Search (23 results, page 1 of 2)

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Abstract

Purpose - The purpose of this paper is to compare five major web search engines (Google, Yahoo, MSN, Ask.com, and Seekport) for their retrieval effectiveness, taking into account not only the results, but also the results descriptions. Design/methodology/approach - The study uses real-life queries. Results are made anonymous and are randomized. Results are judged by the persons posing the original queries. Findings - The two major search engines, Google and Yahoo, perform best, and there are no significant differences between them. Google delivers significantly more relevant result descriptions than any other search engine. This could be one reason for users perceiving this engine as superior. Research limitations/implications - The study is based on a user model where the user takes into account a certain amount of results rather systematically. This may not be the case in real life. Practical implications - The paper implies that search engines should focus on relevant descriptions. Searchers are advised to use other search engines in addition to Google. Originality/value - This is the first major study comparing results and descriptions systematically and proposes new retrieval measures to take into account results descriptions.

Source

Journal of documentation. 64(2008) no.6, S.915-937

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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

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Abstract

Purpose This paper demonstrates how to apply traditional information retrieval evaluation methods based on standards from the Text REtrieval Conference (TREC) and web search evaluation to all types of modern library information systems including online public access catalogs, discovery systems, and digital libraries that provide web search features to gather information from heterogeneous sources. Design/methodology/approach We apply conventional procedures from information retrieval evaluation to the library information system context considering the specific characteristics of modern library materials. Findings We introduce a framework consisting of five parts: (1) search queries, (2) search results, (3) assessors, (4) testing, and (5) data analysis. We show how to deal with comparability problems resulting from diverse document types, e.g., electronic articles vs. printed monographs and what issues need to be considered for retrieval tests in the library context. Practical implications The framework can be used as a guideline for conducting retrieval effectiveness studies in the library context. Originality/value Although a considerable amount of research has been done on information retrieval evaluation, and standards for conducting retrieval effectiveness studies do exist, to our knowledge this is the first attempt to provide a systematic framework for evaluating the retrieval effectiveness of twenty-first-century library information systems. We demonstrate which issues must be considered and what decisions must be made by researchers prior to a retrieval test.

Source

Journal of documentation. 73(2017) no.3, S.509-527

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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

Source

Aslib journal of information management. 71(2019) no.3, S.310-324

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Abstract

This chapter presents a theoretical framework for evaluating next generation search engines. The author focuses on search engines whose results presentation is enriched with additional information and does not merely present the usual list of "10 blue links," that is, of ten links to results, accompanied by a short description. While Web search is used as an example here, the framework can easily be applied to search engines in any other area. The framework not only addresses the results presentation, but also takes into account an extension of the general design of retrieval effectiveness tests. The chapter examines the ways in which this design might influence the results of such studies and how a reliable test is best designed.

Source

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

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Abstract

Search engine retrieval effectiveness studies are usually small scale, using only limited query samples. Furthermore, queries are selected by the researchers. We address these issues by taking a random representative sample of 1,000 informational and 1,000 navigational queries from a major German search engine and comparing Google's and Bing's results based on this sample. Jurors were found through crowdsourcing, and data were collected using specialized software, the Relevance Assessment Tool (RAT). We found that although Google outperforms Bing in both query types, the difference in the performance for informational queries was rather low. However, for navigational queries, Google found the correct answer in 95.3% of cases, whereas Bing only found the correct answer 76.6% of the time. We conclude that search engine performance on navigational queries is of great importance, because users in this case can clearly identify queries that have returned correct results. So, performance on this query type may contribute to explaining user satisfaction with search engines.

Source

Journal of the Association for Information Science and Technology. 66(2015) no.9, S.1763-1775

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Abstract

Purpose - The purpose of this paper is to test major web search engines on their performance on navigational queries, i.e. searches for homepages. Design/methodology/approach - In total, 100 user queries are posed to six search engines (Google, Yahoo!, MSN, Ask, Seekport, and Exalead). Users described the desired pages, and the results position of these was recorded. Measured success and mean reciprocal rank are calculated. Findings - The performance of the major search engines Google, Yahoo!, and MSN was found to be the best, with around 90 per cent of queries answered correctly. Ask and Exalead performed worse but received good scores as well. Research limitations/implications - All queries were in German, and the German-language interfaces of the search engines were used. Therefore, the results are only valid for German queries. Practical implications - When designing a search engine to compete with the major search engines, care should be taken on the performance on navigational queries. Users can be influenced easily in their quality ratings of search engines based on this performance. Originality/value - This study systematically compares the major search engines on navigational queries and compares the findings with studies on the retrieval effectiveness of the engines on informational queries.

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Abstract

This opinion piece takes Google's response to the so-called COVID-19 infodemic, as a starting point to argue for the need to consider societal relevance as a complement to other types of relevance. The authors maintain that if information science wants to be a discipline at the forefront of research on relevance, search engines, and their use, then the information science research community needs to address itself to the challenges and conditions that commercial search engines create in. The article concludes with a tentative list of related research topics.

Source

Journal of the Association for Information Science and Technology. 73(2022) no.5, S.637-642

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Abstract

This representative study of German search engine users (N?=?1,000) focuses on the ability of users to distinguish between organic results and advertisements on Google results pages. We combine questions about Google's business with task-based studies in which users were asked to distinguish between ads and organic results in screenshots of results pages. We find that only a small percentage of users can reliably distinguish between ads and organic results, and that user knowledge of Google's business model is very limited. We conclude that ads are insufficiently labelled as such, and that many users may click on ads assuming that they are selecting organic results.

Source

Journal of the Association for Information Science and Technology. 69(2018) no.3, S.420-437

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Abstract

Purpose: To provide a critical review of Bergman's 2001 study on the deep web. In addition, we bring a new concept into the discussion, the academic invisible web (AIW). We define the academic invisible web as consisting of all databases and collections relevant to academia but not searchable by the general-purpose internet search engines. Indexing this part of the invisible web is central to scientific search engines. We provide an overview of approaches followed thus far. Design/methodology/approach: Discussion of measures and calculations, estimation based on informetric laws. Literature review on approaches for uncovering information from the invisible web. Findings: Bergman's size estimate of the invisible web is highly questionable. We demonstrate some major errors in the conceptual design of the Bergman paper. A new (raw) size estimate is given. Research limitations/implications: The precision of our estimate is limited due to a small sample size and lack of reliable data. Practical implications: We can show that no single library alone will be able to index the academic invisible web. We suggest collaboration to accomplish this task. Originality/value: Provides library managers and those interested in developing academic search engines with data on the size and attributes of the academic invisible web.

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Abstract

In an online experiment using a representative sample of the German online population (n = 1.000), we compare users' selection behaviour on two versions of the same Google search engine results page (SERP), one showing advertisements and organic results, the other showing organic results only. Selection behaviour is analyzed in relation to users' knowledge on Google's business model, on SERP design, and on these users' actual performance in marking advertisements on SERPs correctly. We find that users who were not able to mark ads correctly selected ads significantly more often. This leads to the conclusion that ads need to be labeled more clearly, and that there is a need for more information literacy in search engine users.

Source

Everything changes, everything stays the same? - Understanding information spaces : Proceedings of the 15th International Symposium of Information Science (ISI 2017), Berlin/Germany, 13th - 15th March 2017. Eds.: M. Gäde, V. Trkulja u. V. Petras

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Abstract

WebInformationRetrieval hat sich als gesonderter Forschungsbereich herausgebildet. Neben den im klassischen Information Retrieval behandelten Fragen ergeben sich durch die Eigenheiten des Web neue und zusätzliche Forschungsfragen. Die Unterschiede zwischen Information Retrieval und Web Information Retrieval werden diskutiert. Derzweite Teil des Aufsatzes gibt einen Überblick über die Forschungsliteratur der letzten zwei Jahre. Dieser Aufsatz gibt einen Überblick über den Stand der Forschung im Bereich Web Information Retrieval. Im ersten Teil werden die besonderen Probleme, die sich in diesem Bereich ergeben, anhand einer Gegenüberstellung mit dem "klassischen" Information Retrieval erläutert. Der weitere Text diskutiert die wichtigste in den letzten Jahren erschienene Literatur zum Thema, wobei ein Schwerpunkt auf die - so vorhanden-deutschsprachige Literatur gelegt wird. Der Schwerpunkt liegt auf Literatur aus den Jahren 2003 und 2004. Zum einen zeigt sich in dem betrachteten Forschungsfeld eine schnelle Entwicklung, so dass viele ältere Untersuchungen nur noch einen historischen bzw. methodischen Wert haben; andererseits existieren umfassende ältere Reviewartikel (s. v.a. Rasmussen 2003). Schon bei der Durchsicht der Literatur wird allerdings deutlich, dass zu einigen Themenfeldern keine oder nur wenig deutschsprachige Literatur vorhanden ist. Leider ist dies aber nicht nur darauf zurückzuführen, dass die Autoren aus den deutschsprachigen Ländern ihre Ergebnisse in englischer Sprache publizieren. Vielmehr wird deutlich, dass in diesen Ländern nur wenig Forschung im Suchmaschinen-Bereich stattfindet. Insbesondere zu sprachspezifischen Problemen von Web-Suchmaschinen fehlen Untersuchungen. Ein weiteres Problem der Forschung im Suchmaschinen-Bereich liegt in der Tatsache begründet, dass diese zu einem großen Teil innerhalb von Unternehmen stattfindet, welche sich scheuen, die Ergebnisse in großem Umfang zu publizieren, da sie fürchten, die Konkurrenz könnte von solchen Veröffentlichungen profitieren. So finden sich etwa auch Vergleichszahlen über einzelne Suchmaschinen oft nur innerhalb von Vorträgen oder Präsentationen von Firmenvertretern (z.B. Singhal 2004; Dean 2004). Das Hauptaugenmerk dieses Artikels liegt auf der Frage, inwieweit Suchmaschinen in der Lage sind, die im Web vorhanden Inhalte zu indexieren, mit welchen Methoden sie dies tun und ob bzw. wie sie ihre Ziele erreichen. Ausgenommen bleiben damit explizit Fragen der Effizienz bei der Erschließung des Web und der Skalierbarkeit von Suchmaschinen. Anders formuliert: Diese Übersicht orientiert sich an klassisch informationswissenschaftlichen Fragen und spart die eher im Bereich der Informatik diskutierten Fragen weitgehend aus.
Eine regelmäßige Übersicht neuer US-Patente und US-Patentanmeldungen im Bereich Information Retrieval bietet die News-Seite Resourceshelf (www.resourceshelf.com).

Content

Mit einer Tabelle, die eine Gegenüberstellung des WebRetrieval zum 'klassischen' Information Retrieval anbietet

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Source

Re:inventing information science in the networked society: Proceedings of the 14th International Symposium on Information Science, Zadar/Croatia, 19th-21st May 2015. Eds.: F. Pehar, C. Schloegl u. C. Wolff

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Abstract

Users of web search engines are known to mostly focus on the top ranked results of the search engine result page. While many studies support this well known information seeking pattern only few studies concentrate on the question what users are missing by neglecting lower ranked results. To learn more about the relevance distributions in the so-called long tail we conducted a relevance assessment study with the Million Short long-tail web search engine. While we see a clear difference in the content between the head and the tail of the search engine result list we see no statistical significant differences in the binary relevance judgments and weak significant differences when using graded relevance. The tail contains different but still valuable results. We argue that the long tail can be a rich source for the diversification of web search engine result lists but it needs more evaluation to clearly describe the differences.

Footnote

To appear in Experimental IR Meets Multilinguality, Multimodality, and Interaction. 7th International Conference of the CLEF Association, CLEF 2016, \'Evora, Portugal, September 5-8, 2016.

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Abstract

The purpose of this article is to test the reliability of query intents derived from queries, either by the user who entered the query or by another juror. We report the findings of three studies. First, we conducted a large-scale classification study (~50,000 queries) using a crowdsourcing approach. Next, we used clickthrough data from a search engine log and validated the judgments given by the jurors from the crowdsourcing study. Finally, we conducted an online survey on a commercial search engine's portal. Because we used the same queries for all three studies, we also were able to compare the results and the effectiveness of the different approaches. We found that neither the crowdsourcing approach, using jurors who classified queries originating from other users, nor the questionnaire approach, using searchers who were asked about their own query that they just entered into a Web search engine, led to satisfying results. This leads us to conclude that there was little understanding of the classification tasks, even though both groups of jurors were given detailed instructions. Although we used manual classification, our research also has important implications for automatic classification. We must question the success of approaches using automatic classification and comparing its performance to a baseline from human jurors.

Source

Journal of the American Society for Information Science and Technology. 63(2012) no.9, S.1773-1788

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Abstract

In diesem Artikel präsentieren wir die Durchführung und die Ergebnisse eines interdisziplinären Forschungsprojekts zum Thema automatisierte Lebensmittelkontrolle im Web. Es wurden Kompetenzen aus den Disziplinen Lebensmittelwissenschaft, Rechtswissenschaft, Informationswissenschaft und Informatik dazu genutzt, ein detailliertes Konzept und einen Software-Prototypen zu entwickeln, um das Internet nach Produktangeboten zu durchsuchen, die gegen das Lebensmittelrecht verstoßen. Dabei wird deutlich, wie ein solcher Anwendungsfall von den Methoden der Information-Retrieval-Evaluierung profitiert, und wie sich mit relativ geringem Aufwand eine flexible Software programmieren lässt, die auch für eine Vielzahl anderer Fragestellungen einsetzbar ist. Die Ergebnisse des Projekts zeigen, wie komplexe Arbeitsprozesse einer Behörde mit Hilfe der Methoden von Retrieval-Tests und gängigen Verfahren aus dem maschinellen Lernen effektiv und effizient unterstützt werden können.

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Content

"Web-Suchmaschinen gibt es seit mittlerweile etwa zehn Jahren. Mit ihnen ist die Informationsrecherche, welche lange Zeit eine Sache für (uns) Experten war, bei Otto Normalverbraucher angekommen. Suchmaschinen haben sich an die Bedürfnisse dieser Nutzerschaft angepasst, was von Expertenseite zu vielerlei Klagen über ihre "Primitivität` geführt hat. Als Chance kann hier aber die Erkenntnis gesehen werden, dass die Nutzer einfache Interfaces und ein gutes Ranking der Suchergebnisse benötigen - auch in fachlichen Anwendungen. Der Durchbruch des Information Retrieval und seiner Bedeutung zeigt sich aber nicht nur durch die breite Nutzerschaft. Das Kernstück von erfolgreichen Suchmaschinen-Unternehmen wie Google und Yahoo! bilden Information-Retrieval-Verfahren - von besonderem Interesse sind dabei stets die von den Firmen geheim gehaltenen Ranking-Algorithmen. Die Forschung im IR-Bereich findet inzwischen zahlreiche namhafte Sponsoren - bei der letzten Jahrestagung der Special Interest Group an Information Retrieval (SIGIR) waren unter anderem Microsoft, IBM und Google mit im Boot. Suchmaschinen-Forschung findet in Deutschland in zahlreichen Hochschulen und Unternehmen statt, dabei ist sie allerdings verstreut und wenig koordiniert. Die zahlreichen auf das Call for Papers für dieses Themenheft der IWP eingegangenen Beiträge zeigen erfreulicherweise ein großes Potenzial für die informationswissenschaftliche Forschung in diesem Bereich. Der erste Beitrag befasst sich mit den Eigenheiten des Web und arbeitet die Unterschiede zwischen klassischem Information Retrieval und Web Information Retrieval heraus. Damit werden die Grundlagen für die Diskussion über Suchmaschinen gelegt. Der zweite Teil des Beitrags gibt einen Überblick der aktuellen Forschungsliteratur mit informationswissenschaftlichem Schwerpunkt und hat zum Ziel, weitere Forschung anzuregen. Thomas Mandl beschreibt in der Darstellung seines AOUAINT-Projekts die unterschiedlichen Ansätze, (Web-)Dokumente nach ihrer Oualität zu beurteilen. Solche Verfahren werden bereits von den bisher bestehenden Suchmaschinen eingesetzt; man denke etwa an das Kernstück von Google, das so genannte PageRank-Verfahren. Allerdings beschränken sich die bisherigen Verfahren nur auf einzelne Aspekte von Qualität. AOUAINT erweitert die Qualitätsbewertung um weitere Faktoren und kann so das Retrieval verbessern.

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Date

29. 9.2002 18:49:22

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Date

28.11.2004 13:11:22

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Date

18. 9.2018 18:22:18