Search (52 results, page 1 of 3)

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Abstract

Mittels professioneller Suchtechniken lässt sich die Recherche im WWW wesentlich schneller und zuverlässiger gestalten. Selbst eine universelle Suchmaschine, wie Google, erlaubt mittels spezieller Suchfunktionen qualifizierte Recherchen über den Freitext hinaus. Von besonderer Bedeutung sind hier die Selektion nach Domainnamen und die Einbeziehung der URL. In Kombination mit dem Verständnis der Ranking-Techniken, z. B. der Prioritäten der im HTML-Quelltext verankerten inhaltlichen Meta-Informationen (Title-Tag, Meta-Tags Keywords, Description) gegenüber dem Standardverfahren (Google PageRank) lässt sich die Recherche auf professionelle Informationsangebote ausrichten. Eine weitere Qualifizierung lässt sich durch geschickten Einsatz der Phrasensuche erreichen. In jedem Fall setzt eine erfolgreiche Recherche, auch bei scheinbar einfachen Benennungen und Zusammenhängen eine intellektuelle Recherchevorbereitung voraus - bei der zunächst das Thema strukturiert und dann eine Vorstellung über die möglichen Zusammenhänge entwickelt wird, in der die Zielinformation im WWW zu finden sein könnte. Soweit die Möglichkeit besteht, sollte bei anspruchsvollen technischwissenschaftlichen Themen der WWW-Suche eine Recherche in professionellen Literaturdatenbanken vorausgehen. Diese haben den Vorzug, dass eine transparente und sorgfältig selektierte Quellenbasis vorliegt und durch Suche mit kontrolliertem Wortschatz die typischen Unschärfen einer Freitextrecherche (unerwünschte Zusammenhänge, Synonyme, Homonyme, Schreibvarianten usw.) umgangen werden können. Die FIZ-Technik-Inform GmbH bietet kostenpflichtige Weiterbildungsveranstaltungen auch zur WWW-Recherche an.

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RSWK

Internet / Suchmaschine (21)

Subject

Internet / Suchmaschine (21)

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Date

22. 5.2005 19:52:29

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Content

"Ein häufiger Fehler ist auch, bei Google nach Büchern zu suchen", sagt Grau. Die Suchmaschine enthält keine Bibliotheksdaten. Außerdem sollten Studenten darauf achten, ob sie nach einem Zeitschriften-Artikel oder einer Monografie suchen. Benötigt man einen Aufsatz, muss man nach dem Titel der Zeitschrift und nicht nach dem Titel des Artikels suchen. Wichtig ist auch, den Suchschlüssel zu beachten. Wer nach dem Autor Johann Wolfgang von Goethe sucht, aber das Wort in der Titelsuche eingibt, bekommt andere Treffermengen. Studenten sollten die Suche auch nicht zu sehr eingrenzen. "Dann findet man nichts", warnt Grau. Andererseits darf man auch nicht zu allgemein suchen. Wer nach einem Buch zur deutschen Geschichte sucht, bekommt bei der Eingabe von "deutsche Geschichte" Tausende Treffer. "Da muss man den richtigen Suchschlüssel auswählen", erklärt Grau. Wer im Feld "Titelanfänge" etwa "deutsche Geschichte" eingibt, finde alle Titel mit diesen Wörtern in genau dieser Reihenfolge. Er lande also nicht beim Buch "Deutsche Naturlyrik: ihre Geschichte in Einzelanalysen". Das ist bei weit gefassten Begriffen sehr wichtig und hilfreich."

Date

3. 5.1997 8:44:22

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Abstract

It is a frequently seen scenario that when people are not familiar with their search topics, they use a simple keyword search, which leads to a large amount of search results in multiple pages. This makes it difficult for users to pick relevant documents, especially given that they are not knowledgeable of the topics. To explore how systems can better help users find relevant documents from search results, the current research analyzed document selection behaviors of users with different levels of domain knowledge (DK). Data were collected in a laboratory study with 35 participants each searching on four tasks in the genomics domain. The results show that users with high and low DK levels selected different sets of documents to view; those high in DK read more documents and gave higher relevance ratings for the viewed documents than those low in DK did. Users with low DK tended to select documents ranking toward the top of the search result lists, and those with high in DK tended to also select documents ranking down the search result lists. The findings help design search systems that can personalize search results to users with different levels of DK.

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Abstract

Reviews the form and application of information abstraction in an information retrieval interface. Discusses the results of an explanatory study undertaken to develop an understanding of the information search strategy and the decision strategy used, and whether these strategies were related. Describes the design of an experimental interface to evaluate its effects. Discusses an experiment where data were collected on the activities of subjects while they used an interface to solve an alternative ranking problem. Presents an analysis of the data, conclusions and implications of the study

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Abstract

Finding information located somewhere on the WWW is an error-prone and frustrating task. The WebQuey system offers a powerful new method for searching the Web based on connectivity and content. We do this by examining links among the nodes returned in a keyword-based query. We then rank the nodes, giving the highest rank to the most highly connected nodes. By doing so, we are finding 'hot spots' on the Web that contain onformation germane to a user's query. WebQuery not only ranks and filters the results of a Web query, it also extends the result set beyond what the search engine retrieves, by finding 'interesting' sites that are hoghly connected to those sites returned by the original query. Even with WebQuery filtering and ranking query results, the result sets can be enourmous. So, wen need to visualize the returned information. We explore several techniques for visualizing this information - including cone trees, 2D graphs, 3D graphy, lists, and bullseyes - and discuss the criteria for using each of the techniques

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Abstract

Resource description extracted by query-sampling method can be applied to determine which database sources a certain query should be firstly sent to. In this paper, we propose a contextualized query-sampling method to extract the resources which are most relevant to up-to-date context. Practically, the proposed approach is adopted to personal crawler systems (the so-called focused crawlers), which can support the corresponding user's web navigation tasks in real-time. By taking into account the user context (e.g., intentions or interests), the crawler can build the queries to evaluate candidate information sources. As a result, we can discover semantic associations (i) between user context and the sources, and (ii) between all pairs of the sources. These associations are applied to rank the sources, and transform the queries for the other sources. For evaluating the performance of contextualized query sampling on 53 information sources, we compared the ranking lists recommended by the proposed method with user feedbacks (i.e., ideal ranks), and also computed the precision of discovered subsumptions as semantic associations between the sources.

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Abstract

Children represent an increasing group of web users. Some of the key problems that hamper their search experience is their limited vocabulary, their difficulty in using the right keywords, and the inappropriateness of their general-purpose query suggestions. In this work, we propose a method that uses tags from social media to suggest queries related to children's topics. Concretely, we propose a simple yet effective approach to bias a random walk defined on a bipartite graph of web resources and tags through keywords that are more commonly used to describe resources for children. We evaluate our method using a large query log sample of queries submitted by children. We show that our method outperforms by a large margin the query suggestions of modern search engines and state-of-the art query suggestions based on random walks. We improve further the quality of the ranking by combining the score of the random walk with topical and language modeling features to emphasize even more the child-related aspects of the query suggestions.

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Abstract

Subject content analysis of Web query terms is essential to understand Web searching interests. Such analysis includes exploring search topics and observing changes in their frequency distributions with time. To provide a basis for in-depth analysis of users' search interests on a larger scale, this article presents a query categorization approach to automatically classifying Web query terms into broad subject categories. Because a query is short in length and simple in structure, its intended subject(s) of search is difficult to judge. Our approach, therefore, combines the search processes of real-world search engines to obtain highly ranked Web documents based on each unknown query term. These documents are used to extract cooccurring terms and to create a feature set. An effective ranking function has also been developed to find the most appropriate categories. Three search engine logs in Taiwan were collected and tested. They contained over 5 million queries from different periods of time. The achieved performance is quite encouraging compared with that of human categorization. The experimental results demonstrate that the approach is efficient in dealing with large numbers of queries and adaptable to the dynamic Web environment. Through good integration of human and machine efforts, the frequency distributions of subject categories in response to changes in users' search interests can be systematically observed in real time. The approach has also shown potential for use in various information retrieval applications, and provides a basis for further Web searching studies.

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Abstract

Legal researchers, recruitment professionals, healthcare information professionals, and patent analysts all undertake work tasks where search forms a core part of their duties. In these instances, the search task is often complex and time-consuming and requires specialist expertise to identify relevant documents and insights within large domain-specific repositories and collections. Several studies have been made investigating the search practices of professionals such as these, but few have attempted to directly compare their professional practices and so it remains unclear to what extent insights and approaches from one domain can be applied to another. In this paper we describe the results of a survey of a purposive sample of 108 legal researchers, 64 recruitment professionals and 107 healthcare information professionals. Their responses are compared with results from a previous survey of 81 patent analysts. The survey investigated their search practices and preferences, the types of functionality they value, and their requirements for future information retrieval systems. The results reveal that these professions share many fundamental needs and face similar challenges. In particular a continuing preference to formulate queries as Boolean expressions, the need to manage, organise and re-use search strategies and results and an ambivalence toward the use of relevance ranking. The results stress the importance of recall and coverage for the healthcare and patent professionals, while precision and recency were more important to the legal and recruitment professionals. The results also highlight the need to ensure that search systems give confidence to the professional searcher and so trust, explainability and accountability remains a significant challenge when developing such systems. The findings suggest that translational research between the different areas could benefit professionals across domains.

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Abstract

Understanding search engine users' intents has been a popular study in information retrieval, which directly affects the quality of retrieved information. One of the fundamental problems in this field is to find a connection between the entity in a query and the potential intents of the users, the latter of which would further reveal important information for facilitating the users' future actions. In this article, we present a novel research method for mining the actionable intents for search users, by generating a ranked list of the potentially most informative actions based on a massive pool of action samples. We compare different search strategies and their combinations for retrieving the action pool and develop three criteria for measuring the informativeness of the selected action samples, that is, the significance of an action sample within the pool, the representativeness of an action sample for the other candidate samples, and the diverseness of an action sample with respect to the selected actions. Our experiment, based on the Action Mining (AM) query entity data set from the Actionable Knowledge Graph (AKG) task at NTCIR-13, suggests that the proposed approach is effective in generating an informative and early-satisfying ranking of potential actions for search users.

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RSWK

Suchmaschine
Information Retrieval / Datenbanksystem / Suchmaschine (GBV)

Subject

Suchmaschine
Information Retrieval / Datenbanksystem / Suchmaschine (GBV)

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Date

2. 7.2005 12:22:50

Source

Gaining insight from research information (CRIS2002): Proceedings of the 6th International Conference an Current Research Information Systems, University of Kassel, August 29 - 31, 2002. Eds: W. Adamczak u. A. Nase

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Date

22. 5.2005 19:53:09

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Source

Library and information science research. 22(2000) no.4, S.371-382

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Source

Proceedings of the 15th National IASLIC Conference, Annamalainagar, Tamil Nadu, India, 26-29 December 1992. Ed. by. A. Chatterjee et al

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5. 8.2006 13:22:44

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14.12.1995 20:51:29

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29. 1.1996 19:58:10