Search (10 results, page 1 of 1)

  • × theme_ss:"Semantisches Umfeld in Indexierung u. Retrieval"
  • × theme_ss:"Wissensrepräsentation"
  • × year_i:[2000 TO 2010}
  1. Knorz, G.; Rein, B.: Semantische Suche in einer Hochschulontologie (2005) 0.04
    0.035079926 = product of:
      0.07015985 = sum of:
        0.008834538 = weight(_text_:in in 1852) [ClassicSimilarity], result of:
          0.008834538 = score(doc=1852,freq=4.0), product of:
            0.059380736 = queryWeight, product of:
              1.3602545 = idf(docFreq=30841, maxDocs=44218)
              0.043654136 = queryNorm
            0.14877784 = fieldWeight in 1852, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.3602545 = idf(docFreq=30841, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1852)
        0.040624447 = weight(_text_:und in 1852) [ClassicSimilarity], result of:
          0.040624447 = score(doc=1852,freq=12.0), product of:
            0.09675359 = queryWeight, product of:
              2.216367 = idf(docFreq=13101, maxDocs=44218)
              0.043654136 = queryNorm
            0.41987535 = fieldWeight in 1852, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              2.216367 = idf(docFreq=13101, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1852)
        0.020700864 = product of:
          0.04140173 = sum of:
            0.04140173 = weight(_text_:22 in 1852) [ClassicSimilarity], result of:
              0.04140173 = score(doc=1852,freq=2.0), product of:
                0.15286934 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.043654136 = queryNorm
                0.2708308 = fieldWeight in 1852, 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=1852)
          0.5 = coord(1/2)
      0.5 = coord(3/6)
    
    Abstract
    Ontologien werden eingesetzt, um durch semantische Fundierung insbesondere für das Dokumentenretrieval eine grundlegend bessere Basis zu haben, als dies gegenwärtiger Stand der Technik ist. Vorgestellt wird eine an der FH Darmstadt entwickelte und eingesetzte Ontologie, die den Gegenstandsbereich Hochschule sowohl breit abdecken und gleichzeitig differenziert semantisch beschreiben soll. Das Problem der semantischen Suche besteht nun darin, dass sie für Informationssuchende so einfach wie bei gängigen Suchmaschinen zu nutzen sein soll, und gleichzeitig auf der Grundlage des aufwendigen Informationsmodells hochwertige Ergebnisse liefern muss. Es wird beschrieben, welche Möglichkeiten die verwendete Software K-Infinity bereitstellt und mit welchem Konzept diese Möglichkeiten für eine semantische Suche nach Dokumenten und anderen Informationseinheiten (Personen, Veranstaltungen, Projekte etc.) eingesetzt werden.
    Date
    11. 2.2011 18:22:58
    Source
    Information - Wissenschaft und Praxis. 56(2005) H.5/6, S.281-290
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  2. Knorz, G.; Rein, B.: Semantische Suche in einer Hochschulontologie : Ontologie-basiertes Information-Filtering und -Retrieval mit relationalen Datenbanken (2005) 0.04
    0.035079926 = product of:
      0.07015985 = sum of:
        0.008834538 = weight(_text_:in in 4324) [ClassicSimilarity], result of:
          0.008834538 = score(doc=4324,freq=4.0), product of:
            0.059380736 = queryWeight, product of:
              1.3602545 = idf(docFreq=30841, maxDocs=44218)
              0.043654136 = queryNorm
            0.14877784 = fieldWeight in 4324, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.3602545 = idf(docFreq=30841, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4324)
        0.040624447 = weight(_text_:und in 4324) [ClassicSimilarity], result of:
          0.040624447 = score(doc=4324,freq=12.0), product of:
            0.09675359 = queryWeight, product of:
              2.216367 = idf(docFreq=13101, maxDocs=44218)
              0.043654136 = queryNorm
            0.41987535 = fieldWeight in 4324, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              2.216367 = idf(docFreq=13101, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4324)
        0.020700864 = product of:
          0.04140173 = sum of:
            0.04140173 = weight(_text_:22 in 4324) [ClassicSimilarity], result of:
              0.04140173 = score(doc=4324,freq=2.0), product of:
                0.15286934 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.043654136 = 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)
      0.5 = coord(3/6)
    
    Abstract
    Ontologien werden eingesetzt, um durch semantische Fundierung insbesondere für das Dokumentenretrieval eine grundlegend bessere Basis zu haben, als dies gegenwärtiger Stand der Technik ist. Vorgestellt wird eine an der FH Darmstadt entwickelte und eingesetzte Ontologie, die den Gegenstandsbereich Hochschule sowohl breit abdecken und gleichzeitig differenziert semantisch beschreiben soll. Das Problem der semantischen Suche besteht nun darin, dass sie für Informationssuchende so einfach wie bei gängigen Suchmaschinen zu nutzen sein soll, und gleichzeitig auf der Grundlage des aufwendigen Informationsmodells hochwertige Ergebnisse liefern muss. Es wird beschrieben, welche Möglichkeiten die verwendete Software K-Infinity bereitstellt und mit welchem Konzept diese Möglichkeiten für eine semantische Suche nach Dokumenten und anderen Informationseinheiten (Personen, Veranstaltungen, Projekte etc.) eingesetzt werden.
    Date
    11. 2.2011 18:22:25
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  3. Weiermann, S.L.: Semantische Netze und Begriffsdeskription in der Wissensrepräsentation (2000) 0.01
    0.0146632595 = product of:
      0.043989778 = sum of:
        0.010820055 = weight(_text_:in in 3001) [ClassicSimilarity], result of:
          0.010820055 = score(doc=3001,freq=6.0), product of:
            0.059380736 = queryWeight, product of:
              1.3602545 = idf(docFreq=30841, maxDocs=44218)
              0.043654136 = queryNorm
            0.1822149 = fieldWeight in 3001, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.3602545 = idf(docFreq=30841, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3001)
        0.03316972 = weight(_text_:und in 3001) [ClassicSimilarity], result of:
          0.03316972 = score(doc=3001,freq=8.0), product of:
            0.09675359 = queryWeight, product of:
              2.216367 = idf(docFreq=13101, maxDocs=44218)
              0.043654136 = queryNorm
            0.34282678 = fieldWeight in 3001, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              2.216367 = idf(docFreq=13101, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3001)
      0.33333334 = coord(2/6)
    
    BK
    18.00 Einzelne Sprachen und Literaturen allgemein
    Classification
    18.00 Einzelne Sprachen und Literaturen allgemein
    Content
    Inhalt (in Kürze): Einleitung. Wissensrepräsentation. Semantische Netze. Wissensrepräsentationssysteme. Empirische Analyse und Systemerweiterungen.
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  4. Hoang, H.H.; Tjoa, A.M: ¬The state of the art of ontology-based query systems : a comparison of existing approaches (2006) 0.00
    0.0026606917 = product of:
      0.01596415 = sum of:
        0.01596415 = weight(_text_:in in 792) [ClassicSimilarity], result of:
          0.01596415 = score(doc=792,freq=10.0), product of:
            0.059380736 = queryWeight, product of:
              1.3602545 = idf(docFreq=30841, maxDocs=44218)
              0.043654136 = queryNorm
            0.26884392 = fieldWeight in 792, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.3602545 = idf(docFreq=30841, maxDocs=44218)
              0.0625 = fieldNorm(doc=792)
      0.16666667 = coord(1/6)
    
    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
  5. Calegari, S.; Sanchez, E.: Object-fuzzy concept network : an enrichment of ontologies in semantic information retrieval (2008) 0.00
    0.0022310577 = product of:
      0.0133863455 = sum of:
        0.0133863455 = weight(_text_:in in 2393) [ClassicSimilarity], result of:
          0.0133863455 = score(doc=2393,freq=18.0), product of:
            0.059380736 = queryWeight, product of:
              1.3602545 = idf(docFreq=30841, maxDocs=44218)
              0.043654136 = queryNorm
            0.22543246 = fieldWeight in 2393, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              1.3602545 = idf(docFreq=30841, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2393)
      0.16666667 = coord(1/6)
    
    Abstract
    This article shows how a fuzzy ontology-based approach can improve semantic documents retrieval. After formally defining a fuzzy ontology and a fuzzy knowledge base, a special type of new fuzzy relationship called (semantic) correlation, which links the concepts or entities in a fuzzy ontology, is discussed. These correlations, first assigned by experts, are updated after querying or when a document has been inserted into a database. Moreover, in order to define a dynamic knowledge of a domain adapting itself to the context, it is shown how to handle a tradeoff between the correct definition of an object, taken in the ontology structure, and the actual meaning assigned by individuals. The notion of a fuzzy concept network is extended, incorporating database objects so that entities and documents can similarly be represented in the network. Information retrieval (IR) algorithm, using an object-fuzzy concept network (O-FCN), is introduced and described. This algorithm allows us to derive a unique path among the entities involved in the query to obtain maxima semantic associations in the knowledge domain. Finally, the study has been validated by querying a database using fuzzy recall, fuzzy precision, and coefficient variant measures in the crisp and fuzzy cases.
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  6. Drexel, G.: Knowledge engineering for intelligent information retrieval (2001) 0.00
    0.0019955188 = product of:
      0.011973113 = sum of:
        0.011973113 = weight(_text_:in in 4043) [ClassicSimilarity], result of:
          0.011973113 = score(doc=4043,freq=10.0), product of:
            0.059380736 = queryWeight, product of:
              1.3602545 = idf(docFreq=30841, maxDocs=44218)
              0.043654136 = queryNorm
            0.20163295 = fieldWeight in 4043, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.3602545 = idf(docFreq=30841, maxDocs=44218)
              0.046875 = fieldNorm(doc=4043)
      0.16666667 = coord(1/6)
    
    Abstract
    This paper presents a clustered approach to designing an overall ontological model together with a general rule-based component that serves as a mapping device. By observational criteria, a multi-lingual team of experts excerpts concepts from general communication in the media. The team, then, finds equivalent expressions in English, German, French, and Spanish. On the basis of a set of ontological and lexical relations, a conceptual network is built up. Concepts are thought to be universal. Objects unique in time and space are identified by names and will be explained by the universals as their instances. Our approach relies on multi-relational descriptions of concepts. It provides a powerful tool for documentation and conceptual language learning. First and foremost, our multi-lingual, polyhierarchical ontology fills the gap of semantically-based information retrieval by generating enhanced and improved queries for internet search
    Series
    Lecture notes in computer science; vol.2004
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  7. Baofu, P.: ¬The future of information architecture : conceiving a better way to understand taxonomy, network, and intelligence (2008) 0.00
    0.0019676082 = product of:
      0.011805649 = sum of:
        0.011805649 = weight(_text_:in in 2257) [ClassicSimilarity], result of:
          0.011805649 = score(doc=2257,freq=14.0), product of:
            0.059380736 = queryWeight, product of:
              1.3602545 = idf(docFreq=30841, maxDocs=44218)
              0.043654136 = queryNorm
            0.19881277 = fieldWeight in 2257, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              1.3602545 = idf(docFreq=30841, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2257)
      0.16666667 = coord(1/6)
    
    Abstract
    The Future of Information Architecture examines issues surrounding why information is processed, stored and applied in the way that it has, since time immemorial. Contrary to the conventional wisdom held by many scholars in human history, the recurrent debate on the explanation of the most basic categories of information (eg space, time causation, quality, quantity) has been misconstrued, to the effect that there exists some deeper categories and principles behind these categories of information - with enormous implications for our understanding of reality in general. To understand this, the book is organised in to four main parts: Part I begins with the vital question concerning the role of information within the context of the larger theoretical debate in the literature. Part II provides a critical examination of the nature of data taxonomy from the main perspectives of culture, society, nature and the mind. Part III constructively invesitgates the world of information network from the main perspectives of culture, society, nature and the mind. Part IV proposes six main theses in the authors synthetic theory of information architecture, namely, (a) the first thesis on the simpleness-complicatedness principle, (b) the second thesis on the exactness-vagueness principle (c) the third thesis on the slowness-quickness principle (d) the fourth thesis on the order-chaos principle, (e) the fifth thesis on the symmetry-asymmetry principle, and (f) the sixth thesis on the post-human stage.
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  8. Wang, Y.-H.; Jhuo, P.-S.: ¬A semantic faceted search with rule-based inference (2009) 0.00
    0.0017848461 = product of:
      0.010709076 = sum of:
        0.010709076 = weight(_text_:in in 540) [ClassicSimilarity], result of:
          0.010709076 = score(doc=540,freq=8.0), product of:
            0.059380736 = queryWeight, product of:
              1.3602545 = idf(docFreq=30841, maxDocs=44218)
              0.043654136 = queryNorm
            0.18034597 = fieldWeight in 540, 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=540)
      0.16666667 = coord(1/6)
    
    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
  9. Prieto-Díaz, R.: ¬A faceted approach to building ontologies (2002) 0.00
    0.0015457221 = product of:
      0.009274333 = sum of:
        0.009274333 = weight(_text_:in in 2259) [ClassicSimilarity], result of:
          0.009274333 = score(doc=2259,freq=6.0), product of:
            0.059380736 = queryWeight, product of:
              1.3602545 = idf(docFreq=30841, maxDocs=44218)
              0.043654136 = queryNorm
            0.1561842 = fieldWeight in 2259, 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=2259)
      0.16666667 = coord(1/6)
    
    Abstract
    An ontology is "an explicit conceptualization of a domain of discourse, and thus provides a shared and common understanding of the domain." We have been producing ontologies for millennia to understand and explain our rationale and environment. From Plato's philosophical framework to modern day classification systems, ontologies are, in most cases, the product of extensive analysis and categorization. Only recently has the process of building ontologies become a research topic of interest. Today, ontologies are built very much ad-hoc. A terminology is first developed providing a controlled vocabulary for the subject area or domain of interest, then it is organized into a taxonomy where key concepts are identified, and finally these concepts are defined and related to create an ontology. The intent of this paper is to show that domain analysis methods can be used for building ontologies. Domain analysis aims at generic models that represent groups of similar systems within an application domain. In this sense, it deals with categorization of common objects and operations, with clear, unambiguous definitions of them and with defining their relationships.
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  10. Vallet, D.; Fernández, M.; Castells, P.: ¬An ontology-based information retrieval model (2005) 0.00
    0.0012620769 = product of:
      0.0075724614 = sum of:
        0.0075724614 = weight(_text_:in in 4708) [ClassicSimilarity], result of:
          0.0075724614 = score(doc=4708,freq=4.0), product of:
            0.059380736 = queryWeight, product of:
              1.3602545 = idf(docFreq=30841, maxDocs=44218)
              0.043654136 = queryNorm
            0.12752387 = fieldWeight in 4708, 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=4708)
      0.16666667 = coord(1/6)
    
    Series
    Lecture Notes in Computer Science ; 3532
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval