Search (3 results, page 1 of 1)

0.016952079 = product of:
  0.050856233 = sum of:
    0.050856233 = weight(_text_:electronic in 1622) [ClassicSimilarity], result of:
      0.050856233 = score(doc=1622,freq=2.0), product of:
        0.19623034 = queryWeight, product of:
          3.9095051 = idf(docFreq=2409, maxDocs=44218)
          0.05019314 = queryNorm
        0.259166 = fieldWeight in 1622, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.9095051 = idf(docFreq=2409, maxDocs=44218)
          0.046875 = fieldNorm(doc=1622)
  0.33333334 = coord(1/3)

Abstract

Thesauri are used to provide controlled vocabularies for resource classification. Their use can greatly assist document discovery because thesauri man date a consistent shared terminology for describing documents. A particular thesauras classifies documents according to an information community's needs. As a result, there are many different thesaural schemas. This has led to a proliferation of schema-specific thesaural systems. In our research, we exploit schematic regularities to design a generic thesaural ontology and specfiy it as a markup language. The language provides a common representational framework in which to encode the idiosyncrasies of specific thesauri. This approach has several advantages: it offers consistent syntax and semantics in which to express thesauri; it allows general purpose thesaural applications to leverage many thesauri; and it supports a single thesaural user interface by which information communities can consistently organise, score and retrieve electronic documents.

0.014126732 = product of:
  0.042380195 = sum of:
    0.042380195 = weight(_text_:electronic in 3373) [ClassicSimilarity], result of:
      0.042380195 = score(doc=3373,freq=2.0), product of:
        0.19623034 = queryWeight, product of:
          3.9095051 = idf(docFreq=2409, maxDocs=44218)
          0.05019314 = queryNorm
        0.21597168 = fieldWeight in 3373, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.9095051 = idf(docFreq=2409, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3373)
  0.33333334 = coord(1/3)

Abstract

A band of archivists and IT professionals at Harvard took on a project to convert nearly two million descriptions of archival collection components from marked-up text into the ArchivesSpace archival metadata management system. Starting in the mid-1990s, Harvard was an alpha implementer of EAD, an SGML (later XML) text markup language for electronic inventories, indexes, and finding aids that archivists use to wend their way through the sometimes quirky filing systems that bureaucracies establish for their records or the utter chaos in which some individuals keep their personal archives. These pathfinder documents, designed to cope with messy reality, can themselves be difficult to classify. Portions of them are rigorously structured, while other parts are narrative. Early documents predate the establishment of the standard; many feature idiosyncratic encoding that had been through several machine conversions, while others were freshly encoded and fairly consistent. In this paper, we will cover the practical and technical challenges involved in preparing a large (900MiB) corpus of XML for ingest into an open-source archival information system (ArchivesSpace). This case study will give an overview of the project, discuss problem discovery and problem solving, and address the technical challenges, analysis, solutions, and decisions and provide information on the tools produced and lessons learned. The authors of this piece are Kate Bowers, Collections Services Archivist for Metadata, Systems, and Standards at the Harvard University Archive, and Dave Mayo, a Digital Library Software Engineer for Harvard's Library and Technology Services. Kate was heavily involved in both metadata analysis and later problem solving, while Dave was the sole full-time developer assigned to the migration project.

0.009888712 = product of:
  0.029666137 = sum of:
    0.029666137 = weight(_text_:electronic in 5903) [ClassicSimilarity], result of:
      0.029666137 = score(doc=5903,freq=2.0), product of:
        0.19623034 = queryWeight, product of:
          3.9095051 = idf(docFreq=2409, maxDocs=44218)
          0.05019314 = queryNorm
        0.15118018 = fieldWeight in 5903, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.9095051 = idf(docFreq=2409, maxDocs=44218)
          0.02734375 = fieldNorm(doc=5903)
  0.33333334 = coord(1/3)

Abstract

RDF - the Resource Description Framework - is a foundation for processing metadata; it provides interoperability between applications that exchange machine-understandable information on the Web. RDF emphasizes facilities to enable automated processing of Web resources. RDF metadata can be used in a variety of application areas; for example: in resource discovery to provide better search engine capabilities; in cataloging for describing the content and content relationships available at a particular Web site, page, or digital library; by intelligent software agents to facilitate knowledge sharing and exchange; in content rating; in describing collections of pages that represent a single logical "document"; for describing intellectual property rights of Web pages, and in many others. RDF with digital signatures will be key to building the "Web of Trust" for electronic commerce, collaboration, and other applications. Metadata is "data about data" or specifically in the context of RDF "data describing web resources." The distinction between "data" and "metadata" is not an absolute one; it is a distinction created primarily by a particular application. Many times the same resource will be interpreted in both ways simultaneously. RDF encourages this view by using XML as the encoding syntax for the metadata. The resources being described by RDF are, in general, anything that can be named via a URI. The broad goal of RDF is to define a mechanism for describing resources that makes no assumptions about a particular application domain, nor defines the semantics of any application domain. The definition of the mechanism should be domain neutral, yet the mechanism should be suitable for describing information about any domain. This document introduces a model for representing RDF metadata and one syntax for expressing and transporting this metadata in a manner that maximizes the interoperability of independently developed web servers and clients. The syntax described in this document is best considered as a "serialization syntax" for the underlying RDF representation model. The serialization syntax is XML, XML being the W3C's work-in-progress to define a richer Web syntax for a variety of applications. RDF and XML are complementary; there will be alternate ways to represent the same RDF data model, some more suitable for direct human authoring. Future work may lead to including such alternatives in this document.