Search (83 results, page 1 of 5)

0.07726393 = product of:
  0.15452786 = sum of:
    0.15452786 = sum of:
      0.10511739 = weight(_text_:web in 1026) [ClassicSimilarity], result of:
        0.10511739 = score(doc=1026,freq=12.0), product of:
          0.17002425 = queryWeight, product of:
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.052098576 = queryNorm
          0.6182494 = fieldWeight in 1026, product of:
            3.4641016 = tf(freq=12.0), with freq of:
              12.0 = termFreq=12.0
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.0546875 = fieldNorm(doc=1026)
      0.049410466 = weight(_text_:22 in 1026) [ClassicSimilarity], result of:
        0.049410466 = score(doc=1026,freq=2.0), product of:
          0.18244034 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052098576 = queryNorm
          0.2708308 = fieldWeight in 1026, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0546875 = fieldNorm(doc=1026)
  0.5 = coord(1/2)

Abstract

We have created software applications that allow users to both author and use Semantic Web metadata. To create and use a layer of semantic content on top of the existing Web, we have (1) implemented a user interface that expedites the task of attributing metadata to resources on the Web, and (2) augmented a Web browser to leverage this semantic metadata to provide relevant information and tasks to the user. This project provides a framework for annotating and reorganizing existing files, pages, and sites on the Web that is similar to Vannevar Bushrsquos original concepts of trail blazing and associative indexing.

Source

Research and advanced technology for digital libraries : 7th European Conference, proceedings / ECDL 2003, Trondheim, Norway, August 17-22, 2003

Theme

Semantic Web

0.061869845 = product of:
  0.12373969 = sum of:
    0.12373969 = sum of:
      0.07432922 = weight(_text_:web in 1319) [ClassicSimilarity], result of:
        0.07432922 = score(doc=1319,freq=6.0), product of:
          0.17002425 = queryWeight, product of:
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.052098576 = queryNorm
          0.43716836 = fieldWeight in 1319, product of:
            2.4494898 = tf(freq=6.0), with freq of:
              6.0 = termFreq=6.0
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.0546875 = fieldNorm(doc=1319)
      0.049410466 = weight(_text_:22 in 1319) [ClassicSimilarity], result of:
        0.049410466 = score(doc=1319,freq=2.0), product of:
          0.18244034 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052098576 = queryNorm
          0.2708308 = fieldWeight in 1319, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0546875 = fieldNorm(doc=1319)
  0.5 = coord(1/2)

Abstract

Keyword based querying has been an immediate and efficient way to specify and retrieve related information that the user inquired. However, conventional document ranking based on an automatic assessment of document relevance to the query may not be the best approach when little information is given. Proposes an idea to integrate 2 existing techniques, query expansion and relevance feedback to achieve a concept-based information search for the Web

Date

1. 8.1996 22:08:06

Footnote

Contribution to a special issue devoted to the Proceedings of the 7th International World Wide Web Conference, held 14-18 April 1998, Brisbane, Australia

0.04879702 = product of:
  0.09759404 = sum of:
    0.09759404 = sum of:
      0.06935949 = weight(_text_:web in 1626) [ClassicSimilarity], result of:
        0.06935949 = score(doc=1626,freq=16.0), product of:
          0.17002425 = queryWeight, product of:
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.052098576 = queryNorm
          0.4079388 = fieldWeight in 1626, product of:
            4.0 = tf(freq=16.0), with freq of:
              16.0 = termFreq=16.0
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.03125 = fieldNorm(doc=1626)
      0.028234553 = weight(_text_:22 in 1626) [ClassicSimilarity], result of:
        0.028234553 = score(doc=1626,freq=2.0), product of:
          0.18244034 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052098576 = queryNorm
          0.15476047 = fieldWeight in 1626, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.03125 = fieldNorm(doc=1626)
  0.5 = coord(1/2)

Abstract

Purpose - The growing volumes of semantic data available in the web result in the need for handling the information overload phenomenon. The potential of this amount of data is enormous but in most cases it is very difficult for users to visualize, explore and use this data, especially for lay-users without experience with Semantic Web technologies. The paper aims to discuss these issues. Design/methodology/approach - The Visual Information-Seeking Mantra "Overview first, zoom and filter, then details-on-demand" proposed by Shneiderman describes how data should be presented in different stages to achieve an effective exploration. The overview is the first user task when dealing with a data set. The objective is that the user is capable of getting an idea about the overall structure of the data set. Different information architecture (IA) components supporting the overview tasks have been developed, so they are automatically generated from semantic data, and evaluated with end-users. Findings - The chosen IA components are well known to web users, as they are present in most web pages: navigation bars, site maps and site indexes. The authors complement them with Treemaps, a visualization technique for displaying hierarchical data. These components have been developed following an iterative User-Centered Design methodology. Evaluations with end-users have shown that they get easily used to them despite the fact that they are generated automatically from structured data, without requiring knowledge about the underlying semantic technologies, and that the different overview components complement each other as they focus on different information search needs. Originality/value - Obtaining semantic data sets overviews cannot be easily done with the current semantic web browsers. Overviews become difficult to achieve with large heterogeneous data sets, which is typical in the Semantic Web, because traditional IA techniques do not easily scale to large data sets. There is little or no support to obtain overview information quickly and easily at the beginning of the exploration of a new data set. This can be a serious limitation when exploring a data set for the first time, especially for lay-users. The proposal is to reuse and adapt existing IA components to provide this overview to users and show that they can be generated automatically from the thesaurus and ontologies that structure semantic data while providing a comparable user experience to traditional web sites.

Date

20. 1.2015 18:30:22

Theme

Semantic Web

0.039321437 = product of:
  0.078642875 = sum of:
    0.078642875 = sum of:
      0.04334968 = weight(_text_:web in 1343) [ClassicSimilarity], result of:
        0.04334968 = score(doc=1343,freq=4.0), product of:
          0.17002425 = queryWeight, product of:
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.052098576 = queryNorm
          0.25496176 = fieldWeight in 1343, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.0390625 = fieldNorm(doc=1343)
      0.03529319 = weight(_text_:22 in 1343) [ClassicSimilarity], result of:
        0.03529319 = score(doc=1343,freq=2.0), product of:
          0.18244034 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052098576 = queryNorm
          0.19345059 = fieldWeight in 1343, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0390625 = fieldNorm(doc=1343)
  0.5 = coord(1/2)

Abstract

A substantial fraction of web search queries contain references to entities, such as persons, organizations, and locations. Recently, methods that exploit named entities have been shown to be more effective for query expansion than traditional pseudorelevance feedback methods. In this article, we introduce a supervised learning approach that exploits named entities for query expansion using Wikipedia as a repository of high-quality feedback documents. In contrast with existing entity-oriented pseudorelevance feedback approaches, we tackle query expansion as a learning-to-rank problem. As a result, not only do we select effective expansion terms but we also weigh these terms according to their predicted effectiveness. To this end, we exploit the rich structure of Wikipedia articles to devise discriminative term features, including each candidate term's proximity to the original query terms, as well as its frequency across multiple article fields and in category and infobox descriptors. Experiments on three Text REtrieval Conference web test collections attest the effectiveness of our approach, with gains of up to 23.32% in terms of mean average precision, 19.49% in terms of precision at 10, and 7.86% in terms of normalized discounted cumulative gain compared with a state-of-the-art approach for entity-oriented query expansion.

Date

22. 8.2014 17:07:50

0.03297302 = product of:
  0.06594604 = sum of:
    0.06594604 = sum of:
      0.030652853 = weight(_text_:web in 56) [ClassicSimilarity], result of:
        0.030652853 = score(doc=56,freq=2.0), product of:
          0.17002425 = queryWeight, product of:
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.052098576 = queryNorm
          0.18028519 = fieldWeight in 56, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.0390625 = fieldNorm(doc=56)
      0.03529319 = weight(_text_:22 in 56) [ClassicSimilarity], result of:
        0.03529319 = score(doc=56,freq=2.0), product of:
          0.18244034 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052098576 = queryNorm
          0.19345059 = fieldWeight in 56, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0390625 = fieldNorm(doc=56)
  0.5 = coord(1/2)

Abstract

The study reported here investigated the query expansion behavior of end-users interacting with a thesaurus-enhanced search system on the Web. Two groups, namely academic staff and postgraduate students, were recruited into this study. Data were collected from 90 searches performed by 30 users using the OVID interface to the CAB abstracts database. Data-gathering techniques included questionnaires, screen capturing software, and interviews. The results presented here relate to issues of search-topic and search-term characteristics, number and types of expanded queries, usefulness of thesaurus terms, and behavioral differences between academic staff and postgraduate students in their interaction. The key conclusions drawn were that (a) academic staff chose more narrow and synonymous terms than did postgraduate students, who generally selected broader and related terms; (b) topic complexity affected users' interaction with the thesaurus in that complex topics required more query expansion and search term selection; (c) users' prior topic-search experience appeared to have a significant effect on their selection and evaluation of thesaurus terms; (d) in 50% of the searches where additional terms were suggested from the thesaurus, users stated that they had not been aware of the terms at the beginning of the search; this observation was particularly noticeable in the case of postgraduate students.

Date

22. 7.2006 16:32:43

0.025416005 = product of:
  0.05083201 = sum of:
    0.05083201 = product of:
      0.10166402 = sum of:
        0.10166402 = weight(_text_:web in 431) [ClassicSimilarity], result of:
          0.10166402 = score(doc=431,freq=22.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.59793836 = fieldWeight in 431, product of:
              4.690416 = tf(freq=22.0), with freq of:
                22.0 = termFreq=22.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0390625 = fieldNorm(doc=431)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The birth of the Web has brought an exponential growth to the amount of the information that is freely available to the Internet population, overloading users and entangling their efforts to satisfy their information needs. Web search engines such Google, Yahoo, or Bing have become popular mainly due to the fact that they offer an easy-to-use query interface (i.e., based on keywords) and an effective and efficient query execution mechanism. The majority of these search engines do not consider information stored on the deep or hidden Web [9,28], despite the fact that the size of the deep Web is estimated to be much bigger than the surface Web [9,47]. There have been a number of systems that record interactions with the deep Web sources or automatically submit queries them (mainly through their Web form interfaces) in order to index their context. Unfortunately, this technique is only partially indexing the data instance. Moreover, it is not possible to take advantage of the query capabilities of data sources, for example, of the relational query features, because their interface is often restricted from the Web form. Besides, Web search engines focus on retrieving documents and not on querying structured sources, so they are unable to access information based on concepts.

Source

Semantic search over the Web. Eds.: R. De Virgilio, et al

Theme

Semantic Web

0.024956053 = product of:
  0.049912106 = sum of:
    0.049912106 = product of:
      0.09982421 = sum of:
        0.09982421 = weight(_text_:22 in 1352) [ClassicSimilarity], result of:
          0.09982421 = score(doc=1352,freq=4.0), product of:
            0.18244034 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052098576 = queryNorm
            0.54716086 = fieldWeight in 1352, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=1352)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22. 2.2003 17:25:39
22. 2.2003 18:17:40

0.024705233 = product of:
  0.049410466 = sum of:
    0.049410466 = product of:
      0.09882093 = sum of:
        0.09882093 = weight(_text_:22 in 2134) [ClassicSimilarity], result of:
          0.09882093 = score(doc=2134,freq=2.0), product of:
            0.18244034 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052098576 = queryNorm
            0.5416616 = fieldWeight in 2134, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.109375 = fieldNorm(doc=2134)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

30. 3.2001 13:32:22

0.02432995 = product of:
  0.0486599 = sum of:
    0.0486599 = product of:
      0.0973198 = sum of:
        0.0973198 = weight(_text_:web in 3090) [ClassicSimilarity], result of:
          0.0973198 = score(doc=3090,freq=14.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.57238775 = fieldWeight in 3090, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.046875 = fieldNorm(doc=3090)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Recent Web-searching and -mining tools are combining text and link analysis to improve ranking and crawling algorithms. The central assumption behind such approaches is that there is a correiation between the graph structure of the Web and the text and meaning of pages. Here I formalize and empirically evaluate two general conjectures drawing connections from link information to lexical and semantic Web content. The link-content conjecture states that a page is similar to the pages that link to it, and the link-cluster conjecture that pages about the same topic are clustered together. These conjectures are offen simply assumed to hold, and Web search tools are built an such assumptions. The present quantitative confirmation sheds light an the connection between the success of the latest Web-mining techniques and the small world topology of the Web, with encouraging implications for the design of better crawling algorithms.

0.02432995 = product of:
  0.0486599 = sum of:
    0.0486599 = product of:
      0.0973198 = sum of:
        0.0973198 = weight(_text_:web in 3310) [ClassicSimilarity], result of:
          0.0973198 = score(doc=3310,freq=14.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.57238775 = fieldWeight in 3310, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.046875 = fieldNorm(doc=3310)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Introducing context to a user query is effective to improve the search effectiveness. In this article we propose a method employing the taxonomy-based search services such as Web directories to facilitate searches in any Web search interfaces that support Boolean queries. The proposed method enables one to convey current search context an taxonomy of a taxonomy-based search service to the searches conducted with the Web search interfaces. The basic idea is to learn the search context in the form of a Boolean condition that is commonly accepted by many Web search interfaces, and to use the condition to modify the user query before forwarding it to the Web search interfaces. To guarantee that the modified query can always be processed by the Web search interfaces and to make the method adaptive to different user requirements an search result effectiveness, we have developed new fast classification learning algorithms.

0.02056256 = product of:
  0.04112512 = sum of:
    0.04112512 = product of:
      0.08225024 = sum of:
        0.08225024 = weight(_text_:web in 5698) [ClassicSimilarity], result of:
          0.08225024 = score(doc=5698,freq=10.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.48375595 = fieldWeight in 5698, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.046875 = fieldNorm(doc=5698)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Describes the shortcomings of search engines for the WWW comparing their current capabilities to those of the first generation CD-ROM products. Some allow phrase searching and most are improving their Boolean searching. Few allow truncation, wild cards or nested logic. They are stateless, losing previous search criteria. Unlike the indexing and classification systems for today's CD-ROMs, those for Web pages are random, unstructured and of variable quality. Considers that at best Web search engines can only offer free text searching. Discusses whether automatic data classification systems such as Infoseek Ultra can overcome the haphazard nature of the Web with neural network technology, and whether Boolean search techniques may be redundant when replaced by technology such as the Euroferret search engine. However, artificial intelligence is rarely successful on huge, varied databases. Relevance ranking and automatic query expansion still use the same simple inverted indexes. Most Web search engines do nothing more than word counting. Further complications arise with foreign languages

0.020332802 = product of:
  0.040665604 = sum of:
    0.040665604 = product of:
      0.08133121 = sum of:
        0.08133121 = weight(_text_:web in 411) [ClassicSimilarity], result of:
          0.08133121 = score(doc=411,freq=22.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.47835067 = fieldWeight in 411, product of:
              4.690416 = tf(freq=22.0), with freq of:
                22.0 = termFreq=22.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.03125 = fieldNorm(doc=411)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The Web has become the world's largest database, with search being the main tool that allows organizations and individuals to exploit its huge amount of information. Search on the Web has been traditionally based on textual and structural similarities, ignoring to a large degree the semantic dimension, i.e., understanding the meaning of the query and of the document content. Combining search and semantics gives birth to the idea of semantic search. Traditional search engines have already advertised some semantic dimensions. Some of them, for instance, can enhance their generated result sets with documents that are semantically related to the query terms even though they may not include these terms. Nevertheless, the exploitation of the semantic search has not yet reached its full potential. In this book, Roberto De Virgilio, Francesco Guerra and Yannis Velegrakis present an extensive overview of the work done in Semantic Search and other related areas. They explore different technologies and solutions in depth, making their collection a valuable and stimulating reading for both academic and industrial researchers. The book is divided into three parts. The first introduces the readers to the basic notions of the Web of Data. It describes the different kinds of data that exist, their topology, and their storing and indexing techniques. The second part is dedicated to Web Search. It presents different types of search, like the exploratory or the path-oriented, alongside methods for their efficient and effective implementation. Other related topics included in this part are the use of uncertainty in query answering, the exploitation of ontologies, and the use of semantics in mashup design and operation. The focus of the third part is on linked data, and more specifically, on applying ideas originating in recommender systems on linked data management, and on techniques for the efficiently querying answering on linked data.

Content

Inhalt: Introduction.- Part I Introduction to Web of Data.- Topology of the Web of Data.- Storing and Indexing Massive RDF Data Sets.- Designing Exploratory Search Applications upon Web Data Sources.- Part II Search over the Web.- Path-oriented Keyword Search query over RDF.- Interactive Query Construction for Keyword Search on the SemanticWeb.- Understanding the Semantics of Keyword Queries on Relational DataWithout Accessing the Instance.- Keyword-Based Search over Semantic Data.- Semantic Link Discovery over Relational Data.- Embracing Uncertainty in Entity Linking.- The Return of the Entity-Relationship Model: Ontological Query Answering.- Linked Data Services and Semantics-enabled Mashup.- Part III Linked Data Search engines.- A Recommender System for Linked Data.- Flint: from Web Pages to Probabilistic Semantic Data.- Searching and Browsing Linked Data with SWSE.

Theme

Semantic Web

0.01839171 = product of:
  0.03678342 = sum of:
    0.03678342 = product of:
      0.07356684 = sum of:
        0.07356684 = weight(_text_:web in 2236) [ClassicSimilarity], result of:
          0.07356684 = score(doc=2236,freq=8.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.43268442 = fieldWeight in 2236, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.046875 = fieldNorm(doc=2236)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Collection sizes, query rates, and the number of users of Web search engines are increasing. Therefore, there is continued demand for innovation in providing search services that meet user information needs. In this article, we propose new techniques to add additional terms to documents with the goal of providing more accurate searches. Our techniques are based an query association, where queries are stored with documents that are highly similar statistically. We show that adding query associations to documents improves the accuracy of Web topic finding searches by up to 7%, and provides an excellent complement to existing supplement techniques for site finding. We conclude that using document surrogates derived from query association is a valuable new technique for accurate Web searching.

0.01839171 = product of:
  0.03678342 = sum of:
    0.03678342 = product of:
      0.07356684 = sum of:
        0.07356684 = weight(_text_:web in 2591) [ClassicSimilarity], result of:
          0.07356684 = score(doc=2591,freq=18.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.43268442 = fieldWeight in 2591, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.03125 = fieldNorm(doc=2591)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

This study examined the success and information seeking behaviors of seventh-grade science students and graduate students in information science in using Yahooligans! Web search engine/directory. It investigated these users' cognitive, affective, and physical behaviors as they sought the answer for a fact-finding task. It analyzed and compared the overall patterns of children's and graduate students' Web activities, including searching moves, browsing moves, backtracking moves, looping moves, screen scrolling, target location and deviation moves, and the time they took to complete the task. The authors applied Bilal's Web Traversal Measure to quantify these users' effectiveness, efficiency, and quality of moves they made. Results were based on 14 children's Web sessions and nine graduate students' sessions. Both groups' Web activities were captured online using Lotus ScreenCam, a software package that records and replays online activities in Web browsers. Children's affective states were captured via exit interviews. Graduate students' affective states were extracted from the journal writings they kept during the traversal process. The study findings reveal that 89% of the graduate students found the correct answer to the search task as opposed to 50% of the children. Based on the Measure, graduate students' weighted effectiveness, efficiency, and quality of the Web moves they made were much higher than those of the children. Regardless of success and weighted scores, however, similarities and differences in information seeking were found between the two groups. Yahooligans! poor structure of keyword searching was a major factor that contributed to the "breakdowns" children and graduate students experienced. Unlike children, graduate students were able to recover from "breakdowns" quickly and effectively. Three main factors influenced these users' performance: ability to recover from "breakdowns", navigational style, and focus on task. Children and graduate students made recommendations for improving Yahooligans! interface design. Implications for Web user training and system design improvements are made.

0.017646596 = product of:
  0.03529319 = sum of:
    0.03529319 = product of:
      0.07058638 = sum of:
        0.07058638 = weight(_text_:22 in 2751) [ClassicSimilarity], result of:
          0.07058638 = score(doc=2751,freq=2.0), product of:
            0.18244034 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052098576 = queryNorm
            0.38690117 = fieldWeight in 2751, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=2751)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

1. 2.2016 18:25:22

0.017646596 = product of:
  0.03529319 = sum of:
    0.03529319 = product of:
      0.07058638 = sum of:
        0.07058638 = weight(_text_:22 in 2754) [ClassicSimilarity], result of:
          0.07058638 = score(doc=2754,freq=2.0), product of:
            0.18244034 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052098576 = queryNorm
            0.38690117 = fieldWeight in 2754, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=2754)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

1. 2.2016 18:25:22

0.017646596 = product of:
  0.03529319 = sum of:
    0.03529319 = product of:
      0.07058638 = sum of:
        0.07058638 = weight(_text_:22 in 3279) [ClassicSimilarity], result of:
          0.07058638 = score(doc=3279,freq=2.0), product of:
            0.18244034 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052098576 = queryNorm
            0.38690117 = fieldWeight in 3279, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=3279)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Source

Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou

0.017646596 = product of:
  0.03529319 = sum of:
    0.03529319 = product of:
      0.07058638 = sum of:
        0.07058638 = weight(_text_:22 in 3280) [ClassicSimilarity], result of:
          0.07058638 = score(doc=3280,freq=2.0), product of:
            0.18244034 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052098576 = queryNorm
            0.38690117 = fieldWeight in 3280, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=3280)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Source

Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou

0.017469238 = product of:
  0.034938477 = sum of:
    0.034938477 = product of:
      0.069876954 = sum of:
        0.069876954 = weight(_text_:22 in 5295) [ClassicSimilarity], result of:
          0.069876954 = score(doc=5295,freq=4.0), product of:
            0.18244034 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052098576 = queryNorm
            0.38301262 = fieldWeight in 5295, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=5295)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22. 7.2006 17:56:22

0.017339872 = product of:
  0.034679744 = sum of:
    0.034679744 = product of:
      0.06935949 = sum of:
        0.06935949 = weight(_text_:web in 530) [ClassicSimilarity], result of:
          0.06935949 = score(doc=530,freq=4.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.4079388 = fieldWeight in 530, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0625 = fieldNorm(doc=530)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Das WWW ist dumm. Ein neuer Ansatz soll jetzt dafür sorgen, dass Maschinen Bedeutungen erfassen und Informationen richtig einordnen können. Das ist noch nicht alles: Wenn die Server erst mal das Verstehen gelernt haben sollten, würden sie auch in der Lage sein, uns von den Ergebnissen ihrer Plaudereien untereinander zu berichten - das 'semantische Web' wäre geboren