Search (91 results, page 1 of 5)

0.067937374 = product of:
  0.18116634 = sum of:
    0.056407593 = weight(_text_:world in 95) [ClassicSimilarity], result of:
      0.056407593 = score(doc=95,freq=4.0), product of:
        0.15653811 = queryWeight, product of:
          3.8436708 = idf(docFreq=2573, maxDocs=44218)
          0.040726203 = queryNorm
        0.36034414 = fieldWeight in 95, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.8436708 = idf(docFreq=2573, maxDocs=44218)
          0.046875 = fieldNorm(doc=95)
    0.07495513 = weight(_text_:wide in 95) [ClassicSimilarity], result of:
      0.07495513 = score(doc=95,freq=4.0), product of:
        0.18044797 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.040726203 = queryNorm
        0.4153836 = fieldWeight in 95, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.046875 = fieldNorm(doc=95)
    0.049803615 = weight(_text_:web in 95) [ClassicSimilarity], result of:
      0.049803615 = score(doc=95,freq=6.0), product of:
        0.13291039 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.040726203 = queryNorm
        0.37471575 = fieldWeight in 95, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=95)
  0.375 = coord(3/8)

Abstract

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

0.061363168 = product of:
  0.122726336 = sum of:
    0.016619246 = weight(_text_:world in 357) [ClassicSimilarity], result of:
      0.016619246 = score(doc=357,freq=2.0), product of:
        0.15653811 = queryWeight, product of:
          3.8436708 = idf(docFreq=2573, maxDocs=44218)
          0.040726203 = queryNorm
        0.10616741 = fieldWeight in 357, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.8436708 = idf(docFreq=2573, maxDocs=44218)
          0.01953125 = fieldNorm(doc=357)
    0.031231305 = weight(_text_:wide in 357) [ClassicSimilarity], result of:
      0.031231305 = score(doc=357,freq=4.0), product of:
        0.18044797 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.040726203 = queryNorm
        0.17307651 = fieldWeight in 357, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.01953125 = fieldNorm(doc=357)
    0.044828374 = weight(_text_:web in 357) [ClassicSimilarity], result of:
      0.044828374 = score(doc=357,freq=28.0), product of:
        0.13291039 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.040726203 = queryNorm
        0.3372827 = fieldWeight in 357, product of:
          5.2915025 = tf(freq=28.0), with freq of:
            28.0 = termFreq=28.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.01953125 = fieldNorm(doc=357)
    0.030047415 = weight(_text_:computer in 357) [ClassicSimilarity], result of:
      0.030047415 = score(doc=357,freq=8.0), product of:
        0.14883429 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.040726203 = queryNorm
        0.20188503 = fieldWeight in 357, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.01953125 = fieldNorm(doc=357)
  0.5 = coord(4/8)

Abstract

The main goal of this book is to transfer new research results from the fields of advanced computer sciences and information science to the design of new search engines. The readers will have a better idea of the new trends in applied research. The achievement of relevant, organized, sorted, and workable answers- to name but a few - from a search is becoming a daily need for enterprises and organizations, and, to a greater extent, for anyone. It does not consist of getting access to structural information as in standard databases; nor does it consist of searching information strictly by way of a combination of key words. It goes far beyond that. Whatever its modality, the information sought should be identified by the topics it contains, that is to say by its textual, audio, video or graphical contents. This is not a new issue. However, recent technological advances have completely changed the techniques being used. New Web technologies, the emergence of Intranet systems and the abundance of information on the Internet have created the need for efficient search and information access tools.
Recent technological progress in computer science, Web technologies, and constantly evolving information available on the Internet has drastically changed the landscape of search and access to information. Web search has significantly evolved in recent years. In the beginning, web search engines such as Google and Yahoo! were only providing search service over text documents. Aggregated search was one of the first steps to go beyond text search, and was the beginning of a new era for information seeking and retrieval. These days, new web search engines support aggregated search over a number of vertices, and blend different types of documents (e.g., images, videos) in their search results. New search engines employ advanced techniques involving machine learning, computational linguistics and psychology, user interaction and modeling, information visualization, Web engineering, artificial intelligence, distributed systems, social networks, statistical analysis, semantic analysis, and technologies over query sessions. Documents no longer exist on their own; they are connected to other documents, they are associated with users and their position in a social network, and they can be mapped onto a variety of ontologies. Similarly, retrieval tasks have become more interactive and are solidly embedded in a user's geospatial, social, and historical context. It is conjectured that new breakthroughs in information retrieval will not come from smarter algorithms that better exploit existing information sources, but from new retrieval algorithms that can intelligently use and combine new sources of contextual metadata.
With the rapid growth of web-based applications, such as search engines, Facebook, and Twitter, the development of effective and personalized information retrieval techniques and of user interfaces is essential. The amount of shared information and of social networks has also considerably grown, requiring metadata for new sources of information, like Wikipedia and ODP. These metadata have to provide classification information for a wide range of topics, as well as for social networking sites like Twitter, and Facebook, each of which provides additional preferences, tagging information and social contexts. Due to the explosion of social networks and other metadata sources, it is an opportune time to identify ways to exploit such metadata in IR tasks such as user modeling, query understanding, and personalization, to name a few. Although the use of traditional metadata such as html text, web page titles, and anchor text is fairly well-understood, the use of category information, user behavior data, and geographical information is just beginning to be studied. This book is intended for scientists and decision-makers who wish to gain working knowledge about search engines in order to evaluate available solutions and to dialogue with software and data providers.

Content

Enthält die Beiträge: Das, A., A. Jain: Indexing the World Wide Web: the journey so far. Ke, W.: Decentralized search and the clustering paradox in large scale information networks. Roux, M.: Metadata for search engines: what can be learned from e-Sciences? Fluhr, C.: Crosslingual access to photo databases. Djioua, B., J.-P. Desclés u. M. Alrahabi: Searching and mining with semantic categories. Ghorbel, H., A. Bahri u. R. Bouaziz: Fuzzy ontologies building platform for Semantic Web: FOB platform. Lassalle, E., E. Lassalle: Semantic models in information retrieval. Berry, M.W., R. Esau u. B. Kiefer: The use of text mining techniques in electronic discovery for legal matters. Sleem-Amer, M., I. Bigorgne u. S. Brizard u.a.: Intelligent semantic search engines for opinion and sentiment mining. Hoeber, O.: Human-centred Web search.
Vert, S.: Extensions of Web browsers useful to knowledge workers. Chen, L.-C.: Next generation search engine for the result clustering technology. Biskri, I., L. Rompré: Using association rules for query reformulation. Habernal, I., M. Konopík u. O. Rohlík: Question answering. Grau, B.: Finding answers to questions, in text collections or Web, in open domain or specialty domains. Berri, J., R. Benlamri: Context-aware mobile search engine. Bouidghaghen, O., L. Tamine: Spatio-temporal based personalization for mobile search. Chaudiron, S., M. Ihadjadene: Studying Web search engines from a user perspective: key concepts and main approaches. Karaman, F.: Artificial intelligence enabled search engines (AIESE) and the implications. Lewandowski, D.: A framework for evaluating the retrieval effectiveness of search engines.

LCSH

User interfaces (Computer systems)

Subject

User interfaces (Computer systems)

0.037865505 = product of:
  0.15146202 = sum of:
    0.05099215 = weight(_text_:computer in 2605) [ClassicSimilarity], result of:
      0.05099215 = score(doc=2605,freq=4.0), product of:
        0.14883429 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.040726203 = queryNorm
        0.34261024 = fieldWeight in 2605, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.046875 = fieldNorm(doc=2605)
    0.10046987 = product of:
      0.20093974 = sum of:
        0.20093974 = weight(_text_:programming in 2605) [ClassicSimilarity], result of:
          0.20093974 = score(doc=2605,freq=6.0), product of:
            0.26696935 = queryWeight, product of:
              6.5552235 = idf(docFreq=170, maxDocs=44218)
              0.040726203 = queryNorm
            0.7526697 = fieldWeight in 2605, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              6.5552235 = idf(docFreq=170, maxDocs=44218)
              0.046875 = fieldNorm(doc=2605)
      0.5 = coord(1/2)
  0.25 = coord(2/8)

Abstract

For introductory information retrieval courses at the undergraduate and graduate level in computer science, information science and computer engineering departments. Written by a leader in the field of information retrieval, Search Engines: Information Retrieval in Practice, is designed to give undergraduate students the understanding and tools they need to evaluate, compare and modify search engines. Coverage of the underlying IR and mathematical models reinforce key concepts. The book's numerous programming exercises make extensive use of Galago, a Java-based open source search engine. SUPPLEMENTS / Extensive lecture slides (in PDF and PPT format) / Solutions to selected end of chapter problems (Instructors only) / Test collections for exercises / Galago search engine

LCSH

Search engines / Programming

Subject

Search engines / Programming

0.033201035 = product of:
  0.08853609 = sum of:
    0.033238493 = weight(_text_:world in 1605) [ClassicSimilarity], result of:
      0.033238493 = score(doc=1605,freq=2.0), product of:
        0.15653811 = queryWeight, product of:
          3.8436708 = idf(docFreq=2573, maxDocs=44218)
          0.040726203 = queryNorm
        0.21233483 = fieldWeight in 1605, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.8436708 = idf(docFreq=2573, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1605)
    0.04150301 = weight(_text_:web in 1605) [ClassicSimilarity], result of:
      0.04150301 = score(doc=1605,freq=6.0), product of:
        0.13291039 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.040726203 = queryNorm
        0.3122631 = fieldWeight in 1605, 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=1605)
    0.013794596 = product of:
      0.027589193 = sum of:
        0.027589193 = weight(_text_:22 in 1605) [ClassicSimilarity], result of:
          0.027589193 = score(doc=1605,freq=2.0), product of:
            0.14261623 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.040726203 = queryNorm
            0.19345059 = fieldWeight in 1605, 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=1605)
      0.5 = coord(1/2)
  0.375 = coord(3/8)

Abstract

Numerous studies have explored the possibility of uncovering information from web search queries but few have examined the factors that affect web query data sources. We conducted a study that investigated this issue by comparing Google Trends and Baidu Index. Data from these two services are based on queries entered by users into Google and Baidu, two of the largest search engines in the world. We first compared the features and functions of the two services based on documents and extensive testing. We then carried out an empirical study that collected query volume data from the two sources. We found that data from both sources could be used to predict the quality of Chinese universities and companies. Despite the differences between the two services in terms of technology, such as differing methods of language processing, the search volume data from the two were highly correlated and combining the two data sources did not improve the predictive power of the data. However, there was a major difference between the two in terms of data availability. Baidu Index was able to provide more search volume data than Google Trends did. Our analysis showed that the disadvantage of Google Trends in this regard was due to Google's smaller user base in China. The implication of this finding goes beyond China. Google's user bases in many countries are smaller than that in China, so the search volume data related to those countries could result in the same issue as that related to China.

Source

Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22

0.030356273 = product of:
  0.12142509 = sum of:
    0.070432946 = weight(_text_:web in 478) [ClassicSimilarity], result of:
      0.070432946 = score(doc=478,freq=12.0), product of:
        0.13291039 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.040726203 = queryNorm
        0.5299281 = fieldWeight in 478, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=478)
    0.05099215 = weight(_text_:computer in 478) [ClassicSimilarity], result of:
      0.05099215 = score(doc=478,freq=4.0), product of:
        0.14883429 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.040726203 = queryNorm
        0.34261024 = fieldWeight in 478, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.046875 = fieldNorm(doc=478)
  0.25 = coord(2/8)

Abstract

"Web Search Engine Research", edited by Dirk Lewandowski, provides an understanding of Web search engines from the unique perspective of Library and Information Science. The book explores a range of topics including retrieval effectiveness, user satisfaction, the evaluation of search interfaces, the impact of search on society, reliability of search results, query log analysis, user guidance in the search process, and the influence of search engine optimization (SEO) on results quality. While research in computer science has mainly focused on technical aspects of search engines, LIS research is centred on users' behaviour when using search engines and how this interaction can be evaluated. LIS research provides a unique perspective in intermediating between the technical aspects, user aspects and their impact on their role in knowledge acquisition. This book is directly relevant to researchers and practitioners in library and information science, computer science, including Web researchers.

LCSH

Web search engines

Subject

Web search engines

0.030153304 = product of:
  0.08040881 = sum of:
    0.023720946 = weight(_text_:web in 2752) [ClassicSimilarity], result of:
      0.023720946 = score(doc=2752,freq=4.0), product of:
        0.13291039 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.040726203 = queryNorm
        0.17847323 = fieldWeight in 2752, 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=2752)
    0.04703164 = weight(_text_:computer in 2752) [ClassicSimilarity], result of:
      0.04703164 = score(doc=2752,freq=10.0), product of:
        0.14883429 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.040726203 = queryNorm
        0.31600004 = fieldWeight in 2752, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.02734375 = fieldNorm(doc=2752)
    0.009656217 = product of:
      0.019312434 = sum of:
        0.019312434 = weight(_text_:22 in 2752) [ClassicSimilarity], result of:
          0.019312434 = score(doc=2752,freq=2.0), product of:
            0.14261623 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.040726203 = queryNorm
            0.1354154 = fieldWeight in 2752, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.02734375 = fieldNorm(doc=2752)
      0.5 = coord(1/2)
  0.375 = coord(3/8)

Content

Inhalt: Acknowledgments; Abstract; Zusammenfassung; Contents; List of Figures; List of Tables; List of Acronyms; Chapter 1 Introduction ; 1.1 Research Questions; 1.2 Thesis Outline; Part I Fundamentals ; Chapter 2 Information Retrieval for Young Users ; 2.1 Basics of Information Retrieval; 2.1.1 Architecture of an IR System; 2.1.2 Relevance Ranking; 2.1.3 Search User Interfaces; 2.1.4 Targeted Search Engines; 2.2 Aspects of Child Development Relevant for Information Retrieval Tasks; 2.2.1 Human Cognitive Development; 2.2.2 Information Processing Theory; 2.2.3 Psychosocial Development 2.3 User Studies and Evaluation2.3.1 Methods in User Studies; 2.3.2 Types of Evaluation; 2.3.3 Evaluation with Children; 2.4 Discussion; Chapter 3 State of the Art ; 3.1 Children's Information-Seeking Behaviour; 3.1.1 Querying Behaviour; 3.1.2 Search Strategy; 3.1.3 Navigation Style; 3.1.4 User Interface; 3.1.5 Relevance Judgement; 3.2 Existing Algorithms and User Interface Concepts for Children; 3.2.1 Query; 3.2.2 Content; 3.2.3 Ranking; 3.2.4 Search Result Visualisation; 3.3 Existing Information Retrieval Systems for Children; 3.3.1 Digital Book Libraries; 3.3.2 Web Search Engines 3.4 Summary and DiscussionPart II Studying Open Issues ; Chapter 4 Usability of Existing Search Engines for Young Users ; 4.1 Assessment Criteria; 4.1.1 Criteria for Matching the Motor Skills; 4.1.2 Criteria for Matching the Cognitive Skills; 4.2 Results; 4.2.1 Conformance with Motor Skills; 4.2.2 Conformance with the Cognitive Skills; 4.2.3 Presentation of Search Results; 4.2.4 Browsing versus Searching; 4.2.5 Navigational Style; 4.3 Summary and Discussion; Chapter 5 Large-scale Analysis of Children's Queries and Search Interactions; 5.1 Dataset; 5.2 Results; 5.3 Summary and Discussion Chapter 6 Differences in Usability and Perception of Targeted Web Search Engines between Children and Adults 6.1 Related Work; 6.2 User Study; 6.3 Study Results; 6.4 Summary and Discussion; Part III Tackling the Challenges ; Chapter 7 Search User Interface Design for Children ; 7.1 Conceptual Challenges and Possible Solutions; 7.2 Knowledge Journey Design; 7.3 Evaluation; 7.3.1 Study Design; 7.3.2 Study Results; 7.4 Voice-Controlled Search: Initial Study; 7.4.1 User Study; 7.5 Summary and Discussion; Chapter 8 Addressing User Diversity ; 8.1 Evolving Search User Interface 8.1.1 Mapping Function8.1.2 Evolving Skills; 8.1.3 Detection of User Abilities; 8.1.4 Design Concepts; 8.2 Adaptation of a Search User Interface towards User Needs; 8.2.1 Design & Implementation; 8.2.2 Search Input; 8.2.3 Result Output; 8.2.4 General Properties; 8.2.5 Configuration and Further Details; 8.3 Evaluation; 8.3.1 Study Design; 8.3.2 Study Results; 8.3.3 Preferred UI Settings; 8.3.4 User satisfaction; 8.4 Knowledge Journey Exhibit; 8.4.1 Hardware; 8.4.2 Frontend; 8.4.3 Backend; 8.5 Summary and Discussion; Chapter 9 Supporting Visual Searchers in Processing Search Results 9.1 Related Work

Date

1. 2.2016 18:25:22

LCSH

Computer science
User interfaces (Computer systems)

Series

Study in computer science and media design

Subject

Computer science
User interfaces (Computer systems)

0.029324364 = product of:
  0.117297456 = sum of:
    0.053001273 = weight(_text_:wide in 104) [ClassicSimilarity], result of:
      0.053001273 = score(doc=104,freq=2.0), product of:
        0.18044797 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.040726203 = queryNorm
        0.29372054 = fieldWeight in 104, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.046875 = fieldNorm(doc=104)
    0.064296186 = weight(_text_:web in 104) [ClassicSimilarity], result of:
      0.064296186 = score(doc=104,freq=10.0), product of:
        0.13291039 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.040726203 = queryNorm
        0.48375595 = fieldWeight in 104, 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=104)
  0.25 = coord(2/8)

Abstract

Exploiting context information in a web search engine helps fine-tuning web services and applications to deliver custom-made information to end users. While context, including user and environment information, cannot be exploited efficiently in the wired Internet interaction type, it is becoming accessible with the mobile web where users have an intimate relationship with their handsets. In this type of interaction, context plays a significant role enhancing information search and therefore, allowing a search engine to detect relevant content in all digital forms and formats. This chapter proposes a context model and an architecture that promote integration of context information for individuals and social communities to add value to their interaction with the mobile web. The architecture relies on efficient knowledge management of multimedia resources for a wide range of applications and web services. The research is illustrated with a corporate case study showing how efficient context integration improves usability of a mobile search engine.

0.029013261 = product of:
  0.116053045 = sum of:
    0.04416773 = weight(_text_:wide in 1210) [ClassicSimilarity], result of:
      0.04416773 = score(doc=1210,freq=2.0), product of:
        0.18044797 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.040726203 = queryNorm
        0.24476713 = fieldWeight in 1210, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1210)
    0.07188532 = weight(_text_:web in 1210) [ClassicSimilarity], result of:
      0.07188532 = score(doc=1210,freq=18.0), product of:
        0.13291039 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.040726203 = queryNorm
        0.5408555 = fieldWeight in 1210, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1210)
  0.25 = coord(2/8)

Abstract

The current web consists of documents that are highly heterogeneous and hard for machines to understand. The Semantic Web is a progressive movement of the Word Wide Web, aiming at converting the current web of unstructured documents to the web of data. In the Semantic Web, web documents are annotated with metadata using standardized ontology language. These annotated documents are directly processable by machines and it highly improves their usability and usefulness. In Ericsson, similar problems occur. There are massive documents being created with well-defined structures. Though these documents are about domain specific knowledge and can have rich relations, they are currently managed by a traditional search engine, which ignores the rich domain specific information and presents few data to users. Motivated by the Semantic Web, we aim to find standard ways to process these documents, extract rich domain specific information and annotate these data to documents with formal markup languages. We propose this project to develop a domain specific search engine for processing different documents and building explicit relations for them. This research project consists of the three main focuses: examining different domain specific documents and finding ways to extract their metadata; integrating a text search engine with an ontology server; exploring novel ways to build relations for documents. We implement this system and demonstrate its functions. As a prototype, the system provides required features and will be extended in the future.

Theme

Semantic Web

0.022880107 = product of:
  0.09152043 = sum of:
    0.05318159 = weight(_text_:world in 1700) [ClassicSimilarity], result of:
      0.05318159 = score(doc=1700,freq=2.0), product of:
        0.15653811 = queryWeight, product of:
          3.8436708 = idf(docFreq=2573, maxDocs=44218)
          0.040726203 = queryNorm
        0.33973572 = fieldWeight in 1700, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.8436708 = idf(docFreq=2573, maxDocs=44218)
          0.0625 = fieldNorm(doc=1700)
    0.038338836 = weight(_text_:web in 1700) [ClassicSimilarity], result of:
      0.038338836 = score(doc=1700,freq=2.0), product of:
        0.13291039 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.040726203 = queryNorm
        0.2884563 = fieldWeight in 1700, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0625 = fieldNorm(doc=1700)
  0.25 = coord(2/8)

Abstract

The search giant is automatically building Knowledge Vault, a massive database that could give us unprecedented access to the world's facts GOOGLE is building the largest store of knowledge in human history - and it's doing so without any human help. Instead, Knowledge Vault autonomously gathers and merges information from across the web into a single base of facts about the world, and the people and objects in it.

0.022295482 = product of:
  0.05945462 = sum of:
    0.016619246 = weight(_text_:world in 6) [ClassicSimilarity], result of:
      0.016619246 = score(doc=6,freq=2.0), product of:
        0.15653811 = queryWeight, product of:
          3.8436708 = idf(docFreq=2573, maxDocs=44218)
          0.040726203 = queryNorm
        0.10616741 = fieldWeight in 6, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.8436708 = idf(docFreq=2573, maxDocs=44218)
          0.01953125 = fieldNorm(doc=6)
    0.022083865 = weight(_text_:wide in 6) [ClassicSimilarity], result of:
      0.022083865 = score(doc=6,freq=2.0), product of:
        0.18044797 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.040726203 = queryNorm
        0.122383565 = fieldWeight in 6, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.01953125 = fieldNorm(doc=6)
    0.020751504 = weight(_text_:web in 6) [ClassicSimilarity], result of:
      0.020751504 = score(doc=6,freq=6.0), product of:
        0.13291039 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.040726203 = queryNorm
        0.15613155 = fieldWeight in 6, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.01953125 = fieldNorm(doc=6)
  0.375 = coord(3/8)

Content

"Ganze "230 Adressen im World Wide Web, 250 Server und 100 per E-Mail zugängliche Informationsquellen". So stand es 1995 in der "Updated Internet Services List". Die manuell zusammengetragene Adressliste, nach ihrem Urheber auch als "Yanoff-Liste" bekannt, war einer der ersten Versuche, die anschwellende Informationsflut des Internet zu kanalisieren. Aus einem dieser Verzeichnisse, das zunächst von Studenten zusammengetragen wurde, entstand kurze Zeit später mit Yahoo die Mutter aller Suchmaschinen. Die englische Wortkombination "Search Engine" ist allerdings irreführend. Denn dahinter steckt in Wahrheit eine Software, die automatisch einen Index der Internetinhalte erstellt. Denn der Fleiß einer Handvoll Studenten reichte schon bald nicht mehr aus, das sich explosionsartig ausbreitende Web auch nur ansatzweise zu erfassen.
Und natürlich schlägt Microsoft in die gleiche Kerbe wie Google. Denn auch die Bing-Suche steht ganz im Zeichen der "Individualisierung" der Ergebnisse. "Social Search" nennt sich die (noch) optionale Möglichkeit, Facebook-Einträge von Freunden als Suchkriterien zu verwenden. Schließlich, so die Meinung des Konzerns, beeinflusse der "Freunde-Effekt" die Entscheidung von Menschen in der Regel mehr als andere Faktoren. Die Entwicklung zeigt eindrucksvoll, wie schnell sich die beiden letzten Großen im Suchmaschinen-Geschäft neue Entwicklungen im Netz aneignen. Im Web 2.0 bildeten Blogs und Soziale Netzwerke bislang gewissermaßen ein demokratisches Gegengewicht zum Meinungsmonopol. Doch auch hier ist der Internet-Goliath bereits am Start. Wer sich schon immer mal gefragt hat, warum der Such-Gigant praktisch monatlich mit neuen Angeboten wie etwa Google+ aufwartet, findet hier eine Antwort. Mit dem kostenlosen Smartphone-Betriebssystem Android sicherte man sich eine gewichtige Position auf dem expandieren Markt für mobile Plattformen. Trotz ihrer momentanen Allmacht erkennen die Konzernlenker also durchaus die Gefahr, irgendwann einmal selbst vom Zug der Zeit überrollt zu werden. Für die meisten Konkurrenten kommt diese Einsicht zu spät."

0.021704663 = product of:
  0.08681865 = sum of:
    0.033238493 = weight(_text_:world in 3188) [ClassicSimilarity], result of:
      0.033238493 = score(doc=3188,freq=2.0), product of:
        0.15653811 = queryWeight, product of:
          3.8436708 = idf(docFreq=2573, maxDocs=44218)
          0.040726203 = queryNorm
        0.21233483 = fieldWeight in 3188, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.8436708 = idf(docFreq=2573, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3188)
    0.053580154 = weight(_text_:web in 3188) [ClassicSimilarity], result of:
      0.053580154 = score(doc=3188,freq=10.0), product of:
        0.13291039 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.040726203 = queryNorm
        0.40312994 = fieldWeight in 3188, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3188)
  0.25 = coord(2/8)

Abstract

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

0.020392181 = product of:
  0.081568725 = sum of:
    0.06777413 = weight(_text_:web in 1627) [ClassicSimilarity], result of:
      0.06777413 = score(doc=1627,freq=16.0), product of:
        0.13291039 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.040726203 = queryNorm
        0.5099235 = fieldWeight in 1627, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1627)
    0.013794596 = product of:
      0.027589193 = sum of:
        0.027589193 = weight(_text_:22 in 1627) [ClassicSimilarity], result of:
          0.027589193 = score(doc=1627,freq=2.0), product of:
            0.14261623 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.040726203 = queryNorm
            0.19345059 = fieldWeight in 1627, 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=1627)
      0.5 = coord(1/2)
  0.25 = coord(2/8)

Abstract

Purpose The purpose of this paper is to design a watcher-based crawler (WBC) that has the ability of crawling static and dynamic web sites, and can download only the updated and newly added web pages. Design/methodology/approach In the proposed WBC crawler, a watcher file, which can be uploaded to the web sites servers, prepares a report that contains the addresses of the updated and the newly added web pages. In addition, the WBC is split into five units, where each unit is responsible for performing a specific crawling process. Findings Several experiments have been conducted and it has been observed that the proposed WBC increases the number of uniquely visited static and dynamic web sites as compared with the existing crawling techniques. In addition, the proposed watcher file not only allows the crawlers to visit the updated and newly web pages, but also solves the crawlers overlapping and communication problems. Originality/value The proposed WBC performs all crawling processes in the sense that it detects all updated and newly added pages automatically without any human explicit intervention or downloading the entire web sites.

Date

20. 1.2015 18:30:22

0.017887605 = product of:
  0.07155042 = sum of:
    0.04150301 = weight(_text_:web in 2696) [ClassicSimilarity], result of:
      0.04150301 = score(doc=2696,freq=6.0), product of:
        0.13291039 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.040726203 = queryNorm
        0.3122631 = fieldWeight in 2696, 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=2696)
    0.030047415 = weight(_text_:computer in 2696) [ClassicSimilarity], result of:
      0.030047415 = score(doc=2696,freq=2.0), product of:
        0.14883429 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.040726203 = queryNorm
        0.20188503 = fieldWeight in 2696, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2696)
  0.25 = coord(2/8)

Abstract

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

Source

International journal of computer applications. 129(2015) no.5, S12-15

0.017388698 = product of:
  0.06955479 = sum of:
    0.053001273 = weight(_text_:wide in 105) [ClassicSimilarity], result of:
      0.053001273 = score(doc=105,freq=2.0), product of:
        0.18044797 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.040726203 = queryNorm
        0.29372054 = fieldWeight in 105, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.046875 = fieldNorm(doc=105)
    0.016553516 = product of:
      0.03310703 = sum of:
        0.03310703 = weight(_text_:22 in 105) [ClassicSimilarity], result of:
          0.03310703 = score(doc=105,freq=2.0), product of:
            0.14261623 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.040726203 = queryNorm
            0.23214069 = fieldWeight in 105, 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=105)
      0.5 = coord(1/2)
  0.25 = coord(2/8)

Abstract

Result clustering has recently attracted a lot of attention to provide the users with a succinct overview of relevant search results than traditional search engines. This chapter proposes a mixed clustering method to organize all returned search results into a hierarchical tree structure. The clustering method accomplishes two main tasks, one is label construction and the other is tree building. This chapter uses precision to measure the quality of clustering results. According to the results of experiments, the author preliminarily concluded that the performance of the system is better than many other well-known commercial and academic systems. This chapter makes several contributions. First, it presents a high performance system based on the clustering method. Second, it develops a divisive hierarchical clustering algorithm to organize all returned snippets into hierarchical tree structure. Third, it performs a wide range of experimental analyses to show that almost all commercial systems are significantly better than most current academic systems.

Date

17. 4.2012 15:22:11

0.016843688 = product of:
  0.06737475 = sum of:
    0.053580154 = weight(_text_:web in 109) [ClassicSimilarity], result of:
      0.053580154 = score(doc=109,freq=10.0), product of:
        0.13291039 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.040726203 = queryNorm
        0.40312994 = fieldWeight in 109, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=109)
    0.013794596 = product of:
      0.027589193 = sum of:
        0.027589193 = weight(_text_:22 in 109) [ClassicSimilarity], result of:
          0.027589193 = score(doc=109,freq=2.0), product of:
            0.14261623 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.040726203 = queryNorm
            0.19345059 = fieldWeight in 109, 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=109)
      0.5 = coord(1/2)
  0.25 = coord(2/8)

Abstract

This chapter shows that the wider use of Web search engines, reconsidering the theoretical and methodological frameworks to grasp new information practices. Beginning with an overview of the recent challenges implied by the dynamic nature of the Web, this chapter then traces the information behavior related concepts in order to present the different approaches from the user perspective. The authors pay special attention to the concept of "information practice" and other related concepts such as "use", "activity", and "behavior" largely used in the literature but not always strictly defined. The authors provide an overview of user-oriented studies that are meaningful to understand the different contexts of use of electronic information access systems, focusing on five approaches: the system-oriented approaches, the theories of information seeking, the cognitive and psychological approaches, the management science approaches, and the marketing approaches. Future directions of work are then shaped, including social searching and the ethical, cultural, and political dimensions of Web search engines. The authors conclude considering the importance of Critical theory to better understand the role of Web Search engines in our modern society.

Date

20. 4.2012 13:22:37

0.01598362 = product of:
  0.06393448 = sum of:
    0.033887066 = weight(_text_:web in 3325) [ClassicSimilarity], result of:
      0.033887066 = score(doc=3325,freq=4.0), product of:
        0.13291039 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.040726203 = queryNorm
        0.25496176 = fieldWeight in 3325, 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=3325)
    0.030047415 = weight(_text_:computer in 3325) [ClassicSimilarity], result of:
      0.030047415 = score(doc=3325,freq=2.0), product of:
        0.14883429 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.040726203 = queryNorm
        0.20188503 = fieldWeight in 3325, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3325)
  0.25 = coord(2/8)

Abstract

It is important for education in computer science and information systems to keep up to date with the latest development in technology. With the rapid development of the Internet and the Web, many schools have included Internet-related technologies, such as Web search engines and e-commerce, as part of their curricula. Previous research has shown that it is effective to use search engine development tools to facilitate students' learning. However, the effectiveness of these tools in the classroom has not been evaluated. In this article, we review the design of three search engine development tools, SpidersRUs, Greenstone, and Alkaline, followed by an evaluation study that compared the three tools in the classroom. In the study, 33 students were divided into 13 groups and each group used the three tools to develop three independent search engines in a class project. Our evaluation results showed that SpidersRUs performed better than the two other tools in overall satisfaction and the level of knowledge gained in their learning experience when using the tools for a class project on Internet applications development.

0.015102548 = product of:
  0.06041019 = sum of:
    0.038338836 = weight(_text_:web in 344) [ClassicSimilarity], result of:
      0.038338836 = score(doc=344,freq=2.0), product of:
        0.13291039 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.040726203 = queryNorm
        0.2884563 = fieldWeight in 344, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0625 = fieldNorm(doc=344)
    0.022071354 = product of:
      0.04414271 = sum of:
        0.04414271 = weight(_text_:22 in 344) [ClassicSimilarity], result of:
          0.04414271 = score(doc=344,freq=2.0), product of:
            0.14261623 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.040726203 = queryNorm
            0.30952093 = fieldWeight in 344, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=344)
      0.5 = coord(1/2)
  0.25 = coord(2/8)

Date

18. 9.2018 18:22:18

Source

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

0.013824401 = product of:
  0.055297606 = sum of:
    0.04150301 = weight(_text_:web in 5493) [ClassicSimilarity], result of:
      0.04150301 = score(doc=5493,freq=6.0), product of:
        0.13291039 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.040726203 = queryNorm
        0.3122631 = fieldWeight in 5493, 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=5493)
    0.013794596 = product of:
      0.027589193 = sum of:
        0.027589193 = weight(_text_:22 in 5493) [ClassicSimilarity], result of:
          0.027589193 = score(doc=5493,freq=2.0), product of:
            0.14261623 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.040726203 = queryNorm
            0.19345059 = fieldWeight in 5493, 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=5493)
      0.5 = coord(1/2)
  0.25 = coord(2/8)

Abstract

Purpose Web search is more and more moving into mobile contexts. However, screen size of mobile devices is limited and search engine result pages face a trade-off between offering informative snippets and optimal use of space. One factor clearly influencing this trade-off is snippet length. The purpose of this paper is to find out what snippet size to use in mobile web search. Design/methodology/approach For this purpose, an eye-tracking experiment was conducted showing participants search interfaces with snippets of one, three or five lines on a mobile device to analyze 17 dependent variables. In total, 31 participants took part in the study. Each of the participants solved informational and navigational tasks. Findings Results indicate a strong influence of page fold on scrolling behavior and attention distribution across search results. Regardless of query type, short snippets seem to provide too little information about the result, so that search performance and subjective measures are negatively affected. Long snippets of five lines lead to better performance than medium snippets for navigational queries, but to worse performance for informational queries. Originality/value Although space in mobile search is limited, this study shows that longer snippets improve usability and user experience. It further emphasizes that page fold plays a stronger role in mobile than in desktop search for attention distribution.

Date

20. 1.2015 18:30:22

0.010960503 = product of:
  0.04384201 = sum of:
    0.030047415 = weight(_text_:computer in 3815) [ClassicSimilarity], result of:
      0.030047415 = score(doc=3815,freq=2.0), product of:
        0.14883429 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.040726203 = queryNorm
        0.20188503 = fieldWeight in 3815, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3815)
    0.013794596 = product of:
      0.027589193 = sum of:
        0.027589193 = weight(_text_:22 in 3815) [ClassicSimilarity], result of:
          0.027589193 = score(doc=3815,freq=2.0), product of:
            0.14261623 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.040726203 = queryNorm
            0.19345059 = fieldWeight in 3815, 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=3815)
      0.5 = coord(1/2)
  0.25 = coord(2/8)

Content

"Bei der Suche nach Politikern und Parteien über Suchmaschinen wie Google spielt Personalisierung einem Forschungsprojekt zufolge eine geringere Rolle als bisher angenommen. Bei der Eingabe von Politikernamen erhalten verschiedene Nutzer größtenteils die gleichen Ergebnisse angezeigt, lautet ein gestern veröffentlichtes Zwischenergebnis einer Analyse im Auftrag der Landesmedienanstalten. Die Ergebnisse stammen aus dem Forschungsprojekt "#Datenspende: Google und die Bundestagswahl2017" der Initiative AIgorithmWatch und der Technischen Universität Kaiserslautern. Im Durchschnitt erhalten zwei unterschiedliche Nutzer demnach bei insgesamt neun Suchergebnissen sieben bis acht identische Treffer, wenn sie mit Google nach Spitzenkandidaten der Parteien im Bundestagswahlkampf suchen. Die Suchergebnisse zu Parteien unterscheiden sich allerdings stärker. Bei neun Suchanfragen gebe es hier nur fünf bis sechs gemeinsame Suchergebnisse, fanden die Wissenschaftler heraus. Die Informatikprofessorin Katharina Zweig von der TU Kaiserslautern zeigte sich überrascht, dass die Suchergebisse verschiedener Nutzer sich so wenig unterscheiden. "Das könnte allerdings morgen schon wieder anders aussehen", warnte sie, Die Studie beweise erstmals, dass es grundsätzlich möglich sei, Algorithmen von Intermediären wie Suchmaschinen im Verdachtsfall nachvollziehbar zu machen. Den Ergebnissen zufolge gibt es immer wieder kleine Nutzergruppen mit stark abweichenden Ergebnislisten. Eine abschließende, inhaltliche Bewertung stehe noch aus. Für das Projekt haben nach Angaben der Medienanstalt bisher fast 4000 freiwillige Nutzer ein von den Forschern programmiertes Plug-ln auf ihrem Computer- installiert. Bisher seien damitdrei Millionen gespendete Datensätze gespeichert worden. Das Projekt wird finanziert von den Landesmedienanstalten Bayern, Berlin-Brandenburg, Hessen, Rheinland-Pfalz, Saarland und Sachsen." Vgl. auch: https://www.swr.de/swraktuell/rp/kaiserslautern/forschung-in-kaiserslautern-beeinflusst-google-die-bundestagswahl/-/id=1632/did=20110680/nid=1632/1mohmie/index.html. https://www.uni-kl.de/aktuelles/news/news/detail/News/aufruf-zur-datenspende-welche-nachrichten-zeigt-die-suchmaschine-google-zur-bundestagswahl-an/.

Date

22. 7.2004 9:42:33

0.010375752 = product of:
  0.08300602 = sum of:
    0.08300602 = weight(_text_:web in 438) [ClassicSimilarity], result of:
      0.08300602 = score(doc=438,freq=24.0), product of:
        0.13291039 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.040726203 = queryNorm
        0.6245262 = fieldWeight in 438, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=438)
  0.125 = coord(1/8)

Abstract

In this paper, we discuss the architecture and implementation of the Semantic Web Search Engine (SWSE). Following traditional search engine architecture, SWSE consists of crawling, data enhancing, indexing and a user interface for search, browsing and retrieval of information; unlike traditional search engines, SWSE operates over RDF Web data - loosely also known as Linked Data - which implies unique challenges for the system design, architecture, algorithms, implementation and user interface. In particular, many challenges exist in adopting Semantic Web technologies for Web data: the unique challenges of the Web - in terms of scale, unreliability, inconsistency and noise - are largely overlooked by the current Semantic Web standards. Herein, we describe the current SWSE system, initially detailing the architecture and later elaborating upon the function, design, implementation and performance of each individual component. In so doing, we also give an insight into how current Semantic Web standards can be tailored, in a best-effort manner, for use on Web data. Throughout, we offer evaluation and complementary argumentation to support our design choices, and also offer discussion on future directions and open research questions. Later, we also provide candid discussion relating to the difficulties currently faced in bringing such a search engine into the mainstream, and lessons learnt from roughly six years working on the Semantic Web Search Engine project.

Object

Semantic Web Search Engine

Theme

Semantic Web