Search (5 results, page 1 of 1)

0.018822905 = product of:
  0.09411452 = sum of:
    0.09411452 = weight(_text_:index in 231) [ClassicSimilarity], result of:
      0.09411452 = score(doc=231,freq=6.0), product of:
        0.2250935 = queryWeight, product of:
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.051511593 = queryNorm
        0.418113 = fieldWeight in 231, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.0390625 = fieldNorm(doc=231)
  0.2 = coord(1/5)

Abstract

As an extension to the current Web, Semantic Web will not only contain structured data with machine understandable semantics but also textual information. While structured queries can be used to find information more precisely on the Semantic Web, keyword searches are still needed to help exploit textual information. It thus becomes very important that we can combine precise structured queries with imprecise keyword searches to have a hybrid query capability. In addition, due to the huge volume of information on the Semantic Web, the hybrid query must be processed in a very scalable way. In this paper, we define such a hybrid query capability that combines unary tree-shaped structured queries with keyword searches. We show how existing information retrieval (IR) index structures and functions can be reused to index semantic web data and its textual information, and how the hybrid query is evaluated on the index structure using IR engines in an efficient and scalable manner. We implemented this IR approach in an engine called Semplore. Comprehensive experiments on its performance show that it is a promising approach. It leads us to believe that it may be possible to evolve current web search engines to query and search the Semantic Web. Finally, we briefy describe how Semplore is used for searching Wikipedia and an IBM customer's product information.

0.013040888 = product of:
  0.06520444 = sum of:
    0.06520444 = weight(_text_:index in 1963) [ClassicSimilarity], result of:
      0.06520444 = score(doc=1963,freq=2.0), product of:
        0.2250935 = queryWeight, product of:
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.051511593 = queryNorm
        0.28967714 = fieldWeight in 1963, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.046875 = fieldNorm(doc=1963)
  0.2 = coord(1/5)

Abstract

Index modeling and computer simulation techniques are used to examine the influence of indexing frequency distributions, indexing exhaustivity distributions, and three weighting methods on hypothetical document spaces in a vector-based information retrieval (IR) system. The way documents are indexed plays an important role in retrieval. The authors demonstrate the influence of different indexing characteristics on document space density (DSD) changes and document space discriminative capacity for IR. Document environments that contain a relatively higher percentage of infrequently occurring terms provide lower density outcomes than do environments where a higher percentage of frequently occurring terms exists. Different indexing exhaustivity levels, however, have little influence on the document space densities. A weighting algorithm that favors higher weights for infrequently occurring terms results in the lowest overall document space densities, which allows documents to be more readily differentiated from one another. This in turn can positively influence IR. The authors also discuss the influence on outcomes using two methods of normalization of term weights (i.e., means and ranges) for the different weighting methods.

0.010867408 = product of:
  0.054337036 = sum of:
    0.054337036 = weight(_text_:index in 5238) [ClassicSimilarity], result of:
      0.054337036 = score(doc=5238,freq=2.0), product of:
        0.2250935 = queryWeight, product of:
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.051511593 = queryNorm
        0.24139762 = fieldWeight in 5238, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5238)
  0.2 = coord(1/5)

Abstract

Wolfram and Zhang are interested in the effect of different indexing exhaustivity, by which they mean the number of terms chosen, and of different index term distributions and different term weighting methods on the resulting document cluster organization. The Distance Angle Retrieval Environment, DARE, which provides a two dimensional display of retrieved documents was used to represent the document clusters based upon a document's distance from the searcher's main interest, and on the angle formed by the document, a point representing a minor interest, and the point representing the main interest. If the centroid and the origin of the document space are assigned as major and minor points the average distance between documents and the centroid can be measured providing an indication of cluster organization. in the form of a size normalized similarity measure. Using 500 records from NTIS and nine models created by intersecting low, observed, and high exhaustivity levels (based upon a negative binomial distribution) with shallow, observed, and steep term distributions (based upon a Zipf distribution) simulation runs were preformed using inverse document frequency, inter-document term frequency, and inverse document frequency based upon both inter and intra-document frequencies. Low exhaustivity and shallow distributions result in a more dense document space and less effective retrieval. High exhaustivity and steeper distributions result in a more diffuse space.

0.00697911 = product of:
  0.03489555 = sum of:
    0.03489555 = weight(_text_:22 in 1778) [ClassicSimilarity], result of:
      0.03489555 = score(doc=1778,freq=2.0), product of:
        0.18038483 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.051511593 = queryNorm
        0.19345059 = fieldWeight in 1778, 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=1778)
  0.2 = coord(1/5)

Date

8. 4.2015 16:22:13

0.005433704 = product of:
  0.027168518 = sum of:
    0.027168518 = weight(_text_:index in 1211) [ClassicSimilarity], result of:
      0.027168518 = score(doc=1211,freq=2.0), product of:
        0.2250935 = queryWeight, product of:
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.051511593 = queryNorm
        0.12069881 = fieldWeight in 1211, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.01953125 = fieldNorm(doc=1211)
  0.2 = coord(1/5)

Abstract

From the user's perspective, however, it is still difficult to use current information retrieval systems. Users frequently have problems expressing their information needs and translating those needs into queries. This is partly due to the fact that information needs cannot be expressed appropriately in systems terms. It is not unusual for users to input search terms that are different from the index terms information systems use. Various methods have been proposed to help users choose search terms and articulate queries. One widely used approach is to incorporate into the information system a thesaurus-like component that represents both the important concepts in a particular subject area and the semantic relationships among those concepts. Unfortunately, the development and use of thesauri is not without its own problems. The thesaurus employed in a specific information system has often been developed for a general subject area and needs significant enhancement to be tailored to the information system where it is to be used. This thesaurus development process, if done manually, is both time consuming and labor intensive. Usage of a thesaurus in searching is complex and may raise barriers for the user. For illustration purposes, let us consider two scenarios of thesaurus usage. In the first scenario the user inputs a search term and the thesaurus then displays a matching set of related terms. Without an overview of the thesaurus - and without the ability to see the matching terms in the context of other terms - it may be difficult to assess the quality of the related terms in order to select the correct term. In the second scenario the user browses the whole thesaurus, which is organized as in an alphabetically ordered list. The problem with this approach is that the list may be long, and neither does it show users the global semantic relationship among all the listed terms.