Search (28 results, page 1 of 2)

0.11015203 = product of:
  0.1835867 = sum of:
    0.048630223 = weight(_text_:retrieval in 3264) [ClassicSimilarity], result of:
      0.048630223 = score(doc=3264,freq=6.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.34732026 = fieldWeight in 3264, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=3264)
    0.118616216 = weight(_text_:semantic in 3264) [ClassicSimilarity], result of:
      0.118616216 = score(doc=3264,freq=10.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.616327 = fieldWeight in 3264, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.046875 = fieldNorm(doc=3264)
    0.01634027 = product of:
      0.03268054 = sum of:
        0.03268054 = weight(_text_:web in 3264) [ClassicSimilarity], result of:
          0.03268054 = score(doc=3264,freq=2.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.21634221 = fieldWeight in 3264, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.046875 = fieldNorm(doc=3264)
      0.5 = coord(1/2)
  0.6 = coord(3/5)

Abstract

We scho how the beenfits of the view-based search method, developed within the information retrieval community, can be extended with ontology-based search, developed within the Semantic Web community, and with semantic recommendations. As a proof of the concept, we have implemented an ontology-and view-based search engine and recommendations system Ontogaotr for RDF(S) repositories. Ontogator is innovative in two ways. Firstly, the RDFS.based ontologies used for annotating metadata are used in the user interface to facilitate view-based information retrieval. The views provide the user with an overview of the repositorys contents and a vocabulary for expressing search queries. Secondlyy, a semantic browsing function is provided by a recommender system. This system enriches instance level metadata by ontologies and provides the user with links to semantically related relevant resources. The semantic linkage is specified in terms of logical rules. To illustrate and discuss the ideas, a deployed application of Ontogator to a photo repository of the Helsinki University Museum is presented.

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.0978199 = product of:
  0.16303316 = sum of:
    0.028076671 = weight(_text_:retrieval in 540) [ClassicSimilarity], result of:
      0.028076671 = score(doc=540,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.20052543 = fieldWeight in 540, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=540)
    0.118616216 = weight(_text_:semantic in 540) [ClassicSimilarity], result of:
      0.118616216 = score(doc=540,freq=10.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.616327 = fieldWeight in 540, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.046875 = fieldNorm(doc=540)
    0.01634027 = product of:
      0.03268054 = sum of:
        0.03268054 = weight(_text_:web in 540) [ClassicSimilarity], result of:
          0.03268054 = score(doc=540,freq=2.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.21634221 = fieldWeight in 540, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.046875 = fieldNorm(doc=540)
      0.5 = coord(1/2)
  0.6 = coord(3/5)

Abstract

Semantic Search has become an active research of Semantic Web in recent years. The classification methodology plays a pretty critical role in the beginning of search process to disambiguate irrelevant information. However, the applications related to Folksonomy suffer from many obstacles. This study attempts to eliminate the problems resulted from Folksonomy using existing semantic technology. We also focus on how to effectively integrate heterogeneous ontologies over the Internet to acquire the integrity of domain knowledge. A faceted logic layer is abstracted in order to strengthen category framework and organize existing available ontologies according to a series of steps based on the methodology of faceted classification and ontology construction. The result showed that our approach can facilitate the integration of inconsistent or even heterogeneous ontologies. This paper also generalizes the principles of picking appropriate facets with which our facet browser completely complies so that better semantic search result can be obtained.

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.083605506 = product of:
  0.1393425 = sum of:
    0.032756116 = weight(_text_:retrieval in 2933) [ClassicSimilarity], result of:
      0.032756116 = score(doc=2933,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.23394634 = fieldWeight in 2933, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2933)
    0.08752273 = weight(_text_:semantic in 2933) [ClassicSimilarity], result of:
      0.08752273 = score(doc=2933,freq=4.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.45476598 = fieldWeight in 2933, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2933)
    0.019063652 = product of:
      0.038127303 = sum of:
        0.038127303 = weight(_text_:web in 2933) [ClassicSimilarity], result of:
          0.038127303 = score(doc=2933,freq=2.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.25239927 = fieldWeight in 2933, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2933)
      0.5 = coord(1/2)
  0.6 = coord(3/5)

Abstract

We propose a method for improving access to scientific literature by analyzing the content of research papers beyond citation links and topic tracking. Our model relies on a typology of explicit semantic relations. These relations are instantiated in the abstract/introduction part of the papers and can be identified automatically using textual data and external ontologies. Preliminary results show a promising precision in unsupervised relationship classification.

Content

Vortrag, "Semantics, Analytics, Visualisation: Enhancing Scholarly Data Workshop co-located with the 25th International World Wide Web Conference April 11, 2016 - Montreal, Canada", Montreal 2016.

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.07994416 = product of:
  0.13324027 = sum of:
    0.033088673 = weight(_text_:retrieval in 2208) [ClassicSimilarity], result of:
      0.033088673 = score(doc=2208,freq=4.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.23632148 = fieldWeight in 2208, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2208)
    0.076566435 = weight(_text_:semantic in 2208) [ClassicSimilarity], result of:
      0.076566435 = score(doc=2208,freq=6.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.39783734 = fieldWeight in 2208, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2208)
    0.02358515 = product of:
      0.0471703 = sum of:
        0.0471703 = weight(_text_:web in 2208) [ClassicSimilarity], result of:
          0.0471703 = score(doc=2208,freq=6.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.3122631 = fieldWeight in 2208, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2208)
      0.5 = coord(1/2)
  0.6 = coord(3/5)

Abstract

The formal structure of the information on the Semantic Web lends itself to faceted browsing, an information retrieval method where users can filter results based on the values of properties ("facets"). Numerous faceted browsers have been created to browse RDF and Linked Data, but these systems use their own ontologies for defining how data is queried to populate their facets. Since the source data is the same format across these systems (specifically, RDF), we can unify the different methods of describing how to quer the underlying data, to enable compatibility across systems, and provide an extensible base ontology for future systems. To this end, we present FacetOntology, an ontology that defines how to query data to form a faceted browser, and a number of transformations and filters that can be applied to data before it is shown to users. FacetOntology overcomes limitations in the expressivity of existing work, by enabling the full expressivity of SPARQL when selecting data for facets. By applying a FacetOntology definition to data, a set of facets are specified, each with queries and filters to source RDF data, which enables faceted browsing systems to be created using that RDF data.

Theme

Semantisches Umfeld in Indexierung u. Retrieval
Semantic Web

0.07797098 = product of:
  0.12995163 = sum of:
    0.03743556 = weight(_text_:retrieval in 792) [ClassicSimilarity], result of:
      0.03743556 = score(doc=792,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.26736724 = fieldWeight in 792, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0625 = fieldNorm(doc=792)
    0.07072904 = weight(_text_:semantic in 792) [ClassicSimilarity], result of:
      0.07072904 = score(doc=792,freq=2.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.36750638 = fieldWeight in 792, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.0625 = fieldNorm(doc=792)
    0.021787029 = product of:
      0.043574058 = sum of:
        0.043574058 = weight(_text_:web in 792) [ClassicSimilarity], result of:
          0.043574058 = score(doc=792,freq=2.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.2884563 = fieldWeight in 792, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0625 = fieldNorm(doc=792)
      0.5 = coord(1/2)
  0.6 = coord(3/5)

Abstract

Based on an in-depth analysis of existing approaches in building ontology-based query systems we discuss and compare the methods, approaches to be used in current query systems using Ontology or the Semantic Web techniques. This paper identifies various relevant research directions in ontology-based querying research. Based on the results of our investigation we summarise the state of the art ontology-based query/search and name areas of further research activities.

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.0631024 = product of:
  0.10517066 = sum of:
    0.023397226 = weight(_text_:retrieval in 4599) [ClassicSimilarity], result of:
      0.023397226 = score(doc=4599,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.16710453 = fieldWeight in 4599, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4599)
    0.062516235 = weight(_text_:semantic in 4599) [ClassicSimilarity], result of:
      0.062516235 = score(doc=4599,freq=4.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.32483283 = fieldWeight in 4599, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4599)
    0.019257195 = product of:
      0.03851439 = sum of:
        0.03851439 = weight(_text_:web in 4599) [ClassicSimilarity], result of:
          0.03851439 = score(doc=4599,freq=4.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.25496176 = fieldWeight in 4599, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4599)
      0.5 = coord(1/2)
  0.6 = coord(3/5)

Abstract

Aufgrund der fortgesetzten Publikationsflut stellt sich immer dringender die Frage, wie die Schwellen für die Titel- und Normdatenpflege gesenkt werden können - sowohl für die intellektuelle als auch die automatisierte Sacherschließung. Zu einer Verbesserung der Daten- und Arbeitsqualität in der Sacherschließung kann beigetragen werden a) durch eine flexible Visualisierung der Gemeinsamen Normdatei (GND) und anderer Wissensorganisationssysteme, so dass deren Graphstruktur intuitiv erfassbar wird, und b) durch eine investigative Analyse ihrer aktuellen Struktur und die Entwicklung angepasster automatisierter Methoden zur Ermittlung und Korrektur fehlerhafter Muster. Die Deutsche Nationalbibliothek (DNB) prüft im Rahmen des GND-Entwicklungsprogramms 2017-2021, welche Bedingungen für eine fruchtbare community-getriebene Open-Source-Entwicklung entsprechender Werkzeuge gegeben sein müssen. Weiteres Potential steckt in einem langfristigen Übergang zu einer Darstellung von Titel- und Normdaten in Beschreibungssprachen im Sinne des Semantic Web (RDF; OWL, SKOS). So profitiert die GND von der Interoperabilität mit anderen kontrollierten Vokabularen und von einer erleichterten Interaktion mit anderen Fach-Communities und kann umgekehrt auch außerhalb des Bibliothekswesens zu einem noch attraktiveren Wissensorganisationssystem werden. Darüber hinaus bieten die Ansätze aus dem Semantic Web die Möglichkeit, stärker formalisierte, strukturierende Satellitenvokabulare rund um die GND zu entwickeln. Daraus ergeben sich nicht zuletzt auch neue Perspektiven für die automatisierte Sacherschließung. Es wäre lohnend, näher auszuloten, wie und inwieweit semantisch-logische Verfahren den bestehenden Methodenmix bereichern können.

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.061448015 = product of:
  0.10241336 = sum of:
    0.033088673 = weight(_text_:retrieval in 1211) [ClassicSimilarity], result of:
      0.033088673 = score(doc=1211,freq=16.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.23632148 = fieldWeight in 1211, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.01953125 = fieldNorm(doc=1211)
    0.062516235 = weight(_text_:semantic in 1211) [ClassicSimilarity], result of:
      0.062516235 = score(doc=1211,freq=16.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.32483283 = fieldWeight in 1211, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.01953125 = fieldNorm(doc=1211)
    0.0068084467 = product of:
      0.013616893 = sum of:
        0.013616893 = weight(_text_:web in 1211) [ClassicSimilarity], result of:
          0.013616893 = score(doc=1211,freq=2.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.09014259 = fieldWeight in 1211, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.01953125 = fieldNorm(doc=1211)
      0.5 = coord(1/2)
  0.6 = coord(3/5)

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

0.055508126 = product of:
  0.09251354 = sum of:
    0.01871778 = weight(_text_:retrieval in 1163) [ClassicSimilarity], result of:
      0.01871778 = score(doc=1163,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.13368362 = fieldWeight in 1163, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.03125 = fieldNorm(doc=1163)
    0.06125315 = weight(_text_:semantic in 1163) [ClassicSimilarity], result of:
      0.06125315 = score(doc=1163,freq=6.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.31826988 = fieldWeight in 1163, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.03125 = fieldNorm(doc=1163)
    0.012542613 = product of:
      0.025085226 = sum of:
        0.025085226 = weight(_text_:22 in 1163) [ClassicSimilarity], result of:
          0.025085226 = score(doc=1163,freq=2.0), product of:
            0.16209066 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04628742 = queryNorm
            0.15476047 = fieldWeight in 1163, 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=1163)
      0.5 = coord(1/2)
  0.6 = coord(3/5)

Abstract

This paper addresses the problem of information discovery in large collections of text. For users, one of the key problems in working with such collections is determining where to focus their attention. In selecting documents for examination, users must be able to formulate reasonably precise queries. Queries that are too broad will greatly reduce the efficiency of information discovery efforts by overwhelming the users with peripheral information. In order to formulate efficient queries, a mechanism is needed to automatically alert users regarding potentially interesting information contained within the collection. This paper presents the results of an experiment designed to test one approach to generation of such alerts. The technique of latent semantic indexing (LSI) is used to identify relationships among entities of interest. Entity extraction software is used to pre-process the text of the collection so that the LSI space contains representation vectors for named entities in addition to those for individual terms. In the LSI space, the cosine of the angle between the representation vectors for two entities captures important information regarding the degree of association of those two entities. For appropriate choices of entities, determining the entity pairs with the highest mutual cosine values yields valuable information regarding the contents of the text collection. The test database used for the experiment consists of 150,000 news articles. The proposed approach for alert generation is tested using a counterterrorism analysis example. The approach is shown to have significant potential for aiding users in rapidly focusing on information of potential importance in large text collections. The approach also has value in identifying possible use of aliases.

Object

Latent Semantic Indexing

Source

Proceedings of the Fourth Workshop on Link Analysis, Counterterrorism, and Security, SIAM Data Mining Conference, Bethesda, MD, 20-22 April, 2006. [http://www.siam.org/meetings/sdm06/workproceed/Link%20Analysis/15.pdf]

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.04798256 = product of:
  0.1199564 = sum of:
    0.028076671 = weight(_text_:retrieval in 1162) [ClassicSimilarity], result of:
      0.028076671 = score(doc=1162,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.20052543 = fieldWeight in 1162, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=1162)
    0.091879725 = weight(_text_:semantic in 1162) [ClassicSimilarity], result of:
      0.091879725 = score(doc=1162,freq=6.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.47740483 = fieldWeight in 1162, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.046875 = fieldNorm(doc=1162)
  0.4 = coord(2/5)

Abstract

Latent Semantic Analysis (LSA) is a theory and method for extracting and representing the contextual-usage meaning of words by statistical computations applied to a large corpus of text (Landauer and Dumais, 1997). The underlying idea is that the aggregate of all the word contexts in which a given word does and does not appear provides a set of mutual constraints that largely determines the similarity of meaning of words and sets of words to each other. The adequacy of LSA's reflection of human knowledge has been established in a variety of ways. For example, its scores overlap those of humans on standard vocabulary and subject matter tests; it mimics human word sorting and category judgments; it simulates word-word and passage-word lexical priming data; and as reported in 3 following articles in this issue, it accurately estimates passage coherence, learnability of passages by individual students, and the quality and quantity of knowledge contained in an essay.

Object

Latent Semantic Indexing

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.04798256 = product of:
  0.1199564 = sum of:
    0.028076671 = weight(_text_:retrieval in 4097) [ClassicSimilarity], result of:
      0.028076671 = score(doc=4097,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.20052543 = fieldWeight in 4097, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=4097)
    0.091879725 = weight(_text_:semantic in 4097) [ClassicSimilarity], result of:
      0.091879725 = score(doc=4097,freq=6.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.47740483 = fieldWeight in 4097, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.046875 = fieldNorm(doc=4097)
  0.4 = coord(2/5)

Abstract

A challenge of managing and extracting useful knowledge from social media data sources has attracted much attention from academic and industry. To address this challenge, semantic analysis of textual data is focused in this paper. We propose an ontology-based approach to extract semantics of textual data and define the domain of data. In other words, we semantically analyse the social data at two levels i.e. the entity level and the domain level. We have chosen Twitter as a social channel challenge for a purpose of concept proof. Domain knowledge is captured in ontologies which are then used to enrich the semantics of tweets provided with specific semantic conceptual representation of entities that appear in the tweets. Case studies are used to demonstrate this approach. We experiment and evaluate our proposed approach with a public dataset collected from Twitter and from the politics domain. The ontology-based approach leverages entity extraction and concept mappings in terms of quantity and accuracy of concept identification.

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.04593361 = product of:
  0.114834026 = sum of:
    0.052317787 = weight(_text_:retrieval in 1520) [ClassicSimilarity], result of:
      0.052317787 = score(doc=1520,freq=10.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.37365708 = fieldWeight in 1520, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1520)
    0.062516235 = weight(_text_:semantic in 1520) [ClassicSimilarity], result of:
      0.062516235 = score(doc=1520,freq=4.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.32483283 = fieldWeight in 1520, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1520)
  0.4 = coord(2/5)

Abstract

This paper discusses issues concerning the augmentation of thesaurus relationships, in light of new application possibilities for retrieval. We first discuss a case study that explored the retrieval potential of an augmented set of thesaurus relationships by specialising standard relationships into richer subtypes, in particular hierarchical geographical containment and the associative relationship. We then locate this work in a broader context by reviewing various attempts to build taxonomies of thesaurus relationships, and conclude by discussing the feasibility of hierarchically augmenting the core set of thesaurus relationships, particularly the associative relationship. We discuss the possibility of enriching the specification and semantics of Related Term (RT relationships), while maintaining compatibility with traditional thesauri via a limited hierarchical extension of the associative (and hierarchical) relationships. This would be facilitated by distinguishing the type of term from the (sub)type of relationship and explicitly specifying semantic categories for terms following a faceted approach. We first illustrate how hierarchical spatial relationships can be used to provide more flexible retrieval for queries incorporating place names in applications employing online gazetteers and geographical thesauri. We then employ a set of experimental scenarios to investigate key issues affecting use of the associative (RT) thesaurus relationships in semantic distance measures. Previous work has noted the potential of RTs in thesaurus search aids but also the problem of uncontrolled expansion of query term sets. Results presented in this paper suggest the potential for taking account of the hierarchical context of an RT link and specialisations of the RT relationship

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.033685055 = product of:
  0.08421263 = sum of:
    0.0140383355 = weight(_text_:retrieval in 2133) [ClassicSimilarity], result of:
      0.0140383355 = score(doc=2133,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.10026272 = fieldWeight in 2133, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0234375 = fieldNorm(doc=2133)
    0.0701743 = weight(_text_:semantic in 2133) [ClassicSimilarity], result of:
      0.0701743 = score(doc=2133,freq=14.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.36462396 = fieldWeight in 2133, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.0234375 = fieldNorm(doc=2133)
  0.4 = coord(2/5)

Content

"Open Semantic Desktop Search Zur Tagung des Netzwerk Recherche ist die Desktop Suchmaschine Open Semantic Desktop Search zum unabhängigen und datenschutzfreundlichen Erschliessen und Analysieren von Dokumentenbergen nun erstmals auch als deutschsprachige Version verfügbar. Dank mächtiger Open Source Basis kann die auf Debian GNU/Linux und Apache Solr basierende freie Software als unter Linux, Windows oder Mac lauffähige virtuelle Maschine kostenlos heruntergeladen, genutzt, weitergegeben und weiterentwickelt werden. Dokumentenberge erschliessen Ob grösserer Leak oder Zusammenwürfeln oder (wieder) Erschliessen umfangreicherer (kollaborativer) Recherche(n) oder Archive: Hin und wieder müssen größere Datenberge bzw. Dokumentenberge erschlossen werden, die so viele Dokumente enthalten, dass Mensch diese Masse an Dokumenten nicht mehr alle nacheinander durchschauen und einordnen kann. Auch bei kontinuierlicher Recherche zu Fachthemen sammeln sich mit der Zeit größere Mengen digitalisierter oder digitaler Dokumente zu grösseren Datenbergen an, die immer weiter wachsen und deren Informationen mit einer Suchmaschine für das Archiv leichter auffindbar bleiben. Moderne Tools zur Datenanalyse in Verbindung mit Enterprise Search Suchlösungen und darauf aufbauender Recherche-Tools helfen (halb)automatisch.
Virtuelle Maschine für mehr Plattformunabhängigkeit Die nun auch deutschsprachig verfügbare und mit deutschen Daten wie Ortsnamen oder Bundestagsabgeordneten vorkonfigurierte virtuelle Maschine Open Semantic Desktop Search ermöglicht nun auch auf einzelnen Desktop Computern oder Notebooks mit Windows oder iOS (Mac) die Suche und Analyse von Dokumenten mit der Suchmaschine Open Semantic Search. Als virtuelle Maschine (VM) lässt sich die Suchmaschine Open Semantic Search nicht nur für besonders sensible Dokumente mit dem verschlüsselten Live-System InvestigateIX als abgeschottetes System auf verschlüsselten externen Datenträgern installieren, sondern als virtuelle Maschine für den Desktop auch einfach unter Windows oder auf einem Mac in eine bzgl. weiterer Software und Daten bereits existierende Systemumgebung integrieren, ohne hierzu auf einen (für gemeinsame Recherchen im Team oder für die Redaktion auch möglichen) Suchmaschinen Server angewiesen zu sein. Datenschutz & Unabhängigkeit: Grössere Unabhängigkeit von zentralen IT-Infrastrukturen für unabhängigen investigativen Datenjournalismus Damit ist investigative Recherche weitmöglichst unabhängig möglich: ohne teure, zentrale und von Administratoren abhängige Server, ohne von der Dokumentenanzahl abhängige teure Software-Lizenzen, ohne Internet und ohne spionierende Cloud-Dienste. Datenanalyse und Suche finden auf dem eigenen Computer statt, nicht wie bei vielen anderen Lösungen in der sogenannten Cloud."

Source

http://www.linux-community.de/Internal/Nachrichten/Open-Semantic-Search-zum-unabhaengigen-und-datenschutzfreundlichen-Erschliessen-von-Dokumenten

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.027309487 = product of:
  0.068273716 = sum of:
    0.04632414 = weight(_text_:retrieval in 4324) [ClassicSimilarity], result of:
      0.04632414 = score(doc=4324,freq=4.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.33085006 = fieldWeight in 4324, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4324)
    0.021949572 = product of:
      0.043899145 = sum of:
        0.043899145 = weight(_text_:22 in 4324) [ClassicSimilarity], result of:
          0.043899145 = score(doc=4324,freq=2.0), product of:
            0.16209066 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04628742 = queryNorm
            0.2708308 = fieldWeight in 4324, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4324)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Date

11. 2.2011 18:22:25

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.022418672 = product of:
  0.05604668 = sum of:
    0.03970641 = weight(_text_:retrieval in 3102) [ClassicSimilarity], result of:
      0.03970641 = score(doc=3102,freq=4.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.2835858 = fieldWeight in 3102, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=3102)
    0.01634027 = product of:
      0.03268054 = sum of:
        0.03268054 = weight(_text_:web in 3102) [ClassicSimilarity], result of:
          0.03268054 = score(doc=3102,freq=2.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.21634221 = fieldWeight in 3102, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.046875 = fieldNorm(doc=3102)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

Dewey Decimal Classification has been used to organize materials owned by the three scientific libraries at the University of Pavia, and to allow integrated browsing in their union catalogue through SciGator, a home built web-based user interface. Classification acts as a bridge between collections located in different places and shelved according to different local schemes. Furthermore, cross-discipline relationships recorded in the system allow for expanded queries that increase recall. Advantages and possible improvements of such a system are discussed.

Theme

Klassifikationssysteme im Online-Retrieval
Semantisches Umfeld in Indexierung u. Retrieval

0.012686389 = product of:
  0.06343194 = sum of:
    0.06343194 = weight(_text_:retrieval in 1164) [ClassicSimilarity], result of:
      0.06343194 = score(doc=1164,freq=30.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.45303512 = fieldWeight in 1164, product of:
          5.477226 = tf(freq=30.0), with freq of:
            30.0 = termFreq=30.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.02734375 = fieldNorm(doc=1164)
  0.2 = coord(1/5)

Abstract

The explosive growth of the Internet and other sources of networked information have made automatic mediation of access to networked information sources an increasingly important problem. Much of this information is expressed as electronic text, and it is becoming practical to automatically convert some printed documents and recorded speech to electronic text as well. Thus, automated systems capable of detecting useful documents are finding widespread application. With even a small number of languages it can be inconvenient to issue the same query repeatedly in every language, so users who are able to read more than one language will likely prefer a multilingual text retrieval system over a collection of monolingual systems. And since reading ability in a language does not always imply fluent writing ability in that language, such users will likely find cross-language text retrieval particularly useful for languages in which they are less confident of their ability to express their information needs effectively. The use of such systems can be also be beneficial if the user is able to read only a single language. For example, when only a small portion of the document collection will ever be examined by the user, performing retrieval before translation can be significantly more economical than performing translation before retrieval. So when the application is sufficiently important to justify the time and effort required for translation, those costs can be minimized if an effective cross-language text retrieval system is available. Even when translation is not available, there are circumstances in which cross-language text retrieval could be useful to a monolingual user. For example, a researcher might find a paper published in an unfamiliar language useful if that paper contains references to works by the same author that are in the researcher's native language.
Multilingual text retrieval can be defined as selection of useful documents from collections that may contain several languages (English, French, Chinese, etc.). This formulation allows for the possibility that individual documents might contain more than one language, a common occurrence in some applications. Both cross-language and within-language retrieval are included in this formulation, but it is the cross-language aspect of the problem which distinguishes multilingual text retrieval from its well studied monolingual counterpart. At the SIGIR 96 workshop on "Cross-Linguistic Information Retrieval" the participants discussed the proliferation of terminology being used to describe the field and settled on "Cross-Language" as the best single description of the salient aspect of the problem. "Multilingual" was felt to be too broad, since that term has also been used to describe systems able to perform within-language retrieval in more than one language but that lack any cross-language capability. "Cross-lingual" and "cross-linguistic" were felt to be equally good descriptions of the field, but "crosslanguage" was selected as the preferred term in the interest of standardization. Unfortunately, at about the same time the U.S. Defense Advanced Research Projects Agency (DARPA) introduced "translingual" as their preferred term, so we are still some distance from reaching consensus on this matter.
I will not attempt to draw a sharp distinction between retrieval and filtering in this survey. Although my own work on adaptive cross-language text filtering has led me to make this distinction fairly carefully in other presentations (c.f., (Oard 1997b)), such an proach does little to help understand the fundamental techniques which have been applied or the results that have been obtained in this case. Since it is still common to view filtering (detection of useful documents in dynamic document streams) as a kind of retrieval, will simply adopt that perspective here.

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.01255627 = product of:
  0.06278135 = sum of:
    0.06278135 = weight(_text_:retrieval in 919) [ClassicSimilarity], result of:
      0.06278135 = score(doc=919,freq=10.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.44838852 = fieldWeight in 919, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=919)
  0.2 = coord(1/5)

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

0.012504158 = product of:
  0.031260394 = sum of:
    0.01871778 = weight(_text_:retrieval in 3447) [ClassicSimilarity], result of:
      0.01871778 = score(doc=3447,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.13368362 = fieldWeight in 3447, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.03125 = fieldNorm(doc=3447)
    0.012542613 = product of:
      0.025085226 = sum of:
        0.025085226 = weight(_text_:22 in 3447) [ClassicSimilarity], result of:
          0.025085226 = score(doc=3447,freq=2.0), product of:
            0.16209066 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04628742 = 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)
  0.4 = coord(2/5)

Date

22. 2.2017 10:16:49

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.011230669 = product of:
  0.056153342 = sum of:
    0.056153342 = weight(_text_:retrieval in 966) [ClassicSimilarity], result of:
      0.056153342 = score(doc=966,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.40105087 = fieldWeight in 966, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.09375 = fieldNorm(doc=966)
  0.2 = coord(1/5)

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.00935889 = product of:
  0.04679445 = sum of:
    0.04679445 = weight(_text_:retrieval in 5346) [ClassicSimilarity], result of:
      0.04679445 = score(doc=5346,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.33420905 = fieldWeight in 5346, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.078125 = fieldNorm(doc=5346)
  0.2 = coord(1/5)

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.007487112 = product of:
  0.03743556 = sum of:
    0.03743556 = weight(_text_:retrieval in 2221) [ClassicSimilarity], result of:
      0.03743556 = score(doc=2221,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.26736724 = fieldWeight in 2221, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0625 = fieldNorm(doc=2221)
  0.2 = coord(1/5)

Theme

Semantisches Umfeld in Indexierung u. Retrieval