Search (6 results, page 1 of 1)

0.021451002 = product of:
  0.064353004 = sum of:
    0.064353004 = product of:
      0.12870601 = sum of:
        0.12870601 = weight(_text_:demand in 2236) [ClassicSimilarity], result of:
          0.12870601 = score(doc=2236,freq=2.0), product of:
            0.31127608 = queryWeight, product of:
              6.237302 = idf(docFreq=234, maxDocs=44218)
              0.04990557 = queryNorm
            0.4134786 = fieldWeight in 2236, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              6.237302 = idf(docFreq=234, maxDocs=44218)
              0.046875 = fieldNorm(doc=2236)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

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

0.013066944 = product of:
  0.03920083 = sum of:
    0.03920083 = weight(_text_:on in 2799) [ClassicSimilarity], result of:
      0.03920083 = score(doc=2799,freq=12.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.35714048 = fieldWeight in 2799, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.046875 = fieldNorm(doc=2799)
  0.33333334 = coord(1/3)

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.

0.008890929 = product of:
  0.026672786 = sum of:
    0.026672786 = weight(_text_:on in 2696) [ClassicSimilarity], result of:
      0.026672786 = score(doc=2696,freq=8.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.24300331 = fieldWeight in 2696, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2696)
  0.33333334 = coord(1/3)

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.

0.0053345575 = product of:
  0.016003672 = sum of:
    0.016003672 = weight(_text_:on in 5698) [ClassicSimilarity], result of:
      0.016003672 = score(doc=5698,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.14580199 = fieldWeight in 5698, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.046875 = fieldNorm(doc=5698)
  0.33333334 = coord(1/3)

Abstract

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

0.0053345575 = product of:
  0.016003672 = sum of:
    0.016003672 = weight(_text_:on in 5700) [ClassicSimilarity], result of:
      0.016003672 = score(doc=5700,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.14580199 = fieldWeight in 5700, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.046875 = fieldNorm(doc=5700)
  0.33333334 = coord(1/3)

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

0.0045076776 = product of:
  0.013523032 = sum of:
    0.013523032 = product of:
      0.027046064 = sum of:
        0.027046064 = weight(_text_:22 in 3447) [ClassicSimilarity], result of:
          0.027046064 = score(doc=3447,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.15476047 = fieldWeight in 3447, 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=3447)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Date

22. 2.2017 10:16:49