned tasks to search on Google and Google Scholar, factor analysis identified the core constructs of content, relevance, scope, and style, as well as informational and system "ease of use" as influencing the judgment that useful information had been found. Differences were found in the participants' evaluation of information across the search tasks on Google and on Google Scholar when identified by the factors related to both content and ease of use. The findings from this study suggest how searchers might critically evaluate information, and the study identifies a relation between the user's involvement in the information interaction and the influences of the perceived system ease of use and information design.
    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.4, S.824-840
  19. Jindal, V.; Bawa, S.; Batra, S.: ¬A review of ranking approaches for semantic search on Web (2014) 0.01 
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    Abstract
    With ever increasing information being available to the end users, search engines have become the most powerful tools for obtaining useful information scattered on the Web. However, it is very common that even most renowned search engines return result sets with not so useful pages to the user. Research on semantic search aims to improve traditional information search and retrieval methods where the basic relevance criteria rely primarily on the presence of query keywords within the returned pages. This work is an attempt to explore different relevancy ranking approaches based on semantics which are considered appropriate for the retrieval of relevant information. In this paper, various pilot projects and their corresponding outcomes have been investigated based on methodologies adopted and their most distinctive characteristics towards ranking. An overview of selected approaches and their comparison by means of the classification criteria has been presented. With the help of this comparison, some common concepts and outstanding features have been identified.
    Source
    Information processing and management. 50(2014) no.2, S.416-425
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  20. Next generation search engines : advanced models for information retrieval (2012) 0.00 
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    Abstract
    The main goal of this book is to transfer new research results from the fields of advanced computer sciences and information science to the design of new search engines. The readers will have a better idea of the new trends in applied research. The achievement of relevant, organized, sorted, and workable answers- to name but a few - from a search is becoming a daily need for enterprises and organizations, and, to a greater extent, for anyone. It does not consist of getting access to structural information as in standard databases; nor does it consist of searching information strictly by way of a combination of key words. It goes far beyond that. Whatever its modality, the information sought should be identified by the topics it contains, that is to say by its textual, audio, video or graphical contents. This is not a new issue. However, recent technological advances have completely changed the techniques being used. New Web technologies, the emergence of Intranet systems and the abundance of information on the Internet have created the need for efficient search and information access tools.
    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
    Information behavior
    Subject
    Information retrieval
    Information retrieval / Research
    Information storage and retrieval systems / Research
    Information behavior

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