Search (40 results, page 2 of 2)

0.001913537 = product of:
  0.003827074 = sum of:
    0.003827074 = product of:
      0.007654148 = sum of:
        0.007654148 = weight(_text_:a in 4949) [ClassicSimilarity], result of:
          0.007654148 = score(doc=4949,freq=4.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.14413087 = fieldWeight in 4949, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0625 = fieldNorm(doc=4949)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Source

Knowledge organization, information systems and other essays: Professor A. Neelameghan Festschrift. Ed. by K.S. Raghavan and K.N. Prasad

Type

a

0.001913537 = product of:
  0.003827074 = sum of:
    0.003827074 = product of:
      0.007654148 = sum of:
        0.007654148 = weight(_text_:a in 1145) [ClassicSimilarity], result of:
          0.007654148 = score(doc=1145,freq=4.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.14413087 = fieldWeight in 1145, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0625 = fieldNorm(doc=1145)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Type

a

0.001913537 = product of:
  0.003827074 = sum of:
    0.003827074 = product of:
      0.007654148 = sum of:
        0.007654148 = weight(_text_:a in 2621) [ClassicSimilarity], result of:
          0.007654148 = score(doc=2621,freq=4.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.14413087 = fieldWeight in 2621, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0625 = fieldNorm(doc=2621)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Although less well studied than noun polysemy, verb polysemy affects both natural language and controlled vocabulary searching. This paper reports the preliminary conclusions of an empirical investigation of the semantic relationships between ca. 600 verb sense pairs in English, illustrating six classes of semantic relationships that account for a significant proportion of verbal polysemy.

Type

a

0.001913537 = product of:
  0.003827074 = sum of:
    0.003827074 = product of:
      0.007654148 = sum of:
        0.007654148 = weight(_text_:a in 216) [ClassicSimilarity], result of:
          0.007654148 = score(doc=216,freq=4.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.14413087 = fieldWeight in 216, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0625 = fieldNorm(doc=216)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Classical knowledge representation methods traditionally work with established relations such as synonymy, hierarchy and unspecified associations. Recent developments like ontologies and folksonomies show new forms of collaboration, indexing and knowledge representation and encourage the reconsideration of standard knowledge relationships. In a summarizing overview we show which relations are currently utilized in elaborated knowledge representation methods and which may be inherently hidden in folksonomies and ontologies.

Type

a

0.001757696 = product of:
  0.003515392 = sum of:
    0.003515392 = product of:
      0.007030784 = sum of:
        0.007030784 = weight(_text_:a in 5611) [ClassicSimilarity], result of:
          0.007030784 = score(doc=5611,freq=6.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.13239266 = fieldWeight in 5611, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=5611)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The IFLA Section on Classification and Indexing's Working Group on Principles Underlying Subject Headings Languages has identified a set of eleven principles for subject heading languages and excerpted the texts that match each principle from the instructions for each of eleven national subject indexing systems, including excerpts from the LC's Subject Cataloging Manual: Subject Headings. This study compares the IFLA principles with other texts that express the principles underlying LCSH, especially Library of Congress Subject Headings: Principles of Structure and Policies for Application, prepared by Lois Mai Chan for the Library of Congress in 1990, Chan's later book on LCSH, and earlier documents by Haykin and Cutter. The principles are further elaborated for clarity and discussed

Type

a

0.001757696 = product of:
  0.003515392 = sum of:
    0.003515392 = product of:
      0.007030784 = sum of:
        0.007030784 = weight(_text_:a in 1146) [ClassicSimilarity], result of:
          0.007030784 = score(doc=1146,freq=6.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.13239266 = fieldWeight in 1146, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=1146)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Mapping a multidimensional universe of subjects for linear representation, such as in class number, subject heading, and faset structure is problematic. Into this context is recalled the near-seminal and postulational approach suggested by S. R Ranganathan. The non-hierarchical associative relationship or lateral relationship (LR) is distinguished at different levels-among information sources, databases, records of databases, and among concepts (LR-0). Over thirty lateral relationships at the concept level (LR-0) are identified and enumerated with examples from spiritual and religious texts. Special issues relating to LR-0 in multicultural, multilingual databases intended to be used globally by peoples of different cultures and faith are discussed, using as example the multimedia OM Information Service. Vocabulary assistance for users is described.

Type

a

0.001757696 = product of:
  0.003515392 = sum of:
    0.003515392 = product of:
      0.007030784 = sum of:
        0.007030784 = weight(_text_:a in 1201) [ClassicSimilarity], result of:
          0.007030784 = score(doc=1201,freq=6.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.13239266 = fieldWeight in 1201, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=1201)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Thesaural relations have long been used in information retrieval to enrich queries; they have sometimes been used to cluster documents as well. Sometimes the first query to an information retrieval system yields no results at all, or, what can be even more disconcerting, many thousands of hits. One solution is to rephrase the query, improving the choice of query terms by using related terms of different types. A collection of related terms is often called a thesaurus. This chapter describes the lexical-semantic relations that have been used in building thesauri and summarizes some of the effects of using these relational thesauri in information retrieval experiments

Type

a

0.001757696 = product of:
  0.003515392 = sum of:
    0.003515392 = product of:
      0.007030784 = sum of:
        0.007030784 = weight(_text_:a in 5683) [ClassicSimilarity], result of:
          0.007030784 = score(doc=5683,freq=6.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.13239266 = fieldWeight in 5683, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=5683)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The existence of subject indexing languages does not call for or imply a particular authority control system exclusively dedicated to subject entries. To be really effective and efficient, authority control must be concerned with all the categories of entities, and must regard not just the form but also the meaning and the semantic relations of the expressions used to identify the single entities. Thus, it satisfies the lexical needs of all cataloguing languages, including subject indexing languages. It is not correct nor opportune to extend authority control to the syntactic constructions of subject indexing languages, because this reduces the rigor and efficiency of the control process, weighing it down until it becomes unfeasible, and impeding its function as a unifying element between the different cataloguing languages.

Type

a

0.001757696 = product of:
  0.003515392 = sum of:
    0.003515392 = product of:
      0.007030784 = sum of:
        0.007030784 = weight(_text_:a in 1680) [ClassicSimilarity], result of:
          0.007030784 = score(doc=1680,freq=6.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.13239266 = fieldWeight in 1680, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=1680)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Last word ontological thought and practice is exemplified on an axiomatic framework [a model for an Integrative Cross-Language Ontology (ICLO), cf. Poli, R., Schmitz-Esser, W., forthcoming 2007] that is highly general, based on natural language, multilingual, can be implemented as topic maps and may be openly enhanced by software available for particular languages. Basics of ontological modelling, conditions for construction and maintenance, and the most salient points in application are addressed, such as cross-language text mining and knowledge generation. The rationale is to open the eyes for the tremendous potential of terminology-based ontologies for principled Knowledge Organization and the interchange and reuse of formalized knowledge.

Type

a

0.001757696 = product of:
  0.003515392 = sum of:
    0.003515392 = product of:
      0.007030784 = sum of:
        0.007030784 = weight(_text_:a in 1978) [ClassicSimilarity], result of:
          0.007030784 = score(doc=1978,freq=24.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.13239266 = fieldWeight in 1978, product of:
              4.8989797 = tf(freq=24.0), with freq of:
                24.0 = termFreq=24.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0234375 = fieldNorm(doc=1978)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.
Linguists in the structuralist tradition (e.g., Lyons, 1977; Saussure, 1959) have asserted that concepts cannot be defined on their own but only in relation to other concepts. Semantic relations appear to reflect a logical structure in the fundamental nature of thought (Caplan & Herrmann, 1993). Green, Bean, and Myaeng (2002) noted that semantic relations play a critical role in how we represent knowledge psychologically, linguistically, and computationally, and that many systems of knowledge representation start with a basic distinction between entities and relations. Green (2001, p. 3) said that "relationships are involved as we combine simple entities to form more complex entities, as we compare entities, as we group entities, as one entity performs a process on another entity, and so forth. Indeed, many things that we might initially regard as basic and elemental are revealed upon further examination to involve internal structure, or in other words, internal relationships." Concepts and relations are often expressed in language and text. Language is used not just for communicating concepts and relations, but also for representing, storing, and reasoning with concepts and relations. We shall examine the nature of semantic relations from a linguistic and psychological perspective, with an emphasis on relations expressed in text. The usefulness of semantic relations in information science, especially in ontology construction, information extraction, information retrieval, question-answering, and text summarization is discussed. Research and development in information science have focused on concepts and terms, but the focus will increasingly shift to the identification, processing, and management of relations to achieve greater effectiveness and refinement in information science techniques. Previous chapters in ARIST on natural language processing (Chowdhury, 2003), text mining (Trybula, 1999), information retrieval and the philosophy of language (Blair, 2003), and query expansion (Efthimiadis, 1996) provide a background for this discussion, as semantic relations are an important part of these applications.

Type

a

0.0016913437 = product of:
  0.0033826875 = sum of:
    0.0033826875 = product of:
      0.006765375 = sum of:
        0.006765375 = weight(_text_:a in 1412) [ClassicSimilarity], result of:
          0.006765375 = score(doc=1412,freq=8.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.12739488 = fieldWeight in 1412, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1412)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.

Type

a

0.001674345 = product of:
  0.00334869 = sum of:
    0.00334869 = product of:
      0.00669738 = sum of:
        0.00669738 = weight(_text_:a in 1142) [ClassicSimilarity], result of:
          0.00669738 = score(doc=1142,freq=4.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.12611452 = fieldWeight in 1142, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1142)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Relationships are specified by simultaneously identifying a semantic relationship and the set of participants involved in it, pairing each participant with its role in the relationship. Properties pertaining to the participant set and the nature of the relationship are explored. Relationships in the organization of knowledge are surveyed, encompassing relationships between units of recorded knowledge based an descriptions of those units; intratextual and intertextual relationships, including relationships based an text structure, citation relationships, and hypertext links; subject relationships in thesauri and other classificatory structures, including relationships for literature-based knowledge discovery; and relevance relationships.

Type

a

0.0015127839 = product of:
  0.0030255679 = sum of:
    0.0030255679 = product of:
      0.0060511357 = sum of:
        0.0060511357 = weight(_text_:a in 1224) [ClassicSimilarity], result of:
          0.0060511357 = score(doc=1224,freq=10.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.11394546 = fieldWeight in 1224, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.03125 = fieldNorm(doc=1224)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Content

Enthält die Beiträge: BACKGROUND: Alva T STONE: The LCSH Century: A Brief History of the Library of Congress Subject Headings, and Introduction to the Centennial Essays - THEORY AND PRINCIPLES: Elaine SVENONIUS: LCSH: Semantics, Syntax and Specificity; Heidi Lee HOERMAN u. Kevin A. FURNISS: Turning Practice into Principles: A Comparison of the IFLA: Principles Underlying Subject Heading Languages (SHLs) and the Principles Underlying the Library of Congress Subject Headings System; Hope A. OLSON: Difference, Culture and Change:The Untapped Potential of LCSH - ONLINE ENVIRONMENT: Pauline Atherton COCHRANE: Improving LCSH for Use in Online Catalogs Revisited-What Progress Has Been Made? What Issues Still Remain?; Gregory WOOL: Filing and Precoordination: How Subject Headings Are Displayed in Online Catalogs and Why It Matters; Stephen HEARN: Machine-Assisted Validation of LC Subject Headings: Implications for Authority File Structure - SPECIFIC PERSPECTIVES: Thomas MANN: Teaching Library of Congress Subject Headings; Louisa J. KREIDER: LCSH Works! Subject Searching Effectiveness at the Cleveland Public Library and the Growth of Library of Congress Subject Headings Through Cooperation; Harriette HEMMASI u J. Bradford YOUNG: LCSH for Music: Historical and Empirical Perspectives; Joseph MILLER u. Patricia KUHR: LCSH and Periodical Indexing: Adoption vs. Adaptation; David P MILLER: Out from Under: Form/Genre Access in LCSH - WORLD VIEW: Magda HEINER-FREILING: Survey on Subject Heading Languages Used in National Libraries and Bibliographies; Andrew MacEWAN: Crossing Language Barriers in Europe: Linking LCSH to Other Subject Heading Languages; Alvaro QUIJANO-SOLIS u.a.: Automated Authority Files of Spanish-Language Subject Headings - FUTURE PROSPECTS: Lois Mai CHAN u. Theodora HODGES: Entering the Millennium: a new century for LCSH

0.0015127839 = product of:
  0.0030255679 = sum of:
    0.0030255679 = product of:
      0.0060511357 = sum of:
        0.0060511357 = weight(_text_:a in 5366) [ClassicSimilarity], result of:
          0.0060511357 = score(doc=5366,freq=10.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.11394546 = fieldWeight in 5366, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.03125 = fieldNorm(doc=5366)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Content

Enthält die Beiträge: BACKGROUND: Alva T STONE: The LCSH Century: A Brief History of the Library of Congress Subject Headings, and Introduction to the Centennial Essays - THEORY AND PRINCIPLES: Elaine SVENONIUS: LCSH: Semantics, Syntax and Specificity; Heidi Lee HOERMAN u. Kevin A. FURNISS: Turning Practice into Principles: A Comparison of the IFLA: Principles Underlying Subject Heading Languages (SHLs) and the Principles Underlying the Library of Congress Subject Headings System; Hope A. OLSON: Difference, Culture and Change:The Untapped Potential of LCSH - ONLINE ENVIRONMENT: Pauline Atherton COCHRANE: Improving LCSH for Use in Online Catalogs Revisited-What Progress Has Been Made? What Issues Still Remain?; Gregory WOOL: Filing and Precoordination: How Subject Headings Are Displayed in Online Catalogs and Why It Matters; Stephen HEARN: Machine-Assisted Validation of LC Subject Headings: Implications for Authority File Structure - SPECIFIC PERSPECTIVES: Thomas MANN: Teaching Library of Congress Subject Headings; Louisa J. KREIDER: LCSH Works! Subject Searching Effectiveness at the Cleveland Public Library and the Growth of Library of Congress Subject Headings Through Cooperation; Harriette HEMMASI u J. Bradford YOUNG: LCSH for Music: Historical and Empirical Perspectives; Joseph MILLER u. Patricia KUHR: LCSH and Periodical Indexing: Adoption vs. Adaptation; David P MILLER: Out from Under: Form/Genre Access in LCSH - WORLD VIEW: Magda HEINER-FREILING: Survey on Subject Heading Languages Used in National Libraries and Bibliographies; Andrew MacEWAN: Crossing Language Barriers in Europe: Linking LCSH to Other Subject Heading Languages; Alvaro QUIJANO-SOLIS u.a.: Automated Authority Files of Spanish-Language Subject Headings - FUTURE PROSPECTS: Lois Mai CHAN u. Theodora HODGES: Entering the Millennium: a new century for LCSH

0.0014647468 = product of:
  0.0029294936 = sum of:
    0.0029294936 = product of:
      0.005858987 = sum of:
        0.005858987 = weight(_text_:a in 1430) [ClassicSimilarity], result of:
          0.005858987 = score(doc=1430,freq=6.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.11032722 = fieldWeight in 1430, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1430)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Work on relationships takes place in many communities, including, among others, data modeling, knowledge representation, natural language processing, linguistics, and information retrieval. Unfortunately, continued disciplinary splintering and specialization keeps any one person from being familiar with the full expanse of that work. By including contributions form experts in a variety of disciplines and backgrounds, this volume demonstrates both the parallels that inform work on relationships across a number of fields and the singular emphases that have yet to be fully embraced, The volume is organized into 3 parts: (1) Types of relationships (2) Relationships in knowledge representation and reasoning (3) Applications of relationships

Content

Enthält die Beiträge: Pt.1: Types of relationships: CRUDE, D.A.: Hyponymy and its varieties; FELLBAUM, C.: On the semantics of troponymy; PRIBBENOW, S.: Meronymic relationships: from classical mereology to complex part-whole relations; KHOO, C. u.a.: The many facets of cause-effect relation - Pt.2: Relationships in knowledge representation and reasoning: GREEN, R.: Internally-structured conceptual models in cognitive semantics; HOVY, E.: Comparing sets of semantic relations in ontologies; GUARINO, N., C. WELTY: Identity and subsumption; JOUIS; C.: Logic of relationships - Pt.3: Applications of relationships: EVENS, M.: Thesaural relations in information retrieval; KHOO, C., S.H. MYAENG: Identifying semantic relations in text for information retrieval and information extraction; McCRAY, A.T., O. BODENREICHER: A conceptual framework for the biiomedical domain; HETZLER, B.: Visual analysis and exploration of relationships

0.0014647468 = product of:
  0.0029294936 = sum of:
    0.0029294936 = product of:
      0.005858987 = sum of:
        0.005858987 = weight(_text_:a in 1921) [ClassicSimilarity], result of:
          0.005858987 = score(doc=1921,freq=6.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.11032722 = fieldWeight in 1921, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1921)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Classification schemes, thesauri, taxonomies, and other controlled vocabularies play important roles in the organization and retrieval of information in many different environments. While the design and construction of controlled vocabularies have been prescribed at the technical level in great detail over the past decades, the methodological level has been somewhat neglected. However, classification research has in recent years focused on developing approaches to the analysis of users, domains, and activities that could produce requirements for the design of controlled vocabularies. Researchers have often argued that the design, construction, and use of controlled vocabularies need to be based on analyses and understandings of the contexts in which these controlled vocabularies function. While one would assume that the growing body of research on human information behavior might help guide the development of controlled vocabularies shed light on these contexts, unfortunately, much of the research in this area is descriptive in nature and of little use for systems design. This paper discusses these trends and outlines a holistic approach that demonstrates how the design of controlled vocabularies can be informed by investigations of people's interactions with information. This approach is based on the Cognitive Work Analysis framework and outlines several dimensions of human-information interactions. Application of this approach will result is a comprehensive understanding of the contexts in which the controlled vocabulary will function and which can be used for the development of for the development of controlled vocabularies.

Type

a

0.0014351527 = product of:
  0.0028703054 = sum of:
    0.0028703054 = product of:
      0.005740611 = sum of:
        0.005740611 = weight(_text_:a in 1148) [ClassicSimilarity], result of:
          0.005740611 = score(doc=1148,freq=4.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.10809815 = fieldWeight in 1148, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=1148)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.

Type

a

0.0011959607 = product of:
  0.0023919214 = sum of:
    0.0023919214 = product of:
      0.0047838427 = sum of:
        0.0047838427 = weight(_text_:a in 1139) [ClassicSimilarity], result of:
          0.0047838427 = score(doc=1139,freq=4.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.090081796 = fieldWeight in 1139, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1139)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Content

Enthält u.a. die Beiträge: GREEN, R.: Relationships in the organization of knowledge: an overview; TILLETT, B.: Bibliographic relationships; CLARKE, S.G.D.: Thesaural relationships; MILSTEAD, J.L.: Standards for relationships between subject indexing terms; HUDON, M.: Relationships in multilingual thesauri; BODENREIDER, O. u. C.A. BEAN: Relationships among knowledge structures: vocabulary integration within a subject domain; BEGHTOL, C.: Relationships in classificatory structure and meaning; BEAN, C.A. u. R. GREEN: Relevance relationships; EL-HOSHY, L.M.: Relationships in Library of Congress Subject Headings; MOLHOLT, P.: The Art and Architecture Thesaurus: controlling relationships through rules and structure; NELSON, S.J. u.a.: Relationships in Medical Subject Headings (MeSH); NEELAMEGHAN, A.: Lateral relationships in multicultural, mulrilingual databases in the spiritual and religous domains: the OM information service; SATIJA, M.P.: Relationships in Ranganathan's Colon classification; MITCHELL, J.S.: Relationships in the Dewey Decimal Classification System

0.0011959607 = product of:
  0.0023919214 = sum of:
    0.0023919214 = product of:
      0.0047838427 = sum of:
        0.0047838427 = weight(_text_:a in 927) [ClassicSimilarity], result of:
          0.0047838427 = score(doc=927,freq=4.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.090081796 = fieldWeight in 927, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=927)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Extracting semantic relationships between entities from text documents is challenging in information extraction and important for deep information processing and management. This paper investigates the incorporation of diverse lexical, syntactic and semantic knowledge in feature-based relation extraction using support vector machines. Our study illustrates that the base phrase chunking information is very effective for relation extraction and contributes to most of the performance improvement from syntactic aspect while current commonly used features from full parsing give limited further enhancement. This suggests that most of useful information in full parse trees for relation extraction is shallow and can be captured by chunking. This indicates that a cheap and robust solution in relation extraction can be achieved without decreasing too much in performance. We also demonstrate how semantic information such as WordNet, can be used in feature-based relation extraction to further improve the performance. Evaluation on the ACE benchmark corpora shows that effective incorporation of diverse features enables our system outperform previously best-reported systems. It also shows that our feature-based system significantly outperforms tree kernel-based systems. This suggests that current tree kernels fail to effectively explore structured syntactic information in relation extraction.

Type

a

8.4567186E-4 = product of:
  0.0016913437 = sum of:
    0.0016913437 = product of:
      0.0033826875 = sum of:
        0.0033826875 = weight(_text_:a in 4335) [ClassicSimilarity], result of:
          0.0033826875 = score(doc=4335,freq=2.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.06369744 = fieldWeight in 4335, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4335)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Type

a