Search (88 results, page 2 of 5)

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Abstract

In diesem Kapitel wird ein Überblick über bestehende semantische Suchmaschinen gegeben. Insgesamt werden 95 solcher Suchdienste identifiziert und im Rahmen einer Inhaltsanalyse verglichen. Es kann festgestellt werden, dass die Semantische Suche sich wesentlich von den im Rahmen des Semantic Web propagierten Technologien unterscheidet und Semantik in den betrachteten Suchmaschinen weiter zu fassen ist. Die betrachteten Suchmaschinen werden in ein Stufenmodell, welches nach dem Grad der Semantik unterscheidet, eingeordnet. Das Kapitel schließt mit 8 Thesen zum aktuellen Stand der semantischen Suche.

Source

Handbuch Internet-Suchmaschinen, 2: Neue Entwicklungen in der Web-Suche. Hrsg.: D. Lewandowski

Theme

Semantic Web

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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.

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Abstract

In this article, we propose new word sense disambiguation strategies for resolving the senses of polysemous query terms issued to Web search engines, and we explore the application of those strategies when used in a query expansion framework. The novelty of our approach lies in the exploitation of the Web page PageRank values as indicators of the significance the different senses of a term carry when employed in search queries. We also aim at scalable query sense resolution techniques that can be applied without loss of efficiency to large data sets such as those on the Web. Our experimental findings validate that the proposed techniques perform more accurately than do the traditional disambiguation strategies and improve the quality of the search results, when involved in query expansion.

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Abstract

Searches conducted on web search engines reflect the interests of users and society. Google Trends, which provides information about the queries searched by users of the Google web search engine, is a rich data source from which a wealth of information can be mined. We investigated the possibility of using web search volume data from Google Trends to predict academic fame. As queries are language-dependent, we studied universities from two countries with different languages, the United States and Spain. We found a significant correlation between the search volume of a university name and the university's academic reputation or fame. We also examined the effect of some Google Trends features, namely, limiting the search to a specific country or topic category on the search volume data. Finally, we examined the effect of university sizes on the correlations found to gain a deeper understanding of the nature of the relationships.

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Abstract

Folksonomies ermöglichen den Nutzern in Kollaborativen Informationsdiensten den Zugang zu verschiedenartigen Informationsressourcen. In welchen Fällen beide Bestandteile des Web 2.0 am besten für das Information Retrieval geeignet sind und wo sie die Websuche ggf. ersetzen können, wird in diesem Beitrag diskutiert. Dazu erfolgt eine detaillierte Betrachtung der Reichweite von Social-Bookmarking-Systemen und Sharing-Systemen sowie der Retrievaleffektivität von Folksonomies innerhalb von Kollaborativen Informationsdiensten.

Source

Handbuch Internet-Suchmaschinen, 2: Neue Entwicklungen in der Web-Suche. Hrsg.: D. Lewandowski

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Abstract

Der zweite Band des bekannten Handbuchs widmet sich den aktuellen Entwicklungen in der Web-Suche. Neue Such-Ansätze wie die Soziale und die Semantische Suche werden ebenso behandelt wir Spezialsuchen, die Evaluierung und nutzergerechte Gestaltung von Suchdiensten sowie das Monitoring von Webquellen. In 12 Kapiteln geben namhafte Wissenschaftler und Praktiker aus dem deutschsprachigen Raum den Überblick über den State of the Art, zeigen Trends auf und geben Handlungsempfehlungen für alle, die sich als Forscher, Entwickler und Nutzer sich mit dem Thema Suche beschäftigen. Dieser zweite Band erweitert das Themenspektrum des Handbuchs Internet-Suchmaschinen wesentlich und stellt damit eine wertvolle Ergänzung dar.

Content

Titel der einzelnen Kapitel: Social Search Folksonomies und Kollaborative Informationsdienste: Eine Alternative zur Websuche? Query Understanding Semantic Search Suche in Multimediaarchiven und Kultureinrichtungen Wissenschaftliche Suchmaschinen Journalistische Recherche im Internet Evaluierung von Suchmaschinen Usability und User Experience in Suchmaschinen Search Engine Bias Web Monitoring Tools für das Monitoring

RSWK

Internet / Suchmaschine / Online-Recherche / Soziale Software / Evaluation / Web Site / Analyse

Subject

Internet / Suchmaschine / Online-Recherche / Soziale Software / Evaluation / Web Site / Analyse

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Abstract

Across the world, millions of users interact with search engines every day to satisfy their information needs. As the Web grows bigger over time, such information needs, manifested through user search queries, also become more complex. However, there has been no systematic study that quantifies the structural complexity of Web search queries. In this research, we make an attempt towards understanding and characterizing the syntactic complexity of search queries using a multi-pronged approach. We use traditional statistical language modeling techniques to quantify and compare the perplexity of queries with natural language (NL). We then use complex network analysis for a comparative analysis of the topological properties of queries issued by real Web users and those generated by statistical models. Finally, we conduct experiments to study whether search engine users are able to identify real queries, when presented along with model-generated ones. The three complementary studies show that the syntactic structure of Web queries is more complex than what n-grams can capture, but simpler than NL. Queries, thus, seem to represent an intermediate stage between syntactic and non-syntactic communication.

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Abstract

This site provides a collection of schemas, i.e., html tags, that webmasters can use to markup their pages in ways recognized by major search providers. Search engines including Bing, Google and Yahoo! rely on this markup to improve the display of search results, making it easier for people to find the right web pages. Many sites are generated from structured data, which is often stored in databases. When this data is formatted into HTML, it becomes very difficult to recover the original structured data. Many applications, especially search engines, can benefit greatly from direct access to this structured data. On-page markup enables search engines to understand the information on web pages and provide richer search results in order to make it easier for users to find relevant information on the web. Markup can also enable new tools and applications that make use of the structure. A shared markup vocabulary makes easier for webmasters to decide on a markup schema and get the maximum benefit for their efforts. So, in the spirit of sitemaps.org, Bing, Google and Yahoo! have come together to provide a shared collection of schemas that webmasters can use.

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Abstract

In this chapter, the authors describe the key indexing components of today's web search engines. As the World Wide Web has grown, the systems and methods for indexing have changed significantly. The authors present the data structures used, the features extracted, the infrastructure needed, and the options available for designing a brand new search engine. Techniques are highlighted that improve relevance of results, discuss trade-offs to best utilize machine resources, and cover distributed processing concepts in this context. In particular, the authors delve into the topics of indexing phrases instead of terms, storage in memory vs. on disk, and data partitioning. Some thoughts on information organization for the newly emerging data-forms conclude the chapter.

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Abstract

In diesem Kapitel werden Vorschläge für neue Suchparadigmen nach multimedialen Inhalten in Archiven von Kultureinrichtungen vorgestellt. Um die Notwendigkeit für eine Integration dieser neuen Technologien zu zeigen, werden zunächst Einschränkungen der klassischen katalogbasierten Bibliothekssuche im Zeitalter von immer weiter wachsenden Multimediasammlungen beschrieben. Im Anschluss werden die Vor- und Nachteile zweier Suchparadigmen dargestellt, mit deren Hilfe in Zukunft für Wissenschaftler und Kulturschaffende die Suche nach multimedialen Inhalten erleichtert werden könnte. Zunächst werden die Perspektiven einer semantischen Suche auf Basis von Semantic-Web-Technologien in Bibliotheken beschrieben. Im Anschluss werden Suchmöglichkeiten für Multimediainhalte auf Basis von automatischer inhaltsbasierter Medienanalyse gezeigt. Das Kapitel endet mit einem Ausblick auf eine mögliche Vereinigung der beiden neuen Ansätze mit katalogbasierter Bibliothekssuche.

Source

Handbuch Internet-Suchmaschinen, 2: Neue Entwicklungen in der Web-Suche. Hrsg.: D. Lewandowski

Theme

Semantic Web

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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.

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Abstract

Wenn Google-Nutzer den Standortverlauf ausschalten, speichert das Unternehmen trotzdem Bewegungsdaten. Betroffen sind mehr als zwei Milliarden Menschen, die Android-Smartphones oder iPhones mit Google-Diensten verwenden. Wer das Tracking verhindern will, muss die "Web- und App-Aktivitäten" komplett deaktivieren.

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Abstract

Der Suchmaschinenmarkt wird seit vielen Jahren von nur einer einzigen Suchmaschine, Google, dominiert. Es wurde mittlerweile erkannt, dass diese Situation nicht wünschenswert ist. Wir sprechen nun über mögliche Lösungen. Der Artikel diskutiert unterschiedliche Lösungsansätze und fokussiert dabei auf die Idee einen Offenen Web-Index (OWI), der als öffentliche Infrastruktur verfügbar gemacht werden soll. Die Grundidee ist die Trennung von Datenbestand (Index) und darauf aufsetzenden Diensten, welche in großer Zahl in privater Initiative betrieben werden können. Es geht also darum, die Basis für Vielfalt zu schaffen.

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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.

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Date

17.12.2013 11:02:22

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Date

19. 2.2019 17:22:00

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Abstract

Mit Schema.org und dem semantischen Web sollen Suchmaschinen verstehen lernen

Content

"Wörter haben oft mehrere Bedeutungen. Einige kennen den "Kanal" als künstliche Wasserstraße, andere vom Fernsehen. Die Waage kann zum Erfassen des Gewichts nützlich sein oder zur Orientierung auf der Horoskopseite. Casablanca ist eine Stadt und ein Film zugleich. Wo Menschen mit der Zeit Bedeutungen unterscheiden und verarbeiten lernen, können dies Suchmaschinen von selbst nicht. Stets listen sie dumpf hintereinander weg alles auf, was sie zu einem Thema finden. Damit das nicht so bleibt, haben sich nun Google, Yahoo und die zu Microsoft gehörende Suchmaschine Bing zusammengetan, um der Suche im Netz mehr Verständnis zu verpassen. Man spricht dabei auch von einer "semantischen Suche". Das Ergebnis heißt Schema.org. Wer die Webseite einmal besucht, sich ein wenig in die Unterstrukturen hereinklickt und weder Vorkenntnisse im Programmieren noch im Bereich des semantischen Webs hat, wird sich überfordert und gelangweilt wieder abwenden. Doch was hier entstehen könnte, hat das Zeug dazu, Teile des Netzes und speziell die Funktionen von Suchmaschinen mittel- oder langfristig zu verändern. "Große Player sind dabei, sich auf Standards zu einigen", sagt Daniel Bahls, Spezialist für Semantische Technologien beim ZBW Leibniz-Informationszentrum Wirtschaft in Hamburg. "Die semantischen Technologien stehen schon seit Jahren im Raum und wurden bisher nur im kleineren Kontext verwendet." Denn Schema.org lädt Entwickler, Forscher, die Semantic-Web-Community und am Ende auch alle Betreiber von Websites dazu ein, an der Umgestaltung der Suche im Netz mitzuwirken. Inhalte von Websites sollen mit einem speziellen, aber einheitlichen Vokabular für die Crawler - die Analyseprogramme der Suchmaschinen - gekennzeichnet und aufbereitet werden.
Indem Schlagworte, sogenannte Tags, in den für Normal-User nicht sichtbaren Teil des Codes von Websites eingebettet werden, sind Suchmachinen nicht mehr so sehr auf die Analyse der natürlichen Sprache angewiesen, um Texte inhaltlich zu erfassen. Im Blog ZBW Mediatalk wird dies als "Semantic Web light" bezeichnet - ein semantisches Web auf niedrigster Ebene. Aber selbst das werde "schon viel bewirken", meint Bahls. "Das semantische Web wird sich über die nächsten Jahrzehnte evolutionär weiterentwickeln." Einen "Abschluss" werde es nie geben, "da eine einheitliche Formalisierung von Begrifflichkeiten auf feiner Stufe kaum möglich ist". Die Ergebnisse aus Schema.org würden "zeitnah" in die Suchmaschine integriert, "denn einen Zeitplan" gebe es nicht, so Stefan Keuchel, Pressesprecher von Google Deutschland. Bis das so weit ist, hilft der Verweis von Daniel Bahns auf die bereits existierende semantische Suchmaschine Sig.ma. Geschwindigkeit und Menge der Ergebnisse nach einer Suchanfrage spielen hier keine Rolle. Sig.ma sammelt seine Informationen allein im Bereich des semantischen Webs und listet nach einer Anfrage alles Bekannte strukturiert auf.

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Abstract

Search engines are essential tools for web users today. They rely on a large number of features to compute the rank of search results for each given query. The estimated reputation of pages is among the effective features available for search engine designers, probably being adopted by most current commercial search engines. Page reputation is estimated by analyzing the linkage relationships between pages. This information is used by link analysis algorithms as a query-independent feature, to be taken into account when computing the rank of the results. Unfortunately, several types of links found on the web may damage the estimated page reputation and thus cause a negative effect on the quality of search results. This work studies alternatives to reduce the negative impact of such noisy links. More specifically, the authors propose and evaluate new methods that deal with noisy links, considering scenarios where the reputation of pages is computed using the PageRank algorithm. They show, through experiments with real web content, that their methods achieve significant improvements when compared to previous solutions proposed in the literature.

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Abstract

Search Engines have become an integral part of our day to day life. Our reliance on search engines increases with every passing day. With the amount of data available on Internet increasing exponentially, it becomes important to develop new methods and tools that help to return results relevant to the queries and reduce the time spent on searching. The results should be diverse but at the same time should return results focused on the queries asked. Relation Based Page Rank [4] algorithms are considered to be the next frontier in improvement of Semantic Web Search. The probability of finding relevance in the search results as posited by the user while entering the query is used to measure the relevance. However, its application is limited by the complexity of determining relation between the terms and assigning explicit meaning to each term. Trust Rank is one of the most widely used ranking algorithms for semantic web search. Few other ranking algorithms like HITS algorithm, PageRank algorithm are also used for Semantic Web Searching. In this paper, we will provide a comparison of few ranking approaches.

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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.