Search (80 results, page 1 of 4)

0.035472404 = product of:
  0.10641721 = sum of:
    0.10641721 = sum of:
      0.082673214 = weight(_text_:index in 1633) [ClassicSimilarity], result of:
        0.082673214 = score(doc=1633,freq=10.0), product of:
          0.21880072 = queryWeight, product of:
            4.369764 = idf(docFreq=1520, maxDocs=44218)
            0.050071523 = queryNorm
          0.37784708 = fieldWeight in 1633, product of:
            3.1622777 = tf(freq=10.0), with freq of:
              10.0 = termFreq=10.0
            4.369764 = idf(docFreq=1520, maxDocs=44218)
            0.02734375 = fieldNorm(doc=1633)
      0.023743998 = weight(_text_:22 in 1633) [ClassicSimilarity], result of:
        0.023743998 = score(doc=1633,freq=2.0), product of:
          0.17534193 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.050071523 = queryNorm
          0.1354154 = fieldWeight in 1633, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.02734375 = fieldNorm(doc=1633)
  0.33333334 = coord(1/3)

Abstract

Purpose - The purpose of this paper is to improve the conceptual-based search by incorporating structural ontological information such as concepts and relations. Generally, Semantic-based information retrieval aims to identify relevant information based on the meanings of the query terms or on the context of the terms and the performance of semantic information retrieval is carried out through standard measures-precision and recall. Higher precision leads to the (meaningful) relevant documents obtained and lower recall leads to the less coverage of the concepts. Design/methodology/approach - In this paper, the authors enhance the existing ontology-based indexing proposed by Kohler et al., by incorporating sibling information to the index. The index designed by Kohler et al., contains only super and sub-concepts from the ontology. In addition, in our approach, we focus on two tasks; query expansion and ranking of the expanded queries, to improve the efficiency of the ontology-based search. The aforementioned tasks make use of ontological concepts, and relations existing between those concepts so as to obtain semantically more relevant search results for a given query. Findings - The proposed ontology-based indexing technique is investigated by analysing the coverage of concepts that are being populated in the index. Here, we introduce a new measure called index enhancement measure, to estimate the coverage of ontological concepts being indexed. We have evaluated the ontology-based search for the tourism domain with the tourism documents and tourism-specific ontology. The comparison of search results based on the use of ontology "with and without query expansion" is examined to estimate the efficiency of the proposed query expansion task. The ranking is compared with the ORank system to evaluate the performance of our ontology-based search. From these analyses, the ontology-based search results shows better recall when compared to the other concept-based search systems. The mean average precision of the ontology-based search is found to be 0.79 and the recall is found to be 0.65, the ORank system has the mean average precision of 0.62 and the recall is found to be 0.51, while the concept-based search has the mean average precision of 0.56 and the recall is found to be 0.42. Practical implications - When the concept is not present in the domain-specific ontology, the concept cannot be indexed. When the given query term is not available in the ontology then the term-based results are retrieved. Originality/value - In addition to super and sub-concepts, we incorporate the concepts present in same level (siblings) to the ontological index. The structural information from the ontology is determined for the query expansion. The ranking of the documents depends on the type of the query (single concept query, multiple concept queries and concept with relation queries) and the ontological relations that exists in the query and the documents. With this ontological structural information, the search results showed us better coverage of concepts with respect to the query.

Date

20. 1.2015 18:30:22

0.032439798 = product of:
  0.097319394 = sum of:
    0.097319394 = weight(_text_:citation in 535) [ClassicSimilarity], result of:
      0.097319394 = score(doc=535,freq=8.0), product of:
        0.23479973 = queryWeight, product of:
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.050071523 = queryNorm
        0.4144783 = fieldWeight in 535, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.03125 = fieldNorm(doc=535)
  0.33333334 = coord(1/3)

Abstract

Literature-based discovery (LBD) research aims at finding effective computational methods for predicting previously unknown connections between clusters of research papers from disparate research areas. Existing methods encompass two general approaches. The first approach searches for these unknown connections by examining the textual contents of research papers. In addition to the existing textual features, the second approach incorporates structural features of scientific literatures, such as citation structures. These approaches, however, have not considered research papers' latent bibliographic metadata structures as important features that can be used for predicting previously unknown relationships between them. This thesis investigates a new graph-based LBD method that exploits the latent bibliographic metadata connections between pairs of research papers. The heterogeneous bibliographic information network is proposed as an efficient graph-based data structure for modeling the complex relationships between these metadata. In contrast to previous approaches, this method seamlessly combines textual and citation information in the form of pathbased metadata features for predicting future co-citation links between research papers from disparate research fields. The results reported in this thesis provide evidence that the method is effective for reconstructing the historical literature-based discovery hypotheses. This thesis also investigates the effects of semantic modeling and topic modeling on the performance of the proposed method. For semantic modeling, a general-purpose word sense disambiguation technique is proposed to reduce the lexical ambiguity in the title and abstract of research papers. The experimental results suggest that the reduced lexical ambiguity did not necessarily lead to a better performance of the method. This thesis discusses some of the possible contributing factors to these results. Finally, topic modeling is used for learning the latent topical relations between research papers. The learned topic model is incorporated into the heterogeneous bibliographic information network graph and allows new predictive features to be learned. The results in this thesis suggest that topic modeling improves the performance of the proposed method by increasing the overall accuracy for predicting the future co-citation links between disparate research papers.

0.028093692 = product of:
  0.08428107 = sum of:
    0.08428107 = weight(_text_:citation in 2659) [ClassicSimilarity], result of:
      0.08428107 = score(doc=2659,freq=6.0), product of:
        0.23479973 = queryWeight, product of:
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.050071523 = queryNorm
        0.35894877 = fieldWeight in 2659, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.03125 = fieldNorm(doc=2659)
  0.33333334 = coord(1/3)

Abstract

The field of knowledge organization (KO) can be described as composed of the four distinct but connected layers of theory, systems, representation, and application. This paper focuses on the relations between KO theory and KO systems. It is acknowledged how the structure of KO systems is the product of a mixture of ontological, epistemological, and pragmatical factors. However, different systems give different priorities to each factor. A more ontologically-oriented approach, though not offering quick solutions for any particular group of users, will produce systems of wide and long-lasting application as they are based on general, shareable principles. I take the case of the ontological theory of integrative levels, which has been considered as a useful source for general classifications for several decades, and is currently implemented in the Integrative Levels Classification system. The theory produces a sequence of main classes modelling a natural order between phenomena. This order has interesting effects also on other features of the system, like the citation order of concepts within compounds. As it has been shown by facet analytical theory, it is useful that citation order follow a principle of inversion, as compared to the order of the same concepts in the schedules. In the light of integrative levels theory, this principle also acquires an ontological meaning: phenomena of lower level should be cited first, as most often they act as specifications of higher-level ones. This ontological principle should be complemented by consideration of the epistemological treatment of phenomena: in case a lower-level phenomenon is the main theme, it can be promoted to the leading position in the compound subject heading. The integration of these principles is believed to produce optimal results in the ordering of knowledge contents.

Footnote

Übers. des Titels: Ontological foundations in knowledge organization: the theory of integrative levels applied in citation order.

0.026508953 = product of:
  0.07952686 = sum of:
    0.07952686 = product of:
      0.23858055 = sum of:
        0.23858055 = weight(_text_:3a in 400) [ClassicSimilarity], result of:
          0.23858055 = score(doc=400,freq=2.0), product of:
            0.42450693 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.050071523 = queryNorm
            0.56201804 = fieldWeight in 400, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.046875 = fieldNorm(doc=400)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Content

Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.

0.024395581 = product of:
  0.07318674 = sum of:
    0.07318674 = product of:
      0.14637348 = sum of:
        0.14637348 = weight(_text_:index in 3397) [ClassicSimilarity], result of:
          0.14637348 = score(doc=3397,freq=6.0), product of:
            0.21880072 = queryWeight, product of:
              4.369764 = idf(docFreq=1520, maxDocs=44218)
              0.050071523 = queryNorm
            0.6689808 = fieldWeight in 3397, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              4.369764 = idf(docFreq=1520, maxDocs=44218)
              0.0625 = fieldNorm(doc=3397)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

EPC Exhibit 129-36.1 presented intermediate results of a project to connect Relative Index terms to topics associated with classes and to determine if those Relative Index terms approximated the whole of the corresponding class or were in standing room in the class. The Relative Index project constitutes the first stage of a long(er)-term project to instill a more systematic treatment of relationships within the DDC. The present exhibit sets out a plan of study for that long-term project.

0.023130126 = product of:
  0.06939038 = sum of:
    0.06939038 = sum of:
      0.042254377 = weight(_text_:index in 4399) [ClassicSimilarity], result of:
        0.042254377 = score(doc=4399,freq=2.0), product of:
          0.21880072 = queryWeight, product of:
            4.369764 = idf(docFreq=1520, maxDocs=44218)
            0.050071523 = queryNorm
          0.1931181 = fieldWeight in 4399, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            4.369764 = idf(docFreq=1520, maxDocs=44218)
            0.03125 = fieldNorm(doc=4399)
      0.027136 = weight(_text_:22 in 4399) [ClassicSimilarity], result of:
        0.027136 = score(doc=4399,freq=2.0), product of:
          0.17534193 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.050071523 = queryNorm
          0.15476047 = fieldWeight in 4399, 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=4399)
  0.33333334 = coord(1/3)

Abstract

Indexing plays a vital role in Information Retrieval. With the availability of huge volume of information, it has become necessary to index the information in such a way to make easier for the end users to find the information they want efficiently and accurately. Keyword-based indexing uses words as indexing terms. It is not capable of capturing the implicit relation among terms or the semantics of the words in the document. To eliminate this limitation, ontology-based indexing came into existence, which allows semantic based indexing to solve complex and indirect user queries. Ontologies are used for document indexing which allows semantic based information retrieval. Existing ontologies or the ones constructed from scratch are used presently for indexing. Constructing ontologies from scratch is a labor-intensive task and requires extensive domain knowledge whereas use of an existing ontology may leave some important concepts in documents un-annotated. Using multiple ontologies can overcome the problem of missing out concepts to a great extent, but it is difficult to manage (changes in ontologies over time by their developers) multiple ontologies and ontology heterogeneity also arises due to ontologies constructed by different ontology developers. One possible solution to managing multiple ontologies and build from scratch is to use modular ontologies for indexing.

Date

20. 1.2015 18:30:22

0.020274874 = product of:
  0.06082462 = sum of:
    0.06082462 = weight(_text_:citation in 3437) [ClassicSimilarity], result of:
      0.06082462 = score(doc=3437,freq=2.0), product of:
        0.23479973 = queryWeight, product of:
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.050071523 = queryNorm
        0.25904894 = fieldWeight in 3437, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3437)
  0.33333334 = coord(1/3)

Abstract

The idea of constructing science maps based on bibliographic data has intrigued researchers for decades, and various techniques have been developed to map the structure of research disciplines. Most science mapping studies use a single method. However, as research fields have various properties, a valid map of a field should actually be composed of a set of maps derived from a series of investigations using different methods. That leads to the question of what can be learned from a combination-triangulation-of these different science maps. In this paper we propose a method for triangulation, using the example of water science. We combine three different mapping approaches: journal-journal citation relations (JJCR), shared author keywords (SAK), and title word-cited reference co-occurrence (TWRC). Our results demonstrate that triangulation of JJCR, SAK, and TWRC produces a more comprehensive picture than each method applied individually. The outcomes from the three different approaches can be associated with each other and systematically interpreted to provide insights into the complex multidisciplinary structure of the field of water research.

0.020274874 = product of:
  0.06082462 = sum of:
    0.06082462 = weight(_text_:citation in 105) [ClassicSimilarity], result of:
      0.06082462 = score(doc=105,freq=2.0), product of:
        0.23479973 = queryWeight, product of:
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.050071523 = queryNorm
        0.25904894 = fieldWeight in 105, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6892867 = idf(docFreq=1104, maxDocs=44218)
          0.0390625 = fieldNorm(doc=105)
  0.33333334 = coord(1/3)

Abstract

The purpose of this paper is to provide a critical analysis from an ethical perspective of how the concept of indigenous wayfinding and voyaging is mapped in knowledge representation, organization and discovery systems. Design/methodology/approach In this study, the Dewey Decimal Classification, the Library of Congress Subject Headings, the Library of Congress Classifications systems and the Web of Science citation database were methodically examined to determine how these systems represent and facilitate the discovery of indigenous knowledge of wayfinding and voyaging. Findings The analysis revealed that there was no dedicated representation of the indigenous practices of wayfinding and voyaging in the major knowledge representation, organization and discovery systems. By scattering indigenous practice across various, often very broad and unrelated classes, coherence in the record is disrupted, resulting in misrepresentation of these indigenous concepts. Originality/value This study contributes to a relatively limited research literature on representation and organization of indigenous knowledge of wayfinding and voyaging. This study calls to foster a better understanding and appreciation for the rich knowledge that indigenous cultures provide for an enlightened society.

0.019918907 = product of:
  0.05975672 = sum of:
    0.05975672 = product of:
      0.11951344 = sum of:
        0.11951344 = weight(_text_:index in 436) [ClassicSimilarity], result of:
          0.11951344 = score(doc=436,freq=4.0), product of:
            0.21880072 = queryWeight, product of:
              4.369764 = idf(docFreq=1520, maxDocs=44218)
              0.050071523 = queryNorm
            0.5462205 = fieldWeight in 436, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.369764 = idf(docFreq=1520, maxDocs=44218)
              0.0625 = fieldNorm(doc=436)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

This paper discusses the use of wiki technology to provide a navigation structure for a collection of newspaper clippings. We overview the architecture of the wiki, discuss the navigation structure and pose the question: is the navigation structure an index, and if so, what type, or is it just a linkage structure or topic map. Does such a distinction really matter? Are these definitions in reality function based?

0.017672636 = product of:
  0.053017907 = sum of:
    0.053017907 = product of:
      0.15905371 = sum of:
        0.15905371 = weight(_text_:3a in 701) [ClassicSimilarity], result of:
          0.15905371 = score(doc=701,freq=2.0), product of:
            0.42450693 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.050071523 = queryNorm
            0.3746787 = fieldWeight in 701, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.03125 = fieldNorm(doc=701)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Content

Vgl.: http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F1627&ei=tAtYUYrBNoHKtQb3l4GYBw&usg=AFQjCNHeaxKkKU3-u54LWxMNYGXaaDLCGw&sig2=8WykXWQoDKjDSdGtAakH2Q&bvm=bv.44442042,d.Yms.

0.017672636 = product of:
  0.053017907 = sum of:
    0.053017907 = product of:
      0.15905371 = sum of:
        0.15905371 = weight(_text_:3a in 5820) [ClassicSimilarity], result of:
          0.15905371 = score(doc=5820,freq=2.0), product of:
            0.42450693 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.050071523 = queryNorm
            0.3746787 = fieldWeight in 5820, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.03125 = fieldNorm(doc=5820)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Content

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Language and Information Technologies. Vgl.: https%3A%2F%2Fwww.cs.cmu.edu%2F~cx%2Fpapers%2Fknowledge_based_text_representation.pdf&usg=AOvVaw0SaTSvhWLTh__Uz_HtOtl3.

0.017250279 = product of:
  0.051750835 = sum of:
    0.051750835 = product of:
      0.10350167 = sum of:
        0.10350167 = weight(_text_:index in 3894) [ClassicSimilarity], result of:
          0.10350167 = score(doc=3894,freq=12.0), product of:
            0.21880072 = queryWeight, product of:
              4.369764 = idf(docFreq=1520, maxDocs=44218)
              0.050071523 = queryNorm
            0.47304082 = fieldWeight in 3894, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              4.369764 = idf(docFreq=1520, maxDocs=44218)
              0.03125 = fieldNorm(doc=3894)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

AGI - Information Management Consultants liefern seit nunmehr 16 Jahren eine Software zur Entwicklung von Thesauri und Klassifikationen, ehemals bezeichnet als INDEX, seit zweieinhalb Jahren als IC INDEX neu entwickelt. Solche Terminologien werden oft auch als Glossar, Lexikon, Topic Maps, RDF, semantisches Netz, Systematik, Aktenplan oder Nomenklatur bezeichnet. Die Software erlaubt zwar schon immer, dass solche terminologischen Werke mehrsprachig angelegt sind, doch es gab keine speziellen Werkzeuge, um die Übersetzung zu erleichtern. Die Globalisierung führt zunehmend auch zur Mehrsprachigkeit von Fachterminologien, wie laufende Projekte belegen. In IC INDEX 5.08 wurde deshalb ein spezieller Workflow für die Übersetzung implementiert, der Wortfelder bearbeitet und dabei weitgehend automatisch, aber vom Übersetzer kontrolliert, die richtigen Verbindungen zwischen den Termen in den anderen Sprachen erzeugt. Bereits dieser Workflow beschleunigt wesentlich die Übersetzungstätigkeit. Doch es geht noch schneller: der eTranslation Server von Linguatec generiert automatisch Übersetzungsvorschläge für Deutsch/English und Deutsch/Französisch. Demnächst auch Deutsch/Spanisch und Deutsch/Italienisch. Gerade bei Mehrwortbegriffen, Klassenbezeichnungen und Komposita spielt die automatische Übersetzung gegenüber dem Wörterbuch-Lookup ihre Stärke aus. Der Rückgriff ins Wörterbuch ist selbstverständlich auch implementiert, sowohl auf das Linguatec-Wörterbuch und zusätzlich jedes beliebige über eine URL adressierbare Wörterbuch. Jeder Übersetzungsvorschlag muss vom Terminologie-Entwickler bestätigt werden. Im Rahmen der Oualitätskontrolle haben wir anhand vorliegender mehrsprachiger Thesauri getestet mit dem Ergebnis, dass die automatischen Vorschläge oft gleich und fast immer sehr nahe an der gewünschten Übersetzung waren. Worte, die für durchschnittlich gebildete Menschen nicht mehr verständlich sind, bereiten auch der maschinellen Übersetzung Probleme, z.B. Fachbegriffe aus Medizin, Chemie und anderen Wissenschaften. Aber auch ein Humanübersetzer wäre hier ohne einschlägige Fachausbildung überfordert. Also, ohne Fach- und ohne Sprachkompetenz geht es nicht, aber mit geht es ziemlich flott. IC INDEX basiert auf Lotus Notes & Domino 5.08. Beliebige Relationen zwischen Termen sind zulässig, die ANSI-Normen sind implementiert und um zusätzliche Relationen ergänzt, 26 Relationen gehören zum Lieferumfang. Ausgaben gemäß Topic Maps oder RDF - zwei eng verwandte Normen-werden bei Nachfrage entwickelt. Ausgaben sind in HMTL, XML, eine ansprechende Druckversion unter MS Word 2000 und für verschiedene Search-Engines vorhanden. AGI - Information Management Consultants, Neustadt an der Weinstraße, beraten seit 1983 Unternehmen und Organisationen im dem heute als Knowledge Management bezeichneten Feld. Seit 1994 liefern sie eine umfassende, hochintegrative Lösung: "Information Center" - darin ist IC INDEX ein eigenständiges Modul zur Unterstützung von mehrsprachiger Indexierung und mehrsprachigem semantischem Retrieval. Linguatec, München, ist einstmals aus den linguistischen Forschungslabors von IBM hervorgegangen und ist über den Personal Translator weithin bekannt.

Object

Index

0.015247239 = product of:
  0.045741715 = sum of:
    0.045741715 = product of:
      0.09148343 = sum of:
        0.09148343 = weight(_text_:index in 231) [ClassicSimilarity], result of:
          0.09148343 = score(doc=231,freq=6.0), product of:
            0.21880072 = queryWeight, product of:
              4.369764 = idf(docFreq=1520, maxDocs=44218)
              0.050071523 = queryNorm
            0.418113 = fieldWeight in 231, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              4.369764 = idf(docFreq=1520, maxDocs=44218)
              0.0390625 = fieldNorm(doc=231)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

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

0.01493918 = product of:
  0.044817537 = sum of:
    0.044817537 = product of:
      0.089635074 = sum of:
        0.089635074 = weight(_text_:index in 1638) [ClassicSimilarity], result of:
          0.089635074 = score(doc=1638,freq=4.0), product of:
            0.21880072 = queryWeight, product of:
              4.369764 = idf(docFreq=1520, maxDocs=44218)
              0.050071523 = queryNorm
            0.40966535 = fieldWeight in 1638, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.369764 = idf(docFreq=1520, maxDocs=44218)
              0.046875 = fieldNorm(doc=1638)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

The Web of Data keeps growing rapidly. However, the full exploitation of this large amount of structured data faces numerous challenges like usability, scalability, imprecise information needs and data change. We present Semplore, an IR-based system that aims at addressing these issues. Semplore supports intuitive faceted search and complex queries both on text and structured data. It combines imprecise keyword search and precise structured query in a unified ranking scheme. Scalable query processing is supported by leveraging inverted indexes traditionally used in IR systems. This is combined with a novel block-based index structure to support efficient index update when data changes. The experimental results show that Semplore is an efficient and effective system for searching the Web of Data and can be used as a basic infrastructure for Web-scale Semantic Web search engines.

0.012324194 = product of:
  0.036972582 = sum of:
    0.036972582 = product of:
      0.073945165 = sum of:
        0.073945165 = weight(_text_:index in 3530) [ClassicSimilarity], result of:
          0.073945165 = score(doc=3530,freq=2.0), product of:
            0.21880072 = queryWeight, product of:
              4.369764 = idf(docFreq=1520, maxDocs=44218)
              0.050071523 = queryNorm
            0.33795667 = fieldWeight in 3530, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.369764 = idf(docFreq=1520, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3530)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

Classes in the Dewey Decimal Classification (DDC) system function as neighborhoods around focal topics in captions and notes. Topical neighborhoods are generated through specialization and instantiation, complex topic synthesis, index terms and mapped headings, hierarchical force, rules for choosing between numbers, development of the DDC over time, and use of the system in classifying resources. Implications of representation using a formal knowledge representation language are explored.

0.011306668 = product of:
  0.03392 = sum of:
    0.03392 = product of:
      0.06784 = sum of:
        0.06784 = weight(_text_:22 in 6089) [ClassicSimilarity], result of:
          0.06784 = score(doc=6089,freq=2.0), product of:
            0.17534193 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050071523 = queryNorm
            0.38690117 = fieldWeight in 6089, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=6089)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Pages

S.11-22

0.011306668 = product of:
  0.03392 = sum of:
    0.03392 = product of:
      0.06784 = sum of:
        0.06784 = weight(_text_:22 in 5576) [ClassicSimilarity], result of:
          0.06784 = score(doc=5576,freq=2.0), product of:
            0.17534193 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050071523 = queryNorm
            0.38690117 = fieldWeight in 5576, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=5576)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Date

13.12.2017 14:17:22

0.011306668 = product of:
  0.03392 = sum of:
    0.03392 = product of:
      0.06784 = sum of:
        0.06784 = weight(_text_:22 in 539) [ClassicSimilarity], result of:
          0.06784 = score(doc=539,freq=2.0), product of:
            0.17534193 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050071523 = queryNorm
            0.38690117 = fieldWeight in 539, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=539)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Date

26.12.2011 13:22:07

0.011306668 = product of:
  0.03392 = sum of:
    0.03392 = product of:
      0.06784 = sum of:
        0.06784 = weight(_text_:22 in 3406) [ClassicSimilarity], result of:
          0.06784 = score(doc=3406,freq=2.0), product of:
            0.17534193 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050071523 = queryNorm
            0.38690117 = fieldWeight in 3406, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=3406)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Date

30. 5.2010 16:22:35

0.011306668 = product of:
  0.03392 = sum of:
    0.03392 = product of:
      0.06784 = sum of:
        0.06784 = weight(_text_:22 in 4523) [ClassicSimilarity], result of:
          0.06784 = score(doc=4523,freq=2.0), product of:
            0.17534193 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050071523 = queryNorm
            0.38690117 = fieldWeight in 4523, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=4523)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Source

Spektrum der Wissenschaft. 2018, H.1, S.22-27