Search (32 results, page 2 of 2)

4.4060132E-4 = product of:
  0.0074902223 = sum of:
    0.0074902223 = weight(_text_:in in 2131) [ClassicSimilarity], result of:
      0.0074902223 = score(doc=2131,freq=8.0), product of:
        0.04153252 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.030532904 = queryNorm
        0.18034597 = fieldWeight in 2131, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.046875 = fieldNorm(doc=2131)
  0.05882353 = coord(1/17)

Abstract

This is a preprint to be published in the Extensions & Corrections to the UDC. The paper explains the basic functions of browsing and searching that need to be supported in relation to analytico-synthetic classifications such as Universal Decimal Classification (UDC), irrespective of any specific, real-life implementation. UDC is an example of a semi-faceted system that can be used, for instance, for both post-coordinate searching and hierarchical/facet browsing. The advantages of using a classification for IR, however, depend on the strength of the GUI, which should provide a user-friendly interface to classification browsing and searching. The power of this interface is in supporting visualisation that will 'convert' what is potentially a user-unfriendly indexing language based on symbols, to a subject presentation that is easy to understand, search and navigate. A summary of the basic functions of searching and browsing a classification that may be provided on a user-friendly interface is given and examples of classification browsing interfaces are provided.

Content

To be published in the Extensions & Corrections to the UDC. 28(2006).

4.4060132E-4 = product of:
  0.0074902223 = sum of:
    0.0074902223 = weight(_text_:in in 2906) [ClassicSimilarity], result of:
      0.0074902223 = score(doc=2906,freq=8.0), product of:
        0.04153252 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.030532904 = queryNorm
        0.18034597 = fieldWeight in 2906, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.046875 = fieldNorm(doc=2906)
  0.05882353 = coord(1/17)

Abstract

The use of research impact metrics and analytics has become an integral component to many aspects of institutional assessment. Many platforms currently exist to provide such analytics, both proprietary and open source; however, the functionality of these systems may not always overlap to serve uniquely specific needs. In this paper, I describe a novel web-based platform, named Manifold, that I built to serve custom research impact assessment needs in the University of Minnesota Medical School. Built on a standard LAMP architecture, Manifold automatically pulls publication data for faculty from Scopus through APIs, calculates impact metrics through automated analytics, and dynamically generates report-like profiles that visualize those metrics. Work on this project has resulted in many lessons learned about challenges to sustainability and scalability in developing a system of such magnitude.

4.1050607E-4 = product of:
  0.006978603 = sum of:
    0.006978603 = weight(_text_:in in 1211) [ClassicSimilarity], result of:
      0.006978603 = score(doc=1211,freq=40.0), product of:
        0.04153252 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.030532904 = queryNorm
        0.16802745 = fieldWeight in 1211, product of:
          6.3245554 = tf(freq=40.0), with freq of:
            40.0 = termFreq=40.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.01953125 = fieldNorm(doc=1211)
  0.05882353 = coord(1/17)

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.

Theme

Semantisches Umfeld in Indexierung u. Retrieval

3.8157197E-4 = product of:
  0.006486723 = sum of:
    0.006486723 = weight(_text_:in in 3195) [ClassicSimilarity], result of:
      0.006486723 = score(doc=3195,freq=6.0), product of:
        0.04153252 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.030532904 = queryNorm
        0.1561842 = fieldWeight in 3195, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.046875 = fieldNorm(doc=3195)
  0.05882353 = coord(1/17)

Abstract

The scientific community's growing eagerness to make research data available to the public provides libraries - with our expertise in metadata and discovery - an interesting new opportunity. This paper details the in-house creation of a "data catalog" which describes datasets ranging from population-level studies like the US Census to small, specialized datasets created by researchers at our own institution. Based on Symfony2 and Solr, the data catalog provides a powerful search interface to help researchers locate the data that can help them, and an administrative interface so librarians can add, edit, and manage metadata elements at will. This paper will outline the successes, failures, and total redos that culminated in the current manifestation of our data catalog.

3.6716778E-4 = product of:
  0.0062418524 = sum of:
    0.0062418524 = weight(_text_:in in 4837) [ClassicSimilarity], result of:
      0.0062418524 = score(doc=4837,freq=8.0), product of:
        0.04153252 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.030532904 = queryNorm
        0.15028831 = fieldWeight in 4837, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4837)
  0.05882353 = coord(1/17)

Abstract

Icon system could represent an efficient solution for collective iconic categorization of knowledge by providing graphical interpretation. Their pictorial characters assist visualizing the structure of text to become more understandable beyond vocabulary obstacle. In this paper we are proposing a Knowledge Engineering (KM) based iconic representation approach. We assume that these systematic icons improve collective knowledge management. Meanwhile, text (constructed under our knowledge management model - Hypertopic) helps to reduce the diversity of graphical understanding belonging to different users. This "position paper" also prepares to demonstrate our hypothesis by an "iconic social tagging" experiment which is to be accomplished in 2011 with UTT students. We describe the "socio semantic web" information portal involved in this project, and a part of the icons already designed for this experiment in Sustainability field. We have reviewed existing theoretical works on icons from various origins, which can be used to lay the foundation of robust "icons systems".

3.6716778E-4 = product of:
  0.0062418524 = sum of:
    0.0062418524 = weight(_text_:in in 3870) [ClassicSimilarity], result of:
      0.0062418524 = score(doc=3870,freq=8.0), product of:
        0.04153252 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.030532904 = queryNorm
        0.15028831 = fieldWeight in 3870, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3870)
  0.05882353 = coord(1/17)

Abstract

Data from multiple knowledge organization systems are combined to provide a global overview of the content holdings of a small personal library. Subject headings and classification data are used to effectively map the combined book and topic space of the library. While harvested and manipulated by hand, the work reveals issues and potential solutions when using automated techniques to produce topic maps of much larger libraries. The small library visualized consists of the thirty-nine, digital, English language books found in the Osama Bin Laden (OBL) compound in Abbottabad, Pakistan upon his death. As this list of books has garnered considerable media attention, it is worth providing a visual overview of the subject content of these books - some of which is not readily apparent from the titles. Metadata from subject headings and classification numbers was combined to create book-subject maps. Tree maps of the classification data were also produced. The books contain 328 subject headings. In order to enhance the base map with meaningful thematic overlay, library holding count data was also harvested (and aggregated from duplicates). This additional data revealed the relative scarcity or popularity of individual books.

Content

Beitrag bei: NASKO 2017: Visualizing Knowledge Organization: Bringing Focus to Abstract Realities. The sixth North American Symposium on Knowledge Organization (NASKO 2017), June 15-16, 2017, in Champaign, IL, USA.

3.6532735E-4 = product of:
  0.0062105646 = sum of:
    0.0062105646 = weight(_text_:in in 1197) [ClassicSimilarity], result of:
      0.0062105646 = score(doc=1197,freq=22.0), product of:
        0.04153252 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.030532904 = queryNorm
        0.14953499 = fieldWeight in 1197, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0234375 = fieldNorm(doc=1197)
  0.05882353 = coord(1/17)

Abstract

User interfaces for digital information discovery often require users to click around and read a lot of text in order to find the text they want to read-a process that is often frustrating and tedious. This is exacerbated because of the limited amount of text that can be displayed on a computer screen. To improve the user experience of computer mediated information discovery, information visualization techniques are applied to the digital library context, while retaining traditional information organization concepts. In this article, the "virtual (book) spine" and the virtual spine viewer are introduced. The virtual spine viewer is an application which allows users to visually explore large information spaces or collections while also allowing users to hone in on individual resources of interest. The virtual spine viewer introduced here is an alpha prototype, presented to promote discussion and further work. Information discovery changed radically with the introduction of computerized library access catalogs, the World Wide Web and its search engines, and online bookstores. Yet few instances of these technologies provide a user experience analogous to walking among well-organized, well-stocked bookshelves-which many people find useful as well as pleasurable. To put it another way, many of us have heard or voiced complaints about the paucity of "online browsing"-but what does this really mean? In traditional information spaces such as libraries, often we can move freely among the books and other resources. When we walk among organized, labeled bookshelves, we get a sense of the information space-we take in clues, perhaps unconsciously, as to the scope of the collection, the currency of resources, the frequency of their use, etc. We also enjoy unexpected discoveries such as finding an interesting resource because library staff deliberately located it near similar resources, or because it was miss-shelved, or because we saw it on a bookshelf on the way to the water fountain.
When our experience of information discovery is mediated by a computer, we neither move ourselves nor the monitor. We have only the computer's monitor to view, and the keyboard and/or mouse to manipulate what is displayed there. Computer interfaces often reduce our ability to get a sense of the contents of a library: we don't perceive the scope of the library: its breadth, (the quantity of materials/information), its density (how full the shelves are, how thorough the collection is for individual topics), or the general audience for the materials (e.g., whether the materials are appropriate for middle school students, college professors, etc.). Additionally, many computer interfaces for information discovery require users to scroll through long lists, to click numerous navigational links and to read a lot of text to find the exact text they want to read. Text features of resources are almost always presented alphabetically, and the number of items in these alphabetical lists sometimes can be very long. Alphabetical ordering is certainly an improvement over no ordering, but it generally has no bearing on features with an inherent non-alphabetical ordering (e.g., dates of historical events), nor does it necessarily group similar items together. Alphabetical ordering of resources is analogous to one of the most familiar complaints about dictionaries: sometimes you need to know how to spell a word in order to look up its correct spelling in the dictionary. Some have used technology to replicate the appearance of physical libraries, presenting rooms of bookcases and shelves of book spines in virtual 3D environments. This approach presents a problem, as few book spines can be displayed legibly on a monitor screen. This article examines the role of book spines, call numbers, and other traditional organizational and information discovery concepts, and integrates this knowledge with information visualization techniques to show how computers and monitors can meet or exceed similar information discovery methods. The goal is to tap the unique potentials of current information visualization approaches in order to improve information discovery, offer new services, and most important of all, improve user satisfaction. We need to capitalize on what computers do well while bearing in mind their limitations. The intent is to design GUIs to optimize utility and provide a positive experience for the user.

3.597495E-4 = product of:
  0.0061157416 = sum of:
    0.0061157416 = weight(_text_:in in 79) [ClassicSimilarity], result of:
      0.0061157416 = score(doc=79,freq=12.0), product of:
        0.04153252 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.030532904 = queryNorm
        0.14725187 = fieldWeight in 79, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.03125 = fieldNorm(doc=79)
  0.05882353 = coord(1/17)

Abstract

With the terrific growth of data volume and data being produced every second on millions of devices across the globe, there is a desperate need to manage the unstructured data available on web pages efficiently. Semantic Web or also known as Web of Trust structures the scattered data on the Internet according to the needs of the user. It is an extension of the World Wide Web (WWW) which focuses on manipulating web data on behalf of Humans. Due to the ability of the Semantic Web to integrate data from disparate sources and hence makes it more user-friendly, it is an emerging trend. Tim Berners-Lee first introduced the term Semantic Web and since then it has come a long way to become a more intelligent and intuitive web. Data Visualization plays an essential role in explaining complex concepts in a universal manner through pictorial representation, and the Semantic Web helps in broadening the potential of Data Visualization and thus making it an appropriate combination. The objective of this chapter is to provide fundamental insights concerning the semantic web technologies and in addition to that it also elucidates the issues as well as the solutions regarding the semantic web. The purpose of this chapter is to highlight the semantic web architecture in detail while also comparing it with the traditional search system. It classifies the semantic web architecture into three major pillars i.e. RDF, Ontology, and XML. Moreover, it describes different semantic web tools used in the framework and technology. It attempts to illustrate different approaches of the semantic web search engines. Besides stating numerous challenges faced by the semantic web it also illustrates the solutions.

3.1797664E-4 = product of:
  0.005405603 = sum of:
    0.005405603 = weight(_text_:in in 3888) [ClassicSimilarity], result of:
      0.005405603 = score(doc=3888,freq=6.0), product of:
        0.04153252 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.030532904 = queryNorm
        0.1301535 = fieldWeight in 3888, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3888)
  0.05882353 = coord(1/17)

Abstract

We present a new technique called "t-SNE" that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images of objects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large data sets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of data sets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualizations produced by t-SNE are significantly better than those produced by the other techniques on almost all of the data sets.

3.1155223E-4 = product of:
  0.0052963877 = sum of:
    0.0052963877 = weight(_text_:in in 919) [ClassicSimilarity], result of:
      0.0052963877 = score(doc=919,freq=4.0), product of:
        0.04153252 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.030532904 = queryNorm
        0.12752387 = fieldWeight in 919, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.046875 = fieldNorm(doc=919)
  0.05882353 = coord(1/17)

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.

Theme

Semantisches Umfeld in Indexierung u. Retrieval

2.9373422E-4 = product of:
  0.0049934816 = sum of:
    0.0049934816 = weight(_text_:in in 3887) [ClassicSimilarity], result of:
      0.0049934816 = score(doc=3887,freq=2.0), product of:
        0.04153252 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.030532904 = queryNorm
        0.120230645 = fieldWeight in 3887, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0625 = fieldNorm(doc=3887)
  0.05882353 = coord(1/17)

Abstract

Although extremely useful for visualizing high-dimensional data, t-SNE plots can sometimes be mysterious or misleading. By exploring how it behaves in simple cases, we can learn to use it more effectively. We'll walk through a series of simple examples to illustrate what t-SNE diagrams can and cannot show. The t-SNE technique really is useful-but only if you know how to interpret it.

2.0770147E-4 = product of:
  0.003530925 = sum of:
    0.003530925 = weight(_text_:in in 4746) [ClassicSimilarity], result of:
      0.003530925 = score(doc=4746,freq=4.0), product of:
        0.04153252 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.030532904 = queryNorm
        0.08501591 = fieldWeight in 4746, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.03125 = fieldNorm(doc=4746)
  0.05882353 = coord(1/17)

Abstract

Many real-world domains can be represented as large node-link graphs: backbone Internet routers connect with 70,000 other hosts, mid-sized Web servers handle between 20,000 and 200,000 hyperlinked documents, and dictionaries contain millions of words defined in terms of each other. Computational manipulation of such large graphs is common, but previous tools for graph visualization have been limited to datasets of a few thousand nodes. Visual depictions of graphs and networks are external representations that exploit human visual processing to reduce the cognitive load of many tasks that require understanding of global or local structure. We assert that the two key advantages of computer-based systems for information visualization over traditional paper-based visual exposition are interactivity and scalability. We also argue that designing visualization software by taking the characteristics of a target user's task domain into account leads to systems that are more effective and scale to larger datasets than previous work. This thesis contains a detailed analysis of three specialized systems for the interactive exploration of large graphs, relating the intended tasks to the spatial layout and visual encoding choices. We present two novel algorithms for specialized layout and drawing that use quite different visual metaphors. The H3 system for visualizing the hyperlink structures of web sites scales to datasets of over 100,000 nodes by using a carefully chosen spanning tree as the layout backbone, 3D hyperbolic geometry for a Focus+Context view, and provides a fluid interactive experience through guaranteed frame rate drawing. The Constellation system features a highly specialized 2D layout intended to spatially encode domain-specific information for computational linguists checking the plausibility of a large semantic network created from dictionaries. The Planet Multicast system for displaying the tunnel topology of the Internet's multicast backbone provides a literal 3D geographic layout of arcs on a globe to help MBone maintainers find misconfigured long-distance tunnels. Each of these three systems provides a very different view of the graph structure, and we evaluate their efficacy for the intended task. We generalize these findings in our analysis of the importance of interactivity and specialization for graph visualization systems that are effective and scalable.