Search (86 results, page 1 of 5)

0.010274886 = product of:
  0.041099545 = sum of:
    0.041099545 = weight(_text_:digitale in 3447) [ClassicSimilarity], result of:
      0.041099545 = score(doc=3447,freq=2.0), product of:
        0.18027179 = queryWeight, product of:
          5.158747 = idf(docFreq=690, maxDocs=44218)
          0.034944877 = queryNorm
        0.22798656 = fieldWeight in 3447, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.158747 = idf(docFreq=690, maxDocs=44218)
          0.03125 = fieldNorm(doc=3447)
  0.25 = coord(1/4)

Abstract

"Die Bayerische Staatsbibliothek testet den semantischen "Discovery Service" Yewno als zusätzliche thematische Suchmaschine für digitale Volltexte. Der Service ist unter folgendem Link erreichbar: https://www.bsb-muenchen.de/recherche-und-service/suchen-und-finden/yewno/. Das Identifizieren von Themen, um die es in einem Text geht, basiert bei Yewno alleine auf Methoden der künstlichen Intelligenz und des maschinellen Lernens. Dabei werden sie nicht - wie bei klassischen Katalogsystemen - einem Text als Ganzem zugeordnet, sondern der jeweiligen Textstelle. Die Eingabe eines Suchwortes bzw. Themas, bei Yewno "Konzept" genannt, führt umgehend zu einer grafischen Darstellung eines semantischen Netzwerks relevanter Konzepte und ihrer inhaltlichen Zusammenhänge. So ist ein Navigieren über thematische Beziehungen bis hin zu den Fundstellen im Text möglich, die dann in sogenannten Snippets angezeigt werden. In der Test-Anwendung der Bayerischen Staatsbibliothek durchsucht Yewno aktuell 40 Millionen englischsprachige Dokumente aus Publikationen namhafter Wissenschaftsverlage wie Cambridge University Press, Oxford University Press, Wiley, Sage und Springer, sowie Dokumente, die im Open Access verfügbar sind. Nach der dreimonatigen Testphase werden zunächst die Rückmeldungen der Nutzer ausgewertet. Ob und wann dann der Schritt von der klassischen Suchmaschine zum semantischen "Discovery Service" kommt und welche Bedeutung Anwendungen wie Yewno in diesem Zusammenhang einnehmen werden, ist heute noch nicht abzusehen. Die Software Yewno wurde vom gleichnamigen Startup in Zusammenarbeit mit der Stanford University entwickelt, mit der auch die Bayerische Staatsbibliothek eng kooperiert. [Inetbib-Posting vom 22.02.2017].

0.009081802 = product of:
  0.03632721 = sum of:
    0.03632721 = weight(_text_:digitale in 6) [ClassicSimilarity], result of:
      0.03632721 = score(doc=6,freq=4.0), product of:
        0.18027179 = queryWeight, product of:
          5.158747 = idf(docFreq=690, maxDocs=44218)
          0.034944877 = queryNorm
        0.20151356 = fieldWeight in 6, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.158747 = idf(docFreq=690, maxDocs=44218)
          0.01953125 = fieldNorm(doc=6)
  0.25 = coord(1/4)

Content

Keine ernsthafte Konkurrenz Damit ist die Geschichte der Suchmaschinen weitestgehend erzählt - außer, dass Yahoo praktisch keine Rolle mehr spielt. Um das Gesuchte in den Weiten des Netzes zu finden, benutzt man heute Google. Mehr als 90 Prozent aller Suchanfragen in Deutschland laufen Schätzungen zufolge über die Rechenzentren des US-Konzerns. Ernsthafte Konkurrenten? Keine. Einst erfolgreiche Dienste wie Excite, Infoseek, AltaVista oder Lycos sind längst von der Bildfläche verschwunden. Liefert nicht Google zu allen erdenklichen Suchbegriffen zigtausend Ergebnisse? Mehr, so die allgemeine Auffassung, kann sowieso kein Mensch verarbeiten. Dahinter steht der naive Glaube, Google bilde die digitale Welt in ihrer Gesamtheit ab. Oder, schlimmer noch, gar die reale Welt. Dabei könnte nichts weiter von der Realität entfernt sein, wie Dr. Wolfgang Sander-Beuermann, Leiter des Suchmaschinenlabors der Leibniz-Universität Hannover erklärt. Denn Google entscheidet, nach welchen Kriterien die digitale Welt durchkämmt wird. Google legt fest, welche Webseiten unter den ersten zehn Ergebnissen zu einer Suche angezeigt werden. Da die Mehrheit der Nutzer ohnehin nur diese wahrnimmt, bestimmt ein einzelnes Unternehmen, welchen Ausschnitt der Wirklichkeit die Menschheit zu sehen bekommt. Und die Algorithmen, nach denen die Suchmaschine funktioniert, hält der Konzern streng unter Verschluss: "Google entscheidet, welches Wissen wahrgenommen wird, und welches nicht; was im Internet existiert und was nicht." Die Macht, die dem Konzern damit zukomme, reiche weit über die Kontrolle des Wissenszugangs durch Suchmaschinen hinaus. "Was wir hier beobachten, ist eine Monokultur mit gravierenden Folgen für die Informations- und Wissenskultur", warnt der Wissenschaftler, der deshalb bereits vor Jahren "SuMa e.V.", einen "Verein für freien Wissenszugang", gegründet hat. Er setzt sich dafür ein, "globale Online-Oligopole besser zu kontrollieren". Um den freien Zugang zu dem im Internet gespeicherten Wissen für möglichst viele Menschen zu ermöglichen, sei es außerdem "von entscheidender Bedeutung, die dahinterstehende Technologie zu entwickeln und zu fördern - auch und gerade in Deutschland." Doch genau das wurde in den vergangenen zehn Jahren versäumt.

0.0059192525 = product of:
  0.02367701 = sum of:
    0.02367701 = weight(_text_:information in 2605) [ClassicSimilarity], result of:
      0.02367701 = score(doc=2605,freq=22.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.38596505 = fieldWeight in 2605, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=2605)
  0.25 = coord(1/4)

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

0.0053541656 = product of:
  0.021416662 = sum of:
    0.021416662 = weight(_text_:information in 94) [ClassicSimilarity], result of:
      0.021416662 = score(doc=94,freq=18.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.34911853 = fieldWeight in 94, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=94)
  0.25 = coord(1/4)

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

0.0053541656 = product of:
  0.021416662 = sum of:
    0.021416662 = weight(_text_:information in 2885) [ClassicSimilarity], result of:
      0.021416662 = score(doc=2885,freq=18.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.34911853 = fieldWeight in 2885, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=2885)
  0.25 = coord(1/4)

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

0.004371658 = product of:
  0.017486632 = sum of:
    0.017486632 = weight(_text_:information in 104) [ClassicSimilarity], result of:
      0.017486632 = score(doc=104,freq=12.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.2850541 = fieldWeight in 104, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=104)
  0.25 = coord(1/4)

Abstract

Exploiting context information in a web search engine helps fine-tuning web services and applications to deliver custom-made information to end users. While context, including user and environment information, cannot be exploited efficiently in the wired Internet interaction type, it is becoming accessible with the mobile web where users have an intimate relationship with their handsets. In this type of interaction, context plays a significant role enhancing information search and therefore, allowing a search engine to detect relevant content in all digital forms and formats. This chapter proposes a context model and an architecture that promote integration of context information for individuals and social communities to add value to their interaction with the mobile web. The architecture relies on efficient knowledge management of multimedia resources for a wide range of applications and web services. The research is illustrated with a corporate case study showing how efficient context integration improves usability of a mobile search engine.

Source

Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a

0.004371658 = product of:
  0.017486632 = sum of:
    0.017486632 = weight(_text_:information in 5363) [ClassicSimilarity], result of:
      0.017486632 = score(doc=5363,freq=12.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.2850541 = fieldWeight in 5363, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=5363)
  0.25 = coord(1/4)

Abstract

This study applies Kuhlthau's Information Search Process stage (ISP) model to understand bilingual users' Internet search experience. We conduct a quasi-field experiment with 30 bilingual searchers and the results suggested that the ISP model was applicable in studying searchers' information retrieval behavior in search tasks. The ISP model was applicable in studying searchers' information retrieval behavior in simple tasks. However, searchers' emotional responses differed from those of the ISP model for a complex task. By testing searchers using different search strategies, the results suggested that search engines with multilanguage search functions provide an advantage for bilingual searchers in the Internet's multilingual environment. The findings showed that when searchers used a search engine as a tool for problem solving, they might experience different feelings in each ISP stage than in searching for information for a term paper using a library. The results echo other research findings that indicate that information seeking is a multifaceted phenomenon.

Source

Journal of the Association for Information Science and Technology. 70(2019) no.9, S.1014-1025

0.004121639 = product of:
  0.016486555 = sum of:
    0.016486555 = weight(_text_:information in 4016) [ClassicSimilarity], result of:
      0.016486555 = score(doc=4016,freq=6.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.2687516 = fieldWeight in 4016, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0625 = fieldNorm(doc=4016)
  0.25 = coord(1/4)

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

0.0040730517 = product of:
  0.016292207 = sum of:
    0.016292207 = weight(_text_:information in 357) [ClassicSimilarity], result of:
      0.016292207 = score(doc=357,freq=60.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.26558346 = fieldWeight in 357, product of:
          7.745967 = tf(freq=60.0), with freq of:
            60.0 = termFreq=60.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.01953125 = fieldNorm(doc=357)
  0.25 = coord(1/4)

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.

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

0.0039907596 = product of:
  0.015963038 = sum of:
    0.015963038 = weight(_text_:information in 102) [ClassicSimilarity], result of:
      0.015963038 = score(doc=102,freq=10.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.2602176 = fieldWeight in 102, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=102)
  0.25 = coord(1/4)

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

0.0039907596 = product of:
  0.015963038 = sum of:
    0.015963038 = weight(_text_:information in 108) [ClassicSimilarity], result of:
      0.015963038 = score(doc=108,freq=10.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.2602176 = fieldWeight in 108, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=108)
  0.25 = coord(1/4)

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.

Source

Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a

0.0039907596 = product of:
  0.015963038 = sum of:
    0.015963038 = weight(_text_:information in 2799) [ClassicSimilarity], result of:
      0.015963038 = score(doc=2799,freq=10.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.2602176 = fieldWeight in 2799, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=2799)
  0.25 = coord(1/4)

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

0.0036430482 = product of:
  0.014572193 = sum of:
    0.014572193 = weight(_text_:information in 109) [ClassicSimilarity], result of:
      0.014572193 = score(doc=109,freq=12.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.23754507 = fieldWeight in 109, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=109)
  0.25 = coord(1/4)

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.

Source

Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a

0.0035694437 = product of:
  0.014277775 = sum of:
    0.014277775 = weight(_text_:information in 4373) [ClassicSimilarity], result of:
      0.014277775 = score(doc=4373,freq=8.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.23274569 = fieldWeight in 4373, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=4373)
  0.25 = coord(1/4)

Abstract

This paper reports on a transaction log analysis of the type and topic of search queries entered into the search engine Google (Australia). Two aspects, in particular, set this apart from previous studies: the sampling and analysis take account of the distribution of search queries, and lifestyle information of the searcher was matched with each search query. A surprising finding was that there was no observed statistically significant difference in search type or topics for different segments of the online population. It was found that queries about popular culture and Ecommerce accounted for almost half of all search engine queries and that half of the queries were entered with a particular Website in mind. The findings of this study also suggest that the Internet search engine is not only an interface to information or a shortcut to Websites, it is equally a site of leisure. This study has implications for the design and evaluation of search engines as well as our understanding of search engine use.

Source

Journal of the American Society for Information Science and Technology. 62(2011) no.4, S.761-775

0.0035694437 = product of:
  0.014277775 = sum of:
    0.014277775 = weight(_text_:information in 5021) [ClassicSimilarity], result of:
      0.014277775 = score(doc=5021,freq=2.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.23274569 = fieldWeight in 5021, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.09375 = fieldNorm(doc=5021)
  0.25 = coord(1/4)

Source

Information - Wissenschaft und Praxis. 70(2019) H.1, S.1-2

0.0033256328 = product of:
  0.013302531 = sum of:
    0.013302531 = weight(_text_:information in 3612) [ClassicSimilarity], result of:
      0.013302531 = score(doc=3612,freq=10.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.21684799 = fieldWeight in 3612, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3612)
  0.25 = coord(1/4)

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

0.0032922088 = product of:
  0.013168835 = sum of:
    0.013168835 = weight(_text_:information in 2752) [ClassicSimilarity], result of:
      0.013168835 = score(doc=2752,freq=20.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.21466857 = fieldWeight in 2752, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.02734375 = fieldNorm(doc=2752)
  0.25 = coord(1/4)

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

LCSH

Information storage and retrieval

Subject

Information storage and retrieval

0.003091229 = product of:
  0.012364916 = sum of:
    0.012364916 = weight(_text_:information in 96) [ClassicSimilarity], result of:
      0.012364916 = score(doc=96,freq=6.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.20156369 = fieldWeight in 96, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=96)
  0.25 = coord(1/4)

Abstract

E-sciences are data-intensive sciences that make a large use of the Web to share, collect, and process data. In this context, primary scientific data is becoming a new challenging issue as data must be extensively described (1) to account for empiric conditions and results that allow interpretation and/or analyses and (2) to be understandable by computers used for data storage and information retrieval. With this respect, metadata is a focal point whatever it is considered from the point of view of the user to visualize and exploit data as well as this of the search tools to find and retrieve information. Numerous disciplines are concerned with the issues of describing complex observations and addressing pertinent knowledge. In this paper, similarities and differences in data description and exploration strategies among disciplines in e-sciences are examined.

Source

Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a

0.003091229 = product of:
  0.012364916 = sum of:
    0.012364916 = weight(_text_:information in 110) [ClassicSimilarity], result of:
      0.012364916 = score(doc=110,freq=6.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.20156369 = fieldWeight in 110, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=110)
  0.25 = coord(1/4)

Abstract

Search engines are the major means of information retrieval over the Internet. People's dependence on them increases over time as SEs introduce new and sophisticated technologies. The developments in the Artificial Intelligence (AI) will transform the current search engines Artificial Intelligence Enabled Search Engines (AIESE). Search engines already play a critical role in classifying, sorting and delivering the information over the Internet. However, as Internet's mainstream role becomes more apparent and AI technology increases the sophistication of the tools of the SEs, their roles will become much more critical. Since, the future of search engines are examined, the technological singularity concept is analyzed in detail. Second and third order indirect side effects are analyzed. A four-stage evolution-model is suggested.

Source

Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a

0.003091229 = product of:
  0.012364916 = sum of:
    0.012364916 = weight(_text_:information in 478) [ClassicSimilarity], result of:
      0.012364916 = score(doc=478,freq=6.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.20156369 = fieldWeight in 478, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=478)
  0.25 = coord(1/4)

Abstract

"Web Search Engine Research", edited by Dirk Lewandowski, provides an understanding of Web search engines from the unique perspective of Library and Information Science. The book explores a range of topics including retrieval effectiveness, user satisfaction, the evaluation of search interfaces, the impact of search on society, reliability of search results, query log analysis, user guidance in the search process, and the influence of search engine optimization (SEO) on results quality. While research in computer science has mainly focused on technical aspects of search engines, LIS research is centred on users' behaviour when using search engines and how this interaction can be evaluated. LIS research provides a unique perspective in intermediating between the technical aspects, user aspects and their impact on their role in knowledge acquisition. This book is directly relevant to researchers and practitioners in library and information science, computer science, including Web researchers.

Series

Library and information science; vol. 4