Search (3 results, page 1 of 1)

0.0029745363 = product of:
  0.011898145 = sum of:
    0.011898145 = weight(_text_:information in 5828) [ClassicSimilarity], result of:
      0.011898145 = score(doc=5828,freq=8.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.19395474 = fieldWeight in 5828, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5828)
  0.25 = coord(1/4)

Abstract

Some of the fundamental activities of the software development process are related to the discipline of Requirements Engineering, whose objective is the discovery, analysis, documentation and verification of the requirements that will be part of the system. Requirements are the conditions or capabilities that software must have or perform to meet the users needs. The present study is being developed to propose a model of cooperation between Information Science and Requirements Engineering. Aims to present the analysis results on the possibilities of using the knowledge organization systems: taxonomies, thesauri and ontologies during the activities of Requirements Engineering: design, survey, elaboration, negotiation, specification, validation and requirements management. From the results obtained it was possible to identify in which stage of the Requirements Engineering process, each type of knowledge organization system could be used. We expect that this study put in evidence the need for new researchs and proposals to strengt the exchange between Information Science, as a science that has information as object of study, and the Requirements Engineering which has in the information the raw material to identify the informational needs of software users.

0.0029446408 = product of:
  0.011778563 = sum of:
    0.011778563 = weight(_text_:information in 572) [ClassicSimilarity], result of:
      0.011778563 = score(doc=572,freq=4.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.1920054 = fieldWeight in 572, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, 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.

0.0014872681 = product of:
  0.0059490725 = sum of:
    0.0059490725 = weight(_text_:information in 592) [ClassicSimilarity], result of:
      0.0059490725 = score(doc=592,freq=2.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.09697737 = fieldWeight in 592, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, 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.