Search (5 results, page 1 of 1)

  • × theme_ss:"Theorie verbaler Dokumentationssprachen"
  • × year_i:[2000 TO 2010}
  1. Khoo, S.G.; Na, J.-C.: Semantic relations in information science (2006) 0.02
    0.022866143 = product of:
      0.11433071 = sum of:
        0.11433071 = weight(_text_:objects in 1978) [ClassicSimilarity], result of:
          0.11433071 = score(doc=1978,freq=8.0), product of:
            0.32448718 = queryWeight, product of:
              5.315071 = idf(docFreq=590, maxDocs=44218)
              0.061050393 = queryNorm
            0.35234275 = fieldWeight in 1978, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              5.315071 = idf(docFreq=590, maxDocs=44218)
              0.0234375 = fieldNorm(doc=1978)
      0.2 = coord(1/5)
    
    Abstract
    This chapter examines the nature of semantic relations and their main applications in information science. The nature and types of semantic relations are discussed from the perspectives of linguistics and psychology. An overview of the semantic relations used in knowledge structures such as thesauri and ontologies is provided, as well as the main techniques used in the automatic extraction of semantic relations from text. The chapter then reviews the use of semantic relations in information extraction, information retrieval, question-answering, and automatic text summarization applications. Concepts and relations are the foundation of knowledge and thought. When we look at the world, we perceive not a mass of colors but objects to which we automatically assign category labels. Our perceptual system automatically segments the world into concepts and categories. Concepts are the building blocks of knowledge; relations act as the cement that links concepts into knowledge structures. We spend much of our lives identifying regular associations and relations between objects, events, and processes so that the world has an understandable structure and predictability. Our lives and work depend on the accuracy and richness of this knowledge structure and its web of relations. Relations are needed for reasoning and inferencing. Chaffin and Herrmann (1988b, p. 290) noted that "relations between ideas have long been viewed as basic to thought, language, comprehension, and memory." Aristotle's Metaphysics (Aristotle, 1961; McKeon, expounded on several types of relations. The majority of the 30 entries in a section of the Metaphysics known today as the Philosophical Lexicon referred to relations and attributes, including cause, part-whole, same and opposite, quality (i.e., attribute) and kind-of, and defined different types of each relation. Hume (1955) pointed out that there is a connection between successive ideas in our minds, even in our dreams, and that the introduction of an idea in our mind automatically recalls an associated idea. He argued that all the objects of human reasoning are divided into relations of ideas and matters of fact and that factual reasoning is founded on the cause-effect relation. His Treatise of Human Nature identified seven kinds of relations: resemblance, identity, relations of time and place, proportion in quantity or number, degrees in quality, contrariety, and causation. Mill (1974, pp. 989-1004) discoursed on several types of relations, claiming that all things are either feelings, substances, or attributes, and that attributes can be a quality (which belongs to one object) or a relation to other objects.
  2. Fugmann, R.: ¬The complementarity of natural and index language in the field of information supply : an overview of their specific capabilities and limitations (2002) 0.02
    0.018214801 = product of:
      0.091074005 = sum of:
        0.091074005 = weight(_text_:index in 1412) [ClassicSimilarity], result of:
          0.091074005 = score(doc=1412,freq=4.0), product of:
            0.2667758 = queryWeight, product of:
              4.369764 = idf(docFreq=1520, maxDocs=44218)
              0.061050393 = queryNorm
            0.3413878 = fieldWeight in 1412, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.369764 = idf(docFreq=1520, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1412)
      0.2 = coord(1/5)
    
    Abstract
    Natural text phrasing is an indeterminate process and, thus, inherently lacks representational predictability. This holds true in particular in the Gase of general concepts and of their syntactical connectivity. Hence, natural language query phrasing and searching is an unending adventure of trial and error and, in most Gases, has an unsatisfactory outcome with respect to the recall and precision ratlos of the responses. Human indexing is based an knowledgeable document interpretation and aims - among other things - at introducing predictability into the representation of documents. Due to the indeterminacy of natural language text phrasing and image construction, any adequate indexing is also indeterminate in nature and therefore inherently defies any satisfactory algorithmization. But human indexing suffers from a different Set of deficiencies which are absent in the processing of non-interpreted natural language. An optimally effective information System combines both types of language in such a manner that their specific strengths are preserved and their weaknesses are avoided. lf the goal is a large and enduring information system for more than merely known-item searches, the expenditure for an advanced index language and its knowledgeable and careful employment is unavoidable.
  3. Milstead, J.L.: Standards for relationships between subject indexing terms (2001) 0.02
    0.015455769 = product of:
      0.077278845 = sum of:
        0.077278845 = weight(_text_:index in 1148) [ClassicSimilarity], result of:
          0.077278845 = score(doc=1148,freq=2.0), product of:
            0.2667758 = queryWeight, product of:
              4.369764 = idf(docFreq=1520, maxDocs=44218)
              0.061050393 = queryNorm
            0.28967714 = fieldWeight in 1148, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.369764 = idf(docFreq=1520, maxDocs=44218)
              0.046875 = fieldNorm(doc=1148)
      0.2 = coord(1/5)
    
    Abstract
    Relationships between the terms in thesauri and Indexes are the subject of national and international standards. The standards for thesauri enumerate and provide criteria for three basic types of relationship: equivalence, hierarchical, and associative. Standards and guidelines for indexes draw an the thesaurus standards to provide less detailed guidance for showing relationships between the terms used in an Index. The international standard for multilingual thesauri adds recommendations for assuring equal treatment of the languages of a thesaurus. The present standards were developed when lookup and search were essentially manual, and the value of the kinds of relationships has never been determined. It is not clear whether users understand or can use the distinctions between kinds of relationships. On the other hand, sophisticated text analysis systems may be able both to assist with development of more powerful term relationship schemes and to use the relationships to improve retrieval.
  4. Mazzocchi, F.; Tiberi, M.; De Santis, B.; Plini, P.: Relational semantics in thesauri : an overview and some remarks at theoretical and practical levels (2007) 0.01
    0.012879808 = product of:
      0.06439904 = sum of:
        0.06439904 = weight(_text_:index in 1462) [ClassicSimilarity], result of:
          0.06439904 = score(doc=1462,freq=2.0), product of:
            0.2667758 = queryWeight, product of:
              4.369764 = idf(docFreq=1520, maxDocs=44218)
              0.061050393 = queryNorm
            0.24139762 = fieldWeight in 1462, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.369764 = idf(docFreq=1520, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1462)
      0.2 = coord(1/5)
    
    Abstract
    A thesaurus is a controlled vocabulary designed to allow for effective information retrieval. It con- sists of different kinds of semantic relationships, with the aim of guiding users to the choice of the most suitable index and search terms for expressing a certain concept. The relational semantics of a thesaurus deal with methods to connect terms with related meanings and arc intended to enhance information recall capabilities. In this paper, focused on hierarchical relations, different aspects of the relational semantics of thesauri, and among them the possibility of developing richer structures, are analyzed. Thesauri are viewed as semantic tools providing, for operational purposes, the representation of the meaning of the terms. The paper stresses how theories of semantics, holding different perspectives about the nature of meaning and how it is represented, affect the design of the relational semantics of thesauri. The need for tools capable of representing the complexity of knowledge and of the semantics of terms as it occurs in the literature of their respective subject fields is advocated. It is underlined how this would contribute to improving the retrieval of information. To achieve this goal, even though in a preliminary manner, we explore the possibility of setting against the framework of thesaurus design the notions of language games and hermeneutic horizon.
  5. Dextre Clarke, S.G.: Thesaural relationships (2001) 0.01
    0.01158008 = product of:
      0.0579004 = sum of:
        0.0579004 = weight(_text_:22 in 1149) [ClassicSimilarity], result of:
          0.0579004 = score(doc=1149,freq=2.0), product of:
            0.21378808 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.061050393 = queryNorm
            0.2708308 = fieldWeight in 1149, 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=1149)
      0.2 = coord(1/5)
    
    Date
    22. 9.2007 15:45:57