Search (15 results, page 1 of 1)

0.016037485 = product of:
  0.06414994 = sum of:
    0.06414994 = weight(_text_:retrieval in 667) [ClassicSimilarity], result of:
      0.06414994 = score(doc=667,freq=8.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.46789268 = fieldWeight in 667, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0546875 = fieldNorm(doc=667)
  0.25 = coord(1/4)

Abstract

We present an overview of the NTCIR Math Tasks organized during NTCIR-10, 11, and 12. These tasks are primarily dedicated to techniques for searching mathematical content with formula expressions. In this chapter, we first summarize the task design and introduce test collections generated in the tasks. We also describe the features and main challenges of mathematical information retrieval systems and discuss future perspectives in the field.

Series

¬The Information retrieval series, vol 43

Source

Evaluating information retrieval and access tasks. Eds.: Sakai, T., Oard, D., Kando, N. [https://doi.org/10.1007/978-981-15-5554-1_12]

0.013746415 = product of:
  0.05498566 = sum of:
    0.05498566 = weight(_text_:retrieval in 5365) [ClassicSimilarity], result of:
      0.05498566 = score(doc=5365,freq=8.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.40105087 = fieldWeight in 5365, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=5365)
  0.25 = coord(1/4)

Abstract

Wikipedia categories, a classification scheme built for organizing and describing Wikpedia articles, are being applied in computer science research. This paper adopts a systematic literature review approach, in order to identify different approaches and uses of Wikipedia categories in information retrieval research. Several types of work are identified, depending on the intrinsic study of the categories structure, or its use as a tool for the processing and analysis of other documentary corpus different to Wikipedia. Information retrieval is identified as one of the major areas of use, in particular its application in the refinement and improvement of search expressions, and the construction of textual corpus. However, the set of available works shows that in many cases research approaches applied and results obtained can be integrated into a comprehensive and inclusive concept of information retrieval.

0.012960245 = product of:
  0.05184098 = sum of:
    0.05184098 = weight(_text_:retrieval in 249) [ClassicSimilarity], result of:
      0.05184098 = score(doc=249,freq=4.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.37811437 = fieldWeight in 249, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0625 = fieldNorm(doc=249)
  0.25 = coord(1/4)

Abstract

This paper describes an approach to path-finding in the intelligent graphs, with vertices being intelligent agents. A possible implementation of this approach is described, based on logical inference in distributed frame hierarchy. Presented approach can be used for implementing distributed intelligent information systems that include automatic navigation and path generation in hypertext, which can be used, for example in distance education, as well as for organizing intelligent web catalogues with flexible ontology-based information retrieval.

0.011455346 = product of:
  0.045821384 = sum of:
    0.045821384 = weight(_text_:retrieval in 5732) [ClassicSimilarity], result of:
      0.045821384 = score(doc=5732,freq=8.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.33420905 = fieldWeight in 5732, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5732)
  0.25 = coord(1/4)

Abstract

Recent technological developments have increased the use of machine learning to solve many problems, including many in information retrieval. Multimedia information retrieval as a problem represents a significant challenge to machine learning as a technological solution, but some problems can still be addressed by using appropriate AI techniques. We review the technological developments and provide a perspective on the use of machine learning in conjunction with knowledge organization to address multimedia IR needs. The semantic gap in multimedia IR remains a significant problem in the field, and solutions to them are many years off. However, new technological developments allow the use of knowledge organization and machine learning in multimedia search systems and services. Specifically, we argue that, the improvement of detection of some classes of lowlevel features in images music and video can be used in conjunction with knowledge organization to tag or label multimedia content for better retrieval performance. We provide an overview of the use of knowledge organization schemes in machine learning and make recommendations to information professionals on the use of this technology with knowledge organization techniques to solve multimedia IR problems. We introduce a five-step process model that extracts features from multimedia objects (Step 1) from both knowledge organization (Step 1a) and machine learning (Step 1b), merging them together (Step 2) to create an index of those multimedia objects (Step 3). We also overview further steps in creating an application to utilize the multimedia objects (Step 4) and maintaining and updating the database of features on those objects (Step 5).

0.0091642765 = product of:
  0.036657106 = sum of:
    0.036657106 = weight(_text_:retrieval in 436) [ClassicSimilarity], result of:
      0.036657106 = score(doc=436,freq=2.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.26736724 = fieldWeight in 436, 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=436)
  0.25 = coord(1/4)

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.008018742 = product of:
  0.03207497 = sum of:
    0.03207497 = weight(_text_:retrieval in 5899) [ClassicSimilarity], result of:
      0.03207497 = score(doc=5899,freq=2.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.23394634 = fieldWeight in 5899, 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=5899)
  0.25 = coord(1/4)

Abstract

S.R. Ranganathan is credited with the introduction of the term "facet" in the field of knowledge organization towards the middle of the twentieth century. Facets have traditionally been used to organize document collections and to express complex subjects. In the digital world, they act as filters to facilitate navigation and improve retrieval. But the popularity of the term does not mean that a definitive characterization of the concept has been established. Indeed, several conceptualizations of the facet co-exist. This article provides an overview of formal and informal definitions found in the literature of knowledge organization, followed by a discussion of four common conceptualizations of the facet: process vs product, nature vs function, object vs subject and organization vs navigation.

0.008018742 = product of:
  0.03207497 = sum of:
    0.03207497 = weight(_text_:retrieval in 572) [ClassicSimilarity], result of:
      0.03207497 = score(doc=572,freq=2.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.23394634 = fieldWeight in 572, 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=572)
  0.25 = coord(1/4)

Abstract

We consider use of ontological background knowledge in intelligent information systems and analyze directions of their reduction in compliance with specifics of particular user task. Such reduction is aimed at simplification of knowledge processing without loss of significant information. We propose methods of generation of task thesauri based on domain ontology that contain such subset of ontological concepts and relations that can be used in task solving. Combinatorial optimization is used for minimization of task thesaurus. In this approach, semantic similarity estimates are used for determination of concept significance for user task. Some practical examples of optimized thesauri application for semantic retrieval and competence analysis demonstrate efficiency of proposed approach.

0.0068732076 = product of:
  0.02749283 = sum of:
    0.02749283 = weight(_text_:retrieval in 1111) [ClassicSimilarity], result of:
      0.02749283 = score(doc=1111,freq=2.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.20052543 = fieldWeight in 1111, 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=1111)
  0.25 = coord(1/4)

Abstract

In the process of searching for a particular course on e-learning platforms, it is required to browse through different platforms, and it becomes a time-consuming process. To resolve the issue, an ontology has been developed that can provide single-point access to all the e-learning platforms. The modelled ONline Course Ontology (ONCO) is based on YAMO, METHONTOLOGY and IDEF5 and built on the Protégé ontology editing tool. ONCO is integrated with sample data and later evaluated using pre-defined competency questions. Complex SPARQL queries are executed to identify the effectiveness of the constructed ontology. The modelled ontology is able to retrieve all the sampled queries. The ONCO has been developed for the efficient retrieval of similar courses from massive open online course (MOOC) platforms.

0.005727673 = product of:
  0.022910692 = sum of:
    0.022910692 = weight(_text_:retrieval in 5731) [ClassicSimilarity], result of:
      0.022910692 = score(doc=5731,freq=2.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.16710453 = fieldWeight in 5731, 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=5731)
  0.25 = coord(1/4)

Abstract

In this paper, we propose an ontology building method, called human-centric faceted approach for ontology construction (HCFOC). HCFOC uses the human-centric approach, improvised with the idea of selective dissemination of information (SDI), to deal with context. Further, this ontology construction process makes use of facet analysis and an analytico-synthetic classification approach. This novel fusion contributes to the originality of HCFOC and distinguishes it from other existing ontology construction methodologies. Based on HCFOC, an ontology of the tourism domain has been designed using the Protégé-5.5.0 ontology editor. The HCFOC methodology has provided the necessary flexibility, extensibility, robustness and has facilitated the capturing of background knowledge. It models the tourism ontology in such a way that it is able to deal with the context of a tourist's information need with precision. This is evident from the result that more than 90% of the user's queries were successfully met. The use of domain knowledge and techniques from both library and information science and computer science has helped in the realization of the desired purpose of this ontology construction process. It is envisaged that HCFOC will have implications for ontology developers. The demonstrated tourism ontology can support any tourism information retrieval system.

0.005727673 = product of:
  0.022910692 = sum of:
    0.022910692 = weight(_text_:retrieval in 592) [ClassicSimilarity], result of:
      0.022910692 = score(doc=592,freq=2.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.16710453 = fieldWeight in 592, 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=592)
  0.25 = coord(1/4)

Abstract

The purpose of this article is to identify the most important factors and features in the evolution of thesauri and ontologies through a dialectic model. This model relies on a dialectic process or idea which could be discovered via a dialectic method. This method has focused on identifying the logical relationship between a beginning proposition, or an idea called a thesis, a negation of that idea called the antithesis, and the result of the conflict between the two ideas, called a synthesis. During the creation of knowl­edge organization systems (KOSs), the identification of logical relations between different ideas has been made possible through the consideration and use of the most influential methods and tools such as dictionaries, Roget's Thesaurus, thesaurus, micro-, macro- and metathesauri, ontology, lower, middle and upper level ontologies. The analysis process has adapted a historical methodology, more specifically a dialectic method and documentary method as the reasoning process. This supports our arguments and synthesizes a method for the analysis of research results. Confirmed by the research results, the principle of unity has shown to be the most important factor in the development and evolution of the structure of knowl­edge organization systems and their types. There are various types of unity when considering the analysis of logical relations. These include the principle of unity of alphabetical order, unity of science, semantic unity, structural unity and conceptual unity. The results have clearly demonstrated a movement from plurality to unity in the assembling of the complex structure of knowl­edge organization systems to increase information and knowl­edge storage and retrieval performance.

0.005727673 = product of:
  0.022910692 = sum of:
    0.022910692 = weight(_text_:retrieval in 593) [ClassicSimilarity], result of:
      0.022910692 = score(doc=593,freq=2.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.16710453 = fieldWeight in 593, 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=593)
  0.25 = coord(1/4)

Abstract

The purpose of this article is to identify the most important factors and features in the evolution of thesauri and ontologies through a dialectic model. This model relies on a dialectic process or idea which could be discovered via a dialectic method. This method has focused on identifying the logical relationship between a beginning proposition, or an idea called a thesis, a negation of that idea called the antithesis, and the result of the conflict between the two ideas, called a synthesis. During the creation of knowl­edge organization systems (KOSs), the identification of logical relations between different ideas has been made possible through the consideration and use of the most influential methods and tools such as dictionaries, Roget's Thesaurus, thesaurus, micro-, macro- and metathesauri, ontology, lower, middle and upper level ontologies. The analysis process has adapted a historical methodology, more specifically a dialectic method and documentary method as the reasoning process. This supports our arguments and synthesizes a method for the analysis of research results. Confirmed by the research results, the principle of unity has shown to be the most important factor in the development and evolution of the structure of knowl­edge organization systems and their types. There are various types of unity when considering the analysis of logical relations. These include the principle of unity of alphabetical order, unity of science, semantic unity, structural unity and conceptual unity. The results have clearly demonstrated a movement from plurality to unity in the assembling of the complex structure of knowl­edge organization systems to increase information and knowl­edge storage and retrieval performance.

0.005727673 = product of:
  0.022910692 = sum of:
    0.022910692 = weight(_text_:retrieval in 1115) [ClassicSimilarity], result of:
      0.022910692 = score(doc=1115,freq=2.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.16710453 = fieldWeight in 1115, 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=1115)
  0.25 = coord(1/4)

Abstract

Great Sites are closely related to the residents' life, urban and rural development. In the process of rapid urbanization in China, the protection and utilization of Great Sites are facing unprecedented pressure. Effective knowl­edge organization with ontology and linked data of Great Sites is a prerequisite for their protection and utilization. In this paper, an interview is conducted to understand the users' awareness towards Great Sites to build the user-centered ontology. As for designing the Great Site ontology, firstly, the scope of Great Sites is determined. Secondly, CIDOC- CRM and OWL-Time Ontology are reused combining the results of literature research and user interviews. Thirdly, the top-level structure and the specific instances are determined to extract knowl­edge concepts of Great Sites. Fourthly, they are transformed into classes, data properties and object properties of the Great Site ontology. Later, based on the linked data technology, taking the Great Sites in Xi'an Area as an example, this paper uses D2RQ to publish the linked data set of the knowl­edge of the Great Sites and realize its opening and sharing. Semantic services such as semantic annotation, semantic retrieval and reasoning are provided based on the ontology.

0.004009371 = product of:
  0.016037485 = sum of:
    0.016037485 = weight(_text_:retrieval in 53) [ClassicSimilarity], result of:
      0.016037485 = score(doc=53,freq=2.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.11697317 = fieldWeight in 53, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.02734375 = fieldNorm(doc=53)
  0.25 = coord(1/4)

Abstract

We are proud to announce DBpedia Archivo (https://archivo.dbpedia.org) an augmented ontology archive and interface to implement FAIRer ontologies. Each ontology is rated with 4 stars measuring basic FAIR features. We discovered 890 ontologies reaching on average 1.95 out of 4 stars. Many of them have no or unclear licenses and have issues w.r.t. retrieval and parsing.

0.0038380611 = product of:
  0.0153522445 = sum of:
    0.0153522445 = product of:
      0.030704489 = sum of:
        0.030704489 = weight(_text_:22 in 106) [ClassicSimilarity], result of:
          0.030704489 = score(doc=106,freq=2.0), product of:
            0.15872006 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045324896 = queryNorm
            0.19345059 = fieldWeight in 106, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=106)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

22. 1.2021 14:24:32

0.0030704488 = product of:
  0.012281795 = sum of:
    0.012281795 = product of:
      0.02456359 = sum of:
        0.02456359 = weight(_text_:22 in 179) [ClassicSimilarity], result of:
          0.02456359 = score(doc=179,freq=2.0), product of:
            0.15872006 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045324896 = queryNorm
            0.15476047 = fieldWeight in 179, 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=179)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

20. 1.2015 18:30:22