Search (345 results, page 2 of 18)

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

0.04779466 = product of:
  0.09558932 = sum of:
    0.09558932 = sum of:
      0.06502452 = weight(_text_:web in 150) [ClassicSimilarity], result of:
        0.06502452 = score(doc=150,freq=36.0), product of:
          0.17002425 = queryWeight, product of:
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.052098576 = queryNorm
          0.38244262 = fieldWeight in 150, product of:
            6.0 = tf(freq=36.0), with freq of:
              36.0 = termFreq=36.0
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.01953125 = fieldNorm(doc=150)
      0.030564802 = weight(_text_:22 in 150) [ClassicSimilarity], result of:
        0.030564802 = score(doc=150,freq=6.0), product of:
          0.18244034 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052098576 = queryNorm
          0.16753313 = fieldWeight in 150, product of:
            2.4494898 = tf(freq=6.0), with freq of:
              6.0 = termFreq=6.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.01953125 = fieldNorm(doc=150)
  0.5 = coord(1/2)

Classification

006.7 22

Date

7. 3.2007 19:30:22

DDC

006.7 22

Footnote

Rez. in: JASIST 58(2007) no.3, S.457-458 (A.M.A. Ahmad): "The concept of the semantic web has emerged because search engines and text-based searching are no longer adequate, as these approaches involve an extensive information retrieval process. The deployed searching and retrieving descriptors arc naturally subjective and their deployment is often restricted to the specific application domain for which the descriptors were configured. The new era of information technology imposes different kinds of requirements and challenges. Automatic extracted audiovisual features are required, as these features are more objective, domain-independent, and more native to audiovisual content. This book is a useful guide for researchers, experts, students, and practitioners; it is a very valuable reference and can lead them through their exploration and research in multimedia content and the semantic web. The book is well organized, and introduces the concept of the semantic web and multimedia content analysis to the reader through a logical sequence from standards and hypotheses through system examples, presenting relevant tools and methods. But in some chapters readers will need a good technical background to understand some of the details. Readers may attain sufficient knowledge here to start projects or research related to the book's theme; recent results and articles related to the active research area of integrating multimedia with semantic web technologies are included. This book includes full descriptions of approaches to specific problem domains such as content search, indexing, and retrieval. This book will be very useful to researchers in the multimedia content analysis field who wish to explore the benefits of emerging semantic web technologies in applying multimedia content approaches. The first part of the book covers the definition of the two basic terms multimedia content and semantic web. The Moving Picture Experts Group standards MPEG7 and MPEG21 are quoted extensively. In addition, the means of multimedia content description are elaborated upon and schematically drawn. This extensive description is introduced by authors who are actively involved in those standards and have been participating in the work of the International Organization for Standardization (ISO)/MPEG for many years. On the other hand, this results in bias against the ad hoc or nonstandard tools for multimedia description in favor of the standard approaches. This is a general book for multimedia content; more emphasis on the general multimedia description and extraction could be provided.
Semantic web technologies are explained, and ontology representation is emphasized. There is an excellent summary of the fundamental theory behind applying a knowledge-engineering approach to vision problems. This summary represents the concept of the semantic web and multimedia content analysis. A definition of the fuzzy knowledge representation that can be used for realization in multimedia content applications has been provided, with a comprehensive analysis. The second part of the book introduces the multimedia content analysis approaches and applications. In addition, some examples of methods applicable to multimedia content analysis are presented. Multimedia content analysis is a very diverse field and concerns many other research fields at the same time; this creates strong diversity issues, as everything from low-level features (e.g., colors, DCT coefficients, motion vectors, etc.) up to the very high and semantic level (e.g., Object, Events, Tracks, etc.) are involved. The second part includes topics on structure identification (e.g., shot detection for video sequences), and object-based video indexing. These conventional analysis methods are supplemented by results on semantic multimedia analysis, including three detailed chapters on the development and use of knowledge models for automatic multimedia analysis. Starting from object-based indexing and continuing with machine learning, these three chapters are very logically organized. Because of the diversity of this research field, including several chapters of recent research results is not sufficient to cover the state of the art of multimedia. The editors of the book should write an introductory chapter about multimedia content analysis approaches, basic problems, and technical issues and challenges, and try to survey the state of the art of the field and thus introduce the field to the reader.
The final part of the book discusses research in multimedia content management systems and the semantic web, and presents examples and applications for semantic multimedia analysis in search and retrieval systems. These chapters describe example systems in which current projects have been implemented, and include extensive results and real demonstrations. For example, real case scenarios such as ECommerce medical applications and Web services have been introduced. Topics in natural language, speech and image processing techniques and their application for multimedia indexing, and content-based retrieval have been elaborated upon with extensive examples and deployment methods. The editors of the book themselves provide the readers with a chapter about their latest research results on knowledge-based multimedia content indexing and retrieval. Some interesting applications for multimedia content and the semantic web are introduced. Applications that have taken advantage of the metadata provided by MPEG7 in order to realize advance-access services for multimedia content have been provided. The applications discussed in the third part of the book provide useful guidance to researchers and practitioners properly planning to implement semantic multimedia analysis techniques in new research and development projects in both academia and industry. A fourth part should be added to this book: performance measurements for integrated approaches of multimedia analysis and the semantic web. Performance of the semantic approach is a very sophisticated issue and requires extensive elaboration and effort. Measuring the semantic search is an ongoing research area; several chapters concerning performance measurement and analysis would be required to adequately cover this area and introduce it to readers."

LCSH

Semantic Web

RSWK

Semantic Web / Multimedia / Automatische Indexierung / Information Retrieval

Subject

Semantic Web / Multimedia / Automatische Indexierung / Information Retrieval
Semantic Web

Theme

Semantic Web

0.046162233 = product of:
  0.092324466 = sum of:
    0.092324466 = sum of:
      0.042913996 = weight(_text_:web in 3283) [ClassicSimilarity], result of:
        0.042913996 = score(doc=3283,freq=2.0), product of:
          0.17002425 = queryWeight, product of:
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.052098576 = queryNorm
          0.25239927 = fieldWeight in 3283, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.0546875 = fieldNorm(doc=3283)
      0.049410466 = weight(_text_:22 in 3283) [ClassicSimilarity], result of:
        0.049410466 = score(doc=3283,freq=2.0), product of:
          0.18244034 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052098576 = queryNorm
          0.2708308 = fieldWeight in 3283, 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=3283)
  0.5 = coord(1/2)

Theme

Semantic Web

0.044192746 = product of:
  0.08838549 = sum of:
    0.08838549 = sum of:
      0.053092297 = weight(_text_:web in 4553) [ClassicSimilarity], result of:
        0.053092297 = score(doc=4553,freq=6.0), product of:
          0.17002425 = queryWeight, product of:
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.052098576 = queryNorm
          0.3122631 = fieldWeight in 4553, product of:
            2.4494898 = tf(freq=6.0), with freq of:
              6.0 = termFreq=6.0
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.0390625 = fieldNorm(doc=4553)
      0.03529319 = weight(_text_:22 in 4553) [ClassicSimilarity], result of:
        0.03529319 = score(doc=4553,freq=2.0), product of:
          0.18244034 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052098576 = queryNorm
          0.19345059 = fieldWeight in 4553, 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=4553)
  0.5 = coord(1/2)

Abstract

Semantic Web knowledge representation standards, and in particular RDF and OWL, often come endowed with a formal semantics which is considered to be of fundamental importance for the field. Reasoning, i.e., the drawing of logical inferences from knowledge expressed in such standards, is traditionally based on logical deductive methods and algorithms which can be proven to be sound and complete and terminating, i.e. correct in a very strong sense. For various reasons, though, in particular the scalability issues arising from the ever increasing amounts of Semantic Web data available and the inability of deductive algorithms to deal with noise in the data, it has been argued that alternative means of reasoning should be investigated which bear high promise for high scalability and better robustness. From this perspective, deductive algorithms can be considered the gold standard regarding correctness against which alternative methods need to be tested. In this paper, we show that it is possible to train a Deep Learning system on RDF knowledge graphs, such that it is able to perform reasoning over new RDF knowledge graphs, with high precision and recall compared to the deductive gold standard.

Date

16.11.2018 14:22:01

Theme

Semantic Web

0.044150814 = product of:
  0.08830163 = sum of:
    0.08830163 = sum of:
      0.06006708 = weight(_text_:web in 2661) [ClassicSimilarity], result of:
        0.06006708 = score(doc=2661,freq=12.0), product of:
          0.17002425 = queryWeight, product of:
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.052098576 = queryNorm
          0.35328537 = fieldWeight in 2661, product of:
            3.4641016 = tf(freq=12.0), with freq of:
              12.0 = termFreq=12.0
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.03125 = fieldNorm(doc=2661)
      0.028234553 = weight(_text_:22 in 2661) [ClassicSimilarity], result of:
        0.028234553 = score(doc=2661,freq=2.0), product of:
          0.18244034 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052098576 = queryNorm
          0.15476047 = fieldWeight in 2661, 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=2661)
  0.5 = coord(1/2)

Abstract

The basic goal of education within a discipline is to transform a novice into an expert. This entails moving the novice toward the "semantic space" that the expert inhabits-the space of concepts, meanings, vocabularies, and other intellectual constructs that comprise the discipline. Metadata is significant to this goal in digitally mediated education environments. Encoding the experts' semantic space not only enables the sharing of semantics among discipline scientists, but also creates an environment that bridges the semantic gap between the common vocabulary of the novice and the granular descriptive language of the seasoned scientist (Greenberg, et al, 2005). Developments underlying the Semantic Web, where vocabularies are formalized in the Web Ontology Language (OWL), and Web 2.0 approaches of user-generated folksonomies provide an infrastructure for linking vocabulary systems and promoting group learning via metadata literacy. Group learning is a pedagogical approach to teaching that harnesses the phenomenon of "collective intelligence" to increase learning by means of collaboration. Learning a new semantic system can be daunting for a novice, and yet it is integral to advance one's knowledge in a discipline and retain interest. These ideas are key to the "BOT 2.0: Botany through Web 2.0, the Memex and Social Learning" project (Bot 2.0).72 Bot 2.0 is a collaboration involving the North Carolina Botanical Garden, the UNC SILS Metadata Research center, and the Renaissance Computing Institute (RENCI). Bot 2.0 presents a curriculum utilizing a memex as a way for students to link and share digital information, working asynchronously in an environment beyond the traditional classroom. Our conception of a memex is not a centralized black box but rather a flexible, distributed framework that uses the most salient and easiest-to-use collaborative platforms (e.g., Facebook, Flickr, wiki and blog technology) for personal information management. By meeting students "where they live" digitally, we hope to attract students to the study of botanical science. A key aspect is to teach students scientific terminology and about the value of metadata, an inherent function in several of the technologies and in the instructional approach we are utilizing. This poster will report on a study examining the value of both folksonomies and taxonomies for post-secondary college students learning plant identification. Our data is drawn from a curriculum involving a virtual independent learning portion and a "BotCamp" weekend at UNC, where students work with digital plan specimens that they have captured. Results provide some insight into the importance of collaboration and shared vocabulary for gaining confidence and for student progression from novice to expert in botany.

Source

Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas

Theme

Semantic Web

0.04334968 = product of:
  0.08669936 = sum of:
    0.08669936 = product of:
      0.17339872 = sum of:
        0.17339872 = weight(_text_:web in 4868) [ClassicSimilarity], result of:
          0.17339872 = score(doc=4868,freq=16.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            1.019847 = fieldWeight in 4868, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.078125 = fieldNorm(doc=4868)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Web 2.0 und Semantic Web schicken sich gleichermaßen an, dem klassischen WWW neuen Lebensatem einzuhauchen. Dabei könnte Web 2.0 sich zu genau dem entwickeln, was das Semantic Web sein wollte, nie wurde und womöglich niemals sein kann

Object

Web 2.0

Theme

Semantic Web

0.039567623 = product of:
  0.07913525 = sum of:
    0.07913525 = sum of:
      0.03678342 = weight(_text_:web in 662) [ClassicSimilarity], result of:
        0.03678342 = score(doc=662,freq=2.0), product of:
          0.17002425 = queryWeight, product of:
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.052098576 = queryNorm
          0.21634221 = fieldWeight in 662, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.046875 = fieldNorm(doc=662)
      0.042351827 = weight(_text_:22 in 662) [ClassicSimilarity], result of:
        0.042351827 = score(doc=662,freq=2.0), product of:
          0.18244034 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052098576 = queryNorm
          0.23214069 = fieldWeight in 662, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046875 = fieldNorm(doc=662)
  0.5 = coord(1/2)

Date

22. 3.2013 19:29:20

Theme

Semantic Web

0.039567623 = product of:
  0.07913525 = sum of:
    0.07913525 = sum of:
      0.03678342 = weight(_text_:web in 2024) [ClassicSimilarity], result of:
        0.03678342 = score(doc=2024,freq=2.0), product of:
          0.17002425 = queryWeight, product of:
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.052098576 = queryNorm
          0.21634221 = fieldWeight in 2024, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.046875 = fieldNorm(doc=2024)
      0.042351827 = weight(_text_:22 in 2024) [ClassicSimilarity], result of:
        0.042351827 = score(doc=2024,freq=2.0), product of:
          0.18244034 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052098576 = queryNorm
          0.23214069 = fieldWeight in 2024, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046875 = fieldNorm(doc=2024)
  0.5 = coord(1/2)

Source

Bulletin of the Association for Information Science and Technology. 41(2015) no.4, S.18-22

Theme

Semantic Web

0.037304714 = product of:
  0.07460943 = sum of:
    0.07460943 = sum of:
      0.034679744 = weight(_text_:web in 2654) [ClassicSimilarity], result of:
        0.034679744 = score(doc=2654,freq=4.0), product of:
          0.17002425 = queryWeight, product of:
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.052098576 = queryNorm
          0.2039694 = fieldWeight in 2654, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.03125 = fieldNorm(doc=2654)
      0.039929688 = weight(_text_:22 in 2654) [ClassicSimilarity], result of:
        0.039929688 = score(doc=2654,freq=4.0), product of:
          0.18244034 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052098576 = queryNorm
          0.21886435 = fieldWeight in 2654, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.03125 = fieldNorm(doc=2654)
  0.5 = coord(1/2)

Abstract

In order to transfer the Chinese Classified Thesaurus (CCT) into a machine-processable format and provide CCT-based Web services, a pilot study has been conducted in which a variety of selected CCT classes and mapped thesaurus entries are encoded with SKOS. OWL and RDFS are also used to encode the same contents for the purposes of feasibility and cost-benefit comparison. CCT is a collected effort led by the National Library of China. It is an integration of the national standards Chinese Library Classification (CLC) 4th edition and Chinese Thesaurus (CT). As a manually created mapping product, CCT provides for each of the classes the corresponding thesaurus terms, and vice versa. The coverage of CCT includes four major clusters: philosophy, social sciences and humanities, natural sciences and technologies, and general works. There are 22 main-classes, 52,992 sub-classes and divisions, 110,837 preferred thesaurus terms, 35,690 entry terms (non-preferred terms), and 59,738 pre-coordinated headings (Chinese Classified Thesaurus, 2005) Major challenges of encoding this large vocabulary comes from its integrated structure. CCT is a result of the combination of two structures (illustrated in Figure 1): a thesaurus that uses ISO-2788 standardized structure and a classification scheme that is basically enumerative, but provides some flexibility for several kinds of synthetic mechanisms Other challenges include the complex relationships caused by differences of granularities of two original schemes and their presentation with various levels of SKOS elements; as well as the diverse coordination of entries due to the use of auxiliary tables and pre-coordinated headings derived from combining classes, subdivisions, and thesaurus terms, which do not correspond to existing unique identifiers. The poster reports the progress, shares the sample SKOS entries, and summarizes problems identified during the SKOS encoding process. Although OWL Lite and OWL Full provide richer expressiveness, the cost-benefit issues and the final purposes of encoding CCT raise questions of using such approaches.

Source

Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas

Theme

Semantic Web

0.03678342 = product of:
  0.07356684 = sum of:
    0.07356684 = product of:
      0.14713368 = sum of:
        0.14713368 = weight(_text_:web in 5788) [ClassicSimilarity], result of:
          0.14713368 = score(doc=5788,freq=18.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.86536884 = fieldWeight in 5788, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0625 = fieldNorm(doc=5788)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Semantic Web - das ist die Anwendung von Wissenstechnologie im World Wide Web. Dieses Kapitel beschreibt in einigen einführenden Absätzen die Aufgabe und Entstehung von Standards. Sodann gibt es einen Überblick über die Technologien und Standards, die für das Web und seine Erweiterung zum Semantic Web entwickelt und eingesetzt werden. Diese werden überwiegend vom World Wide Web Consortium (W3C) [35] definiert. Abschließend folgen einige Bemerkungen zur weiteren Entwicklung des Semantic Web.

Source

Semantic Web: Wege zur vernetzten Wissensgesellschaft. Hrsg.: T. Pellegrini, u. A. Blumauer

Theme

Semantic Web

0.035117254 = product of:
  0.07023451 = sum of:
    0.07023451 = product of:
      0.14046901 = sum of:
        0.14046901 = weight(_text_:web in 4706) [ClassicSimilarity], result of:
          0.14046901 = score(doc=4706,freq=42.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.8261705 = fieldWeight in 4706, product of:
              6.4807405 = tf(freq=42.0), with freq of:
                42.0 = termFreq=42.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4706)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The two-volume set LNCS 6496 and 6497 constitutes the refereed proceedings of the 9th International Semantic Web Conference, ISWC 2010, held in Shanghai, China, during November 7-11, 2010. Part I contains 51 papers out of 578 submissions to the research track. Part II contains 18 papers out of 66 submissions to the semantic Web in-use track, 6 papers out of 26 submissions to the doctoral consortium track, and also 4 invited talks. Each submitted paper were carefully reviewed. The International Semantic Web Conferences (ISWC) constitute the major international venue where the latest research results and technical innovations on all aspects of the Semantic Web are presented. ISWC brings together researchers, practitioners, and users from the areas of artificial intelligence, databases, social networks, distributed computing, Web engineering, information systems, natural language processing, soft computing, and human computer interaction to discuss the major challenges and proposed solutions, the success stories and failures, as well the visions that can advance research and drive innovation in the Semantic Web.

RSWK

Semantic Web / Kongress / Schanghai <2010>
Semantic Web / Ontologie <Wissensverarbeitung> / Kongress / Schanghai <2010>
Semantic Web / Datenverwaltung / Wissensmanagement / Kongress / Schanghai <2010>
Semantic Web / Anwendungssystem / Kongress / Schanghai <2010>
Semantic Web / World Wide Web 2.0 / Kongress / Schanghai <2010>

Subject

Semantic Web / Kongress / Schanghai <2010>
Semantic Web / Ontologie <Wissensverarbeitung> / Kongress / Schanghai <2010>
Semantic Web / Datenverwaltung / Wissensmanagement / Kongress / Schanghai <2010>
Semantic Web / Anwendungssystem / Kongress / Schanghai <2010>
Semantic Web / World Wide Web 2.0 / Kongress / Schanghai <2010>

Theme

Semantic Web

0.034679744 = product of:
  0.06935949 = sum of:
    0.06935949 = product of:
      0.13871898 = sum of:
        0.13871898 = weight(_text_:web in 4856) [ClassicSimilarity], result of:
          0.13871898 = score(doc=4856,freq=16.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.8158776 = fieldWeight in 4856, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0625 = fieldNorm(doc=4856)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Dieser Beitrag diskutiert die Entwicklungsgeschichte und den Facettenreichtum eines Begriffes, der gleichsam für technologische Innovation, soziale Modernisierung und eine schlaue Marketingstrategie steht. Es zeigt sich, dass die Verortung des Phänomens Web 2.0, gerade aufgrund der Polemik und Polarisierung, die der Begriff hervorgerufen hat, schwieriger ist, als man vermuten mag. Doch eines ist gewiss: Seit das Web 2.0 in unser Bewusstsein gelangt ist, ist das Internet wieder "in".

Object

Web 2.0

Source

Social Semantic Web: Web 2.0, was nun? Hrsg.: A. Blumauer u. T. Pellegrini

Theme

Semantic Web

0.034679744 = product of:
  0.06935949 = sum of:
    0.06935949 = product of:
      0.13871898 = sum of:
        0.13871898 = weight(_text_:web in 54) [ClassicSimilarity], result of:
          0.13871898 = score(doc=54,freq=16.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.8158776 = fieldWeight in 54, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0625 = fieldNorm(doc=54)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

This paper presents the semantic web, web writing content, web technology, goals of semantic and obligation for the expansion of web 3.0. This paper also shows the different components of semantic web and such as HTTP, HTML, XML, XML Schema, URI, RDF, Taxonomy and OWL. To provide valuable information services semantic web execute the benefits of library functions and also to be the best use of library collection are mention here.

Theme

Semantic Web

0.03315613 = product of:
  0.06631226 = sum of:
    0.06631226 = product of:
      0.13262452 = sum of:
        0.13262452 = weight(_text_:web in 244) [ClassicSimilarity], result of:
          0.13262452 = score(doc=244,freq=26.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.780033 = fieldWeight in 244, product of:
              5.0990195 = tf(freq=26.0), with freq of:
                26.0 = termFreq=26.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.046875 = fieldNorm(doc=244)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

This article explores the applicability primary library functions (collection development, cataloging, reference, and circulation) to the Semantic Web. The article defines the Semantic Web, identifies similarities between the library institution and the Semantic Web, and presents research questions guiding the inquiry. The article addresses each library function and demonstrates the applicability of each function's polices to Semantic Web development. Results indicate that library functions are applicable to Semantic Web, with "collection development" translating to "Semantic Web selection;" "cataloging" translating to "Semantic Web 'semantic' representation;" "reference" translating to "Semantic Web service," and circulation translating to "Semantic Web resource use." The last part of this article includes a discussion about the lack of embrace between the library and the Semantic Web communities, recommendations for improving this gap, and research conclusions.

Footnote

Simultaneously published as Knitting the Semantic Web

Theme

Semantic Web

0.032641627 = product of:
  0.065283254 = sum of:
    0.065283254 = sum of:
      0.030344777 = weight(_text_:web in 1155) [ClassicSimilarity], result of:
        0.030344777 = score(doc=1155,freq=4.0), product of:
          0.17002425 = queryWeight, product of:
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.052098576 = queryNorm
          0.17847323 = fieldWeight in 1155, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.02734375 = fieldNorm(doc=1155)
      0.034938477 = weight(_text_:22 in 1155) [ClassicSimilarity], result of:
        0.034938477 = score(doc=1155,freq=4.0), product of:
          0.18244034 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052098576 = queryNorm
          0.19150631 = fieldWeight in 1155, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.02734375 = fieldNorm(doc=1155)
  0.5 = coord(1/2)

Abstract

Metadata and semantics are integral to any information system and significant to the sphere of Web data. Research focusing on metadata and semantics is crucial for advancing our understanding and knowledge of metadata; and, more profoundly for being able to effectively discover, use, archive, and repurpose information. In response to this need, researchers are actively examining methods for generating, reusing, and interchanging metadata. Integrated with these developments is research on the application of computational methods, linked data, and data analytics. A growing body of work also targets conceptual and theoretical designs providing foundational frameworks for metadata and semantic applications. There is no doubt that metadata weaves its way into nearly every aspect of our information ecosystem, and there is great motivation for advancing the current state of metadata and semantics. To this end, it is vital that scholars and practitioners convene and share their work.

Date

17.12.2013 12:51:22

Theme

Semantic Web

0.032439932 = product of:
  0.064879864 = sum of:
    0.064879864 = product of:
      0.12975973 = sum of:
        0.12975973 = weight(_text_:web in 5791) [ClassicSimilarity], result of:
          0.12975973 = score(doc=5791,freq=14.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.76318365 = fieldWeight in 5791, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0625 = fieldNorm(doc=5791)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Dieses Kapitel gibt einen Überblick über die Herausforderungen und Möglichkeiten, die sich durch das Semantic Web für die Personalisierung von Informationssystemen ergeben. Nach einer Definition der Begriffe Personalisierung und Personalisierbare Informationssysteme wird eine kurze Rückschau auf Personalisierungsmethoden in Informationssystemen gegeben. Mit diesem Wissen wird untersucht. welche Ausgangslage für Personalisierung durch das Semantic Web gegeben ist und warum Personalisierung für Anwendungen im Semantic Web wichtig ist. Zwei Beispiele, die Möglichkeiten zur Personalisierung von Informationsangeboten im Web realisieren, beschließen dieses Kapitel.

Source

Semantic Web: Wege zur vernetzten Wissensgesellschaft. Hrsg.: T. Pellegrini, u. A. Blumauer

Theme

Semantic Web

0.032185495 = product of:
  0.06437099 = sum of:
    0.06437099 = product of:
      0.12874198 = sum of:
        0.12874198 = weight(_text_:web in 1541) [ClassicSimilarity], result of:
          0.12874198 = score(doc=1541,freq=18.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.75719774 = fieldWeight in 1541, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1541)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The next major advance in the Web-Web 3.0-will be built on semantic Web technologies, which will allow data to be shared and reused across application, enterprise, and community boundaries. Written by a team of highly experienced Web developers, this book explains examines how this powerful new technology can unify and fully leverage the ever-growing data, information, and services that are available on the Internet. Helpful examples demonstrate how to use the semantic Web to solve practical, real-world problems while you take a look at the set of design principles, collaborative working groups, and technologies that form the semantic Web. The companion Web site features full code, as well as a reference section, a FAQ section, a discussion forum, and a semantic blog.

Theme

Semantic Web

0.032185495 = product of:
  0.06437099 = sum of:
    0.06437099 = product of:
      0.12874198 = sum of:
        0.12874198 = weight(_text_:web in 4061) [ClassicSimilarity], result of:
          0.12874198 = score(doc=4061,freq=18.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.75719774 = fieldWeight in 4061, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4061)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Dieser Artikel gibt einen Einblick in die aktuelle Verschmelzung von Web 2.0-und Semantic Web-Ansätzen, die als Social Semantic Web beschrieben werden kann. Die Grundidee des Social Semantic Web wird beschrieben und einzelne erste Anwendungsbeispiele vorgestellt. Ein wesentlicher Schwerpunkt dieser Entwicklung besteht in der Umsetzung neuer Methoden und Herangehensweisen im Bereich der Wissensrepräsentation. Dieser Artikel stellt vier Schwerpunkte vor, in denen sich die Wissensrepräsentationsmethoden im Social Semantic Web weiterentwickeln müssen und geht dabei jeweils auf den aktuellen Stand ein.

Object

Web 2.0

Source

Semantic web & linked data: Elemente zukünftiger Informationsinfrastrukturen ; 1. DGI-Konferenz ; 62. Jahrestagung der DGI ; Frankfurt am Main, 7. - 9. Oktober 2010 ; Proceedings / Deutsche Gesellschaft für Informationswissenschaft und Informationspraxis. Hrsg.: M. Ockenfeld

Theme

Semantic Web

0.032185495 = product of:
  0.06437099 = sum of:
    0.06437099 = product of:
      0.12874198 = sum of:
        0.12874198 = weight(_text_:web in 4877) [ClassicSimilarity], result of:
          0.12874198 = score(doc=4877,freq=18.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.75719774 = fieldWeight in 4877, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4877)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Zwei Trends prägen derzeit die Gestalt des Internets: Auf der einen Seite finden sich Entwicklungen rund um das "Web 2.0" oder "Social Web", die dem einzelnen Nutzer neue Möglichkeiten des onlinegestützten Identitäts-, Beziehungs- und Informationsmanagements eröffnen. Auf der anderen Seite stehen die Innovationen des Semantic Web, die Relationen zwischen Datenbeständen strukturieren helfen, um so zu verbesserten maschinellen Repräsentationen von Wissen zu gelangen. Dieser Beitrag skizziert die Idee eines "Social Semantic Web", in dem beide Entwicklungen zusammenfließen. Als Scharnier dient dabei der Begriff der Prodnutzung, der die aktive Rolle des Nutzers bei der Erstellung, Verbreitung und Weiterentwicklung von Inhalten wie von strukturiertem Wissen betont.

Object

Web 2.0

Source

Social Semantic Web: Web 2.0, was nun? Hrsg.: A. Blumauer u. T. Pellegrini

Theme

Semantic Web

0.032185495 = product of:
  0.06437099 = sum of:
    0.06437099 = product of:
      0.12874198 = sum of:
        0.12874198 = weight(_text_:web in 3939) [ClassicSimilarity], result of:
          0.12874198 = score(doc=3939,freq=18.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.75719774 = fieldWeight in 3939, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3939)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The Semantic Web has attracted much attention, both from academia and industry. An important role in research towards the Semantic Web is played by formalisms and technologies for handling uncertainty and/or vagueness. In this paper, I first provide some motivating examples for handling uncertainty and/or vagueness in the Semantic Web. I then give an overview of some own formalisms for handling uncertainty and/or vagueness in the Semantic Web.

Series

Lecture Notes in Computer Scienc;10370) (Information Systems and Applications, incl. Internet/Web, and HCI

Source

Reasoning Web: Semantic Interoperability on the Web, 13th International Summer School 2017, London, UK, July 7-11, 2017, Tutorial Lectures. Eds.: Ianni, G. et al

Theme

Semantic Web