Search (11 results, page 1 of 1)

0.0029000505 = product of:
  0.005800101 = sum of:
    0.005800101 = product of:
      0.011600202 = sum of:
        0.011600202 = weight(_text_:a in 2385) [ClassicSimilarity], result of:
          0.011600202 = score(doc=2385,freq=12.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.21843673 = fieldWeight in 2385, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2385)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

LyberWorld is a prototype IR user interface. It implements visualizations of an abstract information space: fulltext. The video demonstrates a visual user interface for the probabilistic fulltext retrieval system INQUERY. Visualizations are used to communicate information search and browsing activities in a natural way by applying metaphors of spatial navigation in abstract information spaces. Visualization tools for exploring information spaces and judging relevance of information items are introduced and an example session demonstrates the prototype. The presence of a spatial model in the user's mind is regarded as an essential contribution towards natural interaction and reduction of cognitive costs during retrieval dialogues.

Type

a

0.0028703054 = product of:
  0.005740611 = sum of:
    0.005740611 = product of:
      0.011481222 = sum of:
        0.011481222 = weight(_text_:a in 2384) [ClassicSimilarity], result of:
          0.011481222 = score(doc=2384,freq=16.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.2161963 = fieldWeight in 2384, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=2384)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

LyberWorld is a prototype IR user interface. It implements visualizations of an abstract information space-fulltext. The paper derives a model for such visualizations and an exemplar user interface design is implemented for the probabilistic fulltext retrieval system INQUERY. Visualizations are used to communicate information search and browsing activities in a natural way by applying metaphors of spatial navigation in abstract information spaces. Visualization tools for exploring information spaces and judging relevance of information items are introduced and an example session demonstrates the prototype. The presence of a spatial model in the user's mind and interaction with a system's corresponding display methods is regarded as an essential contribution towards natural interaction and reduction of cognitive costs during e.g. query construction, orientation within the database content, relevance judgement and orientation within the retrieval context.

Type

a

0.0026849252 = product of:
  0.0053698504 = sum of:
    0.0053698504 = product of:
      0.010739701 = sum of:
        0.010739701 = weight(_text_:a in 919) [ClassicSimilarity], result of:
          0.010739701 = score(doc=919,freq=14.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.20223314 = fieldWeight in 919, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=919)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Document retrieval is a highly interactive process dealing with large amounts of information. Visual representations can provide both a means for managing the complexity of large information structures and an interface style well suited to interactive manipulation. The system we have designed utilizes visually displayed graphic structures and a direct manipulation interface style to supply an integrated environment for retrieval. A common visually displayed network structure is used for query, document content, and term relations. A query can be modified through direct manipulation of its visual form by incorporating terms from any other information structure the system displays. An associative thesaurus of terms and an inter-document network provide information about a document collection that can complement other retrieval aids. Visualization of these large data structures makes use of fisheye views and overview diagrams to help overcome some of the inherent difficulties of orientation and navigation in large information structures.

Type

a

0.0026849252 = product of:
  0.0053698504 = sum of:
    0.0053698504 = product of:
      0.010739701 = sum of:
        0.010739701 = weight(_text_:a in 644) [ClassicSimilarity], result of:
          0.010739701 = score(doc=644,freq=14.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.20223314 = fieldWeight in 644, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=644)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

While the search interfaces used by common academic digital libraries provide easy access to a wealth of peer-reviewed literature, their interfaces provide little support for exploratory browsing. When faced with a complex search task (such as one that requires knowledge discovery), exploratory browsing is an important first step in an exploratory search process. To more effectively support exploratory browsing, we have designed and implemented a novel academic digital library search interface (KLink Search) with two new features: visually linked keywords and an interactive workspace. To study the potential value of these features, we have conducted a controlled laboratory study with 32 participants, comparing KLink Search to a baseline digital library search interface modeled after that used by IEEE Xplore. Based on subjective opinions, objective performance, and behavioral data, we show the value of adding lightweight visual and interactive features to academic digital library search interfaces to support exploratory browsing.

Type

a

0.0025370158 = product of:
  0.0050740317 = sum of:
    0.0050740317 = product of:
      0.010148063 = sum of:
        0.010148063 = weight(_text_:a in 1137) [ClassicSimilarity], result of:
          0.010148063 = score(doc=1137,freq=18.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.19109234 = fieldWeight in 1137, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1137)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Based on real-world user demands, we demonstrate how animated visualization of evolving text corpora displays the underlying dynamics of semantic content. To interpret the results, one needs a dynamic theory of word meaning. We suggest that conceptual dynamics as the interaction between kinds of intellectual and emotional content and language is key for such a theory. We demonstrate our method by two-way seriation, which is a popular technique to analyze groups of similar instances and their features as well as the connections between the groups themselves. The two-way seriated data may be visualized as a two-dimensional heat map or as a three-dimensional landscape in which color codes or height correspond to the values in the matrix. In this article, we focus on two-way seriation of sparse data in the Reuters-21568 test collection. To achieve a meaningful visualization, we introduce a compactly supported convolution kernel similar to filter kernels used in image reconstruction and geostatistics. This filter populates the high-dimensional sparse space with values that interpolate nearby elements and provides insight into the clustering structure. We also extend two-way seriation to deal with online updates of both the row and column spaces and, combined with the convolution kernel, demonstrate a three-dimensional visualization of dynamics.

Type

a

0.0023678814 = product of:
  0.0047357627 = sum of:
    0.0047357627 = product of:
      0.009471525 = sum of:
        0.009471525 = weight(_text_:a in 1534) [ClassicSimilarity], result of:
          0.009471525 = score(doc=1534,freq=2.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.17835285 = fieldWeight in 1534, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.109375 = fieldNorm(doc=1534)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Type

a

0.0021560488 = product of:
  0.0043120976 = sum of:
    0.0043120976 = product of:
      0.008624195 = sum of:
        0.008624195 = weight(_text_:a in 1211) [ClassicSimilarity], result of:
          0.008624195 = score(doc=1211,freq=52.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.16239727 = fieldWeight in 1211, product of:
              7.2111025 = tf(freq=52.0), with freq of:
                52.0 = termFreq=52.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.01953125 = fieldNorm(doc=1211)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

In this article we present a method for retrieving documents from a digital library through a visual interface based on automatically generated concepts. We used a vocabulary generation algorithm to generate a set of concepts for the digital library and a technique called the max-min distance technique to cluster them. Additionally, the concepts were visualized in a spring embedding graph layout to depict the semantic relationship among them. The resulting graph layout serves as an aid to users for retrieving documents. An online archive containing the contents of D-Lib Magazine from July 1995 to May 2002 was used to test the utility of an implemented retrieval and visualization system. We believe that the method developed and tested can be applied to many different domains to help users get a better understanding of online document collections and to minimize users' cognitive load during execution of search tasks. Over the past few years, the volume of information available through the World Wide Web has been expanding exponentially. Never has so much information been so readily available and shared among so many people. Unfortunately, the unstructured nature and huge volume of information accessible over networks have made it hard for users to sift through and find relevant information. To deal with this problem, information retrieval (IR) techniques have gained more intensive attention from both industrial and academic researchers. Numerous IR techniques have been developed to help deal with the information overload problem. These techniques concentrate on mathematical models and algorithms for retrieval. Popular IR models such as the Boolean model, the vector-space model, the probabilistic model and their variants are well established.
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.
Nevertheless, because thesaurus use has shown to improve retrieval, for our method we integrate functions in the search interface that permit users to explore built-in search vocabularies to improve retrieval from digital libraries. Our method automatically generates the terms and their semantic relationships representing relevant topics covered in a digital library. We call these generated terms the "concepts", and the generated terms and their semantic relationships we call the "concept space". Additionally, we used a visualization technique to display the concept space and allow users to interact with this space. The automatically generated term set is considered to be more representative of subject area in a corpus than an "externally" imposed thesaurus, and our method has the potential of saving a significant amount of time and labor for those who have been manually creating thesauri as well. Information visualization is an emerging discipline and developed very quickly in the last decade. With growing volumes of documents and associated complexities, information visualization has become increasingly important. Researchers have found information visualization to be an effective way to use and understand information while minimizing a user's cognitive load. Our work was based on an algorithmic approach of concept discovery and association. Concepts are discovered using an algorithm based on an automated thesaurus generation procedure. Subsequently, similarities among terms are computed using the cosine measure, and the associations among terms are established using a method known as max-min distance clustering. The concept space is then visualized in a spring embedding graph, which roughly shows the semantic relationships among concepts in a 2-D visual representation. The semantic space of the visualization is used as a medium for users to retrieve the desired documents. In the remainder of this article, we present our algorithmic approach of concept generation and clustering, followed by description of the visualization technique and interactive interface. The paper ends with key conclusions and discussions on future work.

Content

The JAVA applet is available at <http://ella.slis.indiana.edu/~junzhang/dlib/IV.html>. A prototype of this interface has been developed and is available at <http://ella.slis.indiana.edu/~junzhang/dlib/IV.html>. The D-Lib search interface is available at <http://www.dlib.org/Architext/AT-dlib2query.html>.

Type

a

0.0018909799 = product of:
  0.0037819599 = sum of:
    0.0037819599 = product of:
      0.0075639198 = sum of:
        0.0075639198 = weight(_text_:a in 2717) [ClassicSimilarity], result of:
          0.0075639198 = score(doc=2717,freq=10.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.14243183 = fieldWeight in 2717, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2717)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

As the volume and breadth of online information is rapidly increasing, ad hoc search systems become less and less efficient to answer information needs of modern users. To support the growing complexity of search tasks, researchers in the field of information developed and explored a range of approaches that extend the traditional ad hoc retrieval paradigm. Among these approaches, personalized search systems and exploratory search systems attracted many followers. Personalized search explored the power of artificial intelligence techniques to provide tailored search results according to different user interests, contexts, and tasks. In contrast, exploratory search capitalized on the power of human intelligence by providing users with more powerful interfaces to support the search process. As these approaches are not contradictory, we believe that they can re-enforce each other. We argue that the effectiveness of personalized search systems may be increased by allowing users to interact with the system and learn/investigate the problem in order to reach the final goal. We also suggest that an interactive visualization approach could offer a good ground to combine the strong sides of personalized and exploratory search approaches. This paper proposes a specific way to integrate interactive visualization and personalized search and introduces an adaptive visualization based search system Adaptive VIBE that implements it. We tested the effectiveness of Adaptive VIBE and investigated its strengths and weaknesses by conducting a full-scale user study. The results show that Adaptive VIBE can improve the precision and the productivity of the personalized search system while helping users to discover more diverse sets of information.

Type

a

0.0018909799 = product of:
  0.0037819599 = sum of:
    0.0037819599 = product of:
      0.0075639198 = sum of:
        0.0075639198 = weight(_text_:a in 3366) [ClassicSimilarity], result of:
          0.0075639198 = score(doc=3366,freq=10.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.14243183 = fieldWeight in 3366, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3366)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Documents in rich text corpora usually contain multiple facets of information. For example, an article about a specific disease often consists of different facets such as symptom, treatment, cause, diagnosis, prognosis, and prevention. Thus, documents may have different relations based on different facets. Powerful search tools have been developed to help users locate lists of individual documents that are most related to specific keywords. However, there is a lack of effective analysis tools that reveal the multifaceted relations of documents within or cross the document clusters. In this paper, we present FacetAtlas, a multifaceted visualization technique for visually analyzing rich text corpora. FacetAtlas combines search technology with advanced visual analytical tools to convey both global and local patterns simultaneously. We describe several unique aspects of FacetAtlas, including (1) node cliques and multifaceted edges, (2) an optimized density map, and (3) automated opacity pattern enhancement for highlighting visual patterns, (4) interactive context switch between facets. In addition, we demonstrate the power of FacetAtlas through a case study that targets patient education in the health care domain. Our evaluation shows the benefits of this work, especially in support of complex multifaceted data analysis.

Type

a

0.0011959607 = product of:
  0.0023919214 = sum of:
    0.0023919214 = product of:
      0.0047838427 = sum of:
        0.0047838427 = weight(_text_:a in 566) [ClassicSimilarity], result of:
          0.0047838427 = score(doc=566,freq=4.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.090081796 = fieldWeight in 566, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=566)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Type

a

6.765375E-4 = product of:
  0.001353075 = sum of:
    0.001353075 = product of:
      0.00270615 = sum of:
        0.00270615 = weight(_text_:a in 6043) [ClassicSimilarity], result of:
          0.00270615 = score(doc=6043,freq=2.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.050957955 = fieldWeight in 6043, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.03125 = fieldNorm(doc=6043)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Type

a