Search (8 results, page 1 of 1)

  • × theme_ss:"Semantisches Umfeld in Indexierung u. Retrieval"
  • × theme_ss:"Suchmaschinen"
  • × type_ss:"a"
  1. Schwartz, C.: Web search engines (1998) 0.02 
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    Abstract
    This reviews looks briefly at the history of WWW search engine development, considers the current state of affairs, and reflects on the future. Networked discovery tools have evolved along with Internet resource availability. WWW search engines display some complexity in their variety, content, resource acquisition strategies, and in the array of tools the deploy to assist users. A small but growing body of evaluation literature, much of it not systematic in nature, indicates that performance effectiveness is difficult to assess in this setting. Significant improvements in general-content search engine retrieval and ranking performance may not be possible, and are probalby not worth the effort, although search engine providers have introduced some rudimentary attempts at personalization, summarization, and query expansion. The shift to distributed search across multitype database systems could extend general networked discovery and retrieval to include smaller resource collections with rich metadata and navigation tools
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  2. Bhansali, D.; Desai, H.; Deulkar, K.: ¬A study of different ranking approaches for semantic search (2015) 0.01 
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    Abstract
    Search Engines have become an integral part of our day to day life. Our reliance on search engines increases with every passing day. With the amount of data available on Internet increasing exponentially, it becomes important to develop new methods and tools that help to return results relevant to the queries and reduce the time spent on searching. The results should be diverse but at the same time should return results focused on the queries asked. Relation Based Page Rank [4] algorithms are considered to be the next frontier in improvement of Semantic Web Search. The probability of finding relevance in the search results as posited by the user while entering the query is used to measure the relevance. However, its application is limited by the complexity of determining relation between the terms and assigning explicit meaning to each term. Trust Rank is one of the most widely used ranking algorithms for semantic web search. Few other ranking algorithms like HITS algorithm, PageRank algorithm are also used for Semantic Web Searching. In this paper, we will provide a comparison of few ranking approaches.
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  3. Poynder, R.: Web research engines? (1996) 0.01 
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    Theme
    Verbale Doksprachen im Online-Retrieval
    Klassifikationssysteme im Online-Retrieval
    Semantisches Umfeld in Indexierung u. Retrieval
  4. Jindal, V.; Bawa, S.; Batra, S.: ¬A review of ranking approaches for semantic search on Web (2014) 0.01 
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    Abstract
    With ever increasing information being available to the end users, search engines have become the most powerful tools for obtaining useful information scattered on the Web. However, it is very common that even most renowned search engines return result sets with not so useful pages to the user. Research on semantic search aims to improve traditional information search and retrieval methods where the basic relevance criteria rely primarily on the presence of query keywords within the returned pages. This work is an attempt to explore different relevancy ranking approaches based on semantics which are considered appropriate for the retrieval of relevant information. In this paper, various pilot projects and their corresponding outcomes have been investigated based on methodologies adopted and their most distinctive characteristics towards ranking. An overview of selected approaches and their comparison by means of the classification criteria has been presented. With the help of this comparison, some common concepts and outstanding features have been identified.
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  5. Scholer, F.; Williams, H.E.; Turpin, A.: Query association surrogates for Web search (2004) 0.00 
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    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  6. Pahlevi, S.M.; Kitagawa, H.: Conveying taxonomy context for topic-focused Web search (2005) 0.00 
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    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  7. Vidinli, I.B.; Ozcan, R.: New query suggestion framework and algorithms : a case study for an educational search engine (2016) 0.00 
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    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  8. Roy, R.S.; Agarwal, S.; Ganguly, N.; Choudhury, M.: Syntactic complexity of Web search queries through the lenses of language models, networks and users (2016) 0.00 
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    Theme
    Semantisches Umfeld in Indexierung u. Retrieval

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