Search (56 results, page 2 of 3)

0.0023678814 = product of:
  0.0047357627 = sum of:
    0.0047357627 = product of:
      0.009471525 = sum of:
        0.009471525 = weight(_text_:a in 3915) [ClassicSimilarity], result of:
          0.009471525 = score(doc=3915,freq=2.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.17835285 = fieldWeight in 3915, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.109375 = fieldNorm(doc=3915)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Type

a

0.0023678814 = product of:
  0.0047357627 = sum of:
    0.0047357627 = product of:
      0.009471525 = sum of:
        0.009471525 = weight(_text_:a in 4569) [ClassicSimilarity], result of:
          0.009471525 = score(doc=4569,freq=2.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.17835285 = fieldWeight in 4569, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.109375 = fieldNorm(doc=4569)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Type

a

0.0023678814 = product of:
  0.0047357627 = sum of:
    0.0047357627 = product of:
      0.009471525 = sum of:
        0.009471525 = weight(_text_:a in 5720) [ClassicSimilarity], result of:
          0.009471525 = score(doc=5720,freq=8.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.17835285 = fieldWeight in 5720, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0546875 = fieldNorm(doc=5720)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The Illinois Digital Heritage Hub (IDHH) gathers and enhances metadata from contributing institutions around the state of Illinois and provides this metadata to th Digital Public Library of America (DPLA) for greater access. The IDHH helps contributors shape their metadata to the standards recommended and required by the DPLA in part by analyzing and enhancing aggregated metadata. In late 2018, the IDHH undertook a project to address a particularly problematic field, Type metadata. This paper walks through the project, detailing the process of gathering and analyzing metadata using the DPLA API and OpenRefine, data remediation through XSL transformations in conjunction with local improvements by contributing institutions, and the DPLA ingestion system's quality controls.

Type

a

0.002354251 = product of:
  0.004708502 = sum of:
    0.004708502 = product of:
      0.009417004 = sum of:
        0.009417004 = weight(_text_:a in 1216) [ClassicSimilarity], result of:
          0.009417004 = score(doc=1216,freq=62.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.17732619 = fieldWeight in 1216, product of:
              7.8740077 = tf(freq=62.0), with freq of:
                62.0 = termFreq=62.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.01953125 = fieldNorm(doc=1216)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Reality is messy. Individuals perceive or define objects differently. Objects may change over time, morphing into new versions of their former selves or into things altogether different. A book can give rise to a translation, derivation, or edition, and these resulting objects are related in complex ways to each other and to the people and contexts in which they were created or transformed. Providing a normalized view of such a messy reality is a precondition for managing information. From the first library catalogs, through Melvil Dewey's Decimal Classification system in the nineteenth century, to today's MARC encoding of AACR2 cataloging rules, libraries have epitomized the process of what David Levy calls "order making", whereby catalogers impose a veneer of regularity on the natural disorder of the artifacts they encounter. The pre-digital library within which the Catalog and its standards evolved was relatively self-contained and controlled. Creating and maintaining catalog records was, and still is, the task of professionals. Today's Web, in contrast, has brought together a diversity of information management communities, with a variety of order-making standards, into what Stuart Weibel has called the Internet Commons. The sheer scale of this context has motivated a search for new ways to describe and index information. Second-generation search engines such as Google can yield astonishingly good search results, while tools such as ResearchIndex for automatic citation indexing and techniques for inferring "Web communities" from constellations of hyperlinks promise even better methods for focusing queries on information from authoritative sources. Such "automated digital libraries," according to Bill Arms, promise to radically reduce the cost of managing information. Alongside the development of such automated methods, there is increasing interest in metadata as a means of imposing pre-defined order on Web content. While the size and changeability of the Web makes professional cataloging impractical, a minimal amount of information ordering, such as that represented by the Dublin Core (DC), may vastly improve the quality of an automatic index at low cost; indeed, recent work suggests that some types of simple description may be generated with little or no human intervention.
Metadata is not monolithic. Instead, it is helpful to think of metadata as multiple views that can be projected from a single information object. Such views can form the basis of customized information services, such as search engines. Multiple views -- different types of metadata associated with a Web resource -- can facilitate a "drill-down" search paradigm, whereby people start their searches at a high level and later narrow their focus using domain-specific search categories. In Figure 1, for example, Mona Lisa may be viewed from the perspective of non-specialized searchers, with categories that are valid across domains (who painted it and when?); in the context of a museum (when and how was it acquired?); in the geo-spatial context of a walking tour using mobile devices (where is it in the gallery?); and in a legal framework (who owns the rights to its reproduction?). Multiple descriptive views imply a modular approach to metadata. Modularity is the basis of metadata architectures such as the Resource Description Framework (RDF), which permit different communities of expertise to associate and maintain multiple metadata packages for Web resources. As noted elsewhere, static association of multiple metadata packages with resources is but one way of achieving modularity. Another method is to computationally derive order-making views customized to the current needs of a client. This paper examines the evolution and scope of the Dublin Core from this perspective of metadata modularization. Dublin Core began in 1995 with a specific goal and scope -- as an easy-to-create and maintain descriptive format to facilitate cross-domain resource discovery on the Web. Over the years, this goal of "simple metadata for coarse-granularity discovery" came to mix with another goal -- that of community and domain-specific resource description and its attendant complexity. A notion of "qualified Dublin Core" evolved whereby the model for simple resource discovery -- a set of simple metadata elements in a flat, document-centric model -- would form the basis of more complex descriptions by treating the values of its elements as entities with properties ("component elements") in their own right.
At the time of writing, the Dublin Core Metadata Initiative (DCMI) has clarified its commitment to the simple approach. The qualification principles announced in early 2000 support the use of DC elements as the basis for simple statements about resources, rather than as the foundation for more descriptive clauses. This paper takes a critical look at some of the issues that led up to this renewed commitment to simplicity. We argue that: * There remains a compelling need for simple, "pidgin" metadata. From a technical and economic perspective, document-centric metadata, where simple string values are associated with a finite set of properties, is most appropriate for generic, cross-domain discovery queries in the Internet Commons. Such metadata is not necessarily fixed in physical records, but may be projected algorithmically from more complex metadata or from content itself. * The Dublin Core, while far from perfect from an engineering perspective, is an acceptable standard for such simple metadata. Agreements in the global information space are as much social as technical, and the process by which the Dublin Core has been developed, involving a broad cross-section of international participants, is a model for such "socially developed" standards. * Efforts to introduce complexity into Dublin Core are misguided. Complex descriptions may be necessary for some Web resources and for some purposes, such as administration, preservation, and reference linking. However, complex descriptions require more expressive data models that differentiate between agents, documents, contexts, events, and the like. An attempt to intermix simplicity and complexity, and the data models most appropriate for them, defeats the equally noble goals of cross-domain description and extensive resource description. * The principle of modularity suggests that metadata formats tailored for simplicity be used alongside others tailored for complexity.

Type

a

0.0022438213 = product of:
  0.0044876426 = sum of:
    0.0044876426 = product of:
      0.008975285 = sum of:
        0.008975285 = weight(_text_:a in 1199) [ClassicSimilarity], result of:
          0.008975285 = score(doc=1199,freq=22.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.16900843 = fieldWeight in 1199, product of:
              4.690416 = tf(freq=22.0), with freq of:
                22.0 = termFreq=22.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.03125 = fieldNorm(doc=1199)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

On 18 November 2002, at a meeting organised by the CORES Project (Information Society Technologies Programme, European Union), several organisations regarded as maintenance authorities for metadata elements achieved consensus on a resolution to assign Uniform Resource Identifiers (URIs) to metadata elements as a useful first step towards the development of mapping infrastructures and interoperability services. The signatories of the CORES Resolution agreed to promote this consensus in their communities and beyond and to implement an action plan in the following six months. Six months having passed, the maintainers of GILS, ONIX, MARC 21, CERIF, DOI, IEEE/LOM, and Dublin Core report on their implementations of the resolution and highlight issues of relevance to establishing good-practice conventions for declaring, identifying, and maintaining metadata elements more generally. In June 2003, the resolution was also endorsed by the maintainers of UNIMARC. The "Resolution on Metadata Element Identifiers", or CORES Resolution, is an agreement among the maintenance organisations for several major metadata standards - GILS, ONIX, MARC 21, UNIMARC, CERIF, DOI®, IEEE/LOM, and Dublin Core - to identify their metadata elements using Uniform Resource Identifiers (URIs). The Uniform Resource Identifier, defined in the IETF RFC 2396 as "a compact string of characters for identifying an abstract or physical resource", has been promoted for use as a universal form of identification by the World Wide Web Consortium. The CORES Resolution, formulated at a meeting organised by the European project CORES in November 2002, included a commitment to publicise the consensus statement to a wider audience of metadata standards initiatives and to implement key points of the agreement within the following six months - specifically, to define URI assignment mechanisms, assign URIs to elements, and formulate policies for the persistence of those URIs. This article marks the passage of six months by reporting on progress made in implementing this common action plan. After presenting the text of the CORES Resolution and its three "clarifications", the article summarises the position of each signatory organisation towards assigning URIs to its metadata elements, noting any practical or strategic problems that may have emerged. These progress reports were based on input from Thomas Baker, José Borbinha, Eliot Christian, Erik Duval, Keith Jeffery, Rebecca Guenther, and Norman Paskin. The article closes with a few general observations about these first steps towards the clarification of shared conventions for the identification of metadata elements and perhaps, one can hope, towards the ultimate goal of improving interoperability among a diversity of metadata communities.

Type

a

0.0022374375 = product of:
  0.004474875 = sum of:
    0.004474875 = product of:
      0.00894975 = sum of:
        0.00894975 = weight(_text_:a in 1249) [ClassicSimilarity], result of:
          0.00894975 = score(doc=1249,freq=14.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.1685276 = fieldWeight in 1249, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1249)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The Dublin Core metadata community and the INDECS/DOI community of authors, rights holders, and publishers are seeking common ground in the expression of metadata for information resources. Recent meetings at the 6th Dublin Core Workshop in Washington DC sketched out common models for semantics (informed by the requirements articulated in the IFLA Functional Requirements for the Bibliographic Record) and conventions for knowledge representation (based on the Resource Description Framework under development by the W3C). Further development of detailed requirements is planned by both communities in the coming months with the aim of fully representing the metadata needs of each. An open "Schema Harmonization" working group has been established to identify a common framework to support interoperability among these communities. The present document represents a starting point identifying historical developments and common requirements of these perspectives on metadata and charts a path for harmonizing their respective conceptual models. It is hoped that collaboration over the coming year will result in agreed semantic and syntactic conventions that will support a high degree of interoperability among these communities, ideally expressed in a single data model and using common, standard tools.

Type

a

0.0021393995 = product of:
  0.004278799 = sum of:
    0.004278799 = product of:
      0.008557598 = sum of:
        0.008557598 = weight(_text_:a in 1192) [ClassicSimilarity], result of:
          0.008557598 = score(doc=1192,freq=20.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.16114321 = fieldWeight in 1192, product of:
              4.472136 = tf(freq=20.0), with freq of:
                20.0 = termFreq=20.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.03125 = fieldNorm(doc=1192)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

In the June 2003 issue of D-Lib Magazine, Kenney et al. (2003) discuss a comparative study between Cornell's email reference staff and Google's Answers service. This interesting study provided insights on the potential impact of "computing and simple algorithms combined with human intelligence" for library reference services. As mentioned in the Kenney et al. article, Bill Arms (2000) had discussed the possibilities of automated digital libraries in an even earlier D-Lib article. Arms discusses not only automating reference services, but also another library function that seems to inspire lively debates about automation-metadata creation. While intended to illuminate, these debates sometimes generate more heat than light. In an effort to explore the potential for automating metadata generation, the Digital Knowledge Center (DKC) of the Sheridan Libraries at The Johns Hopkins University developed and tested an automated name authority control (ANAC) tool. ANAC represents a component of a digital workflow management system developed in connection with the digital Lester S. Levy Collection of Sheet Music. The evaluation of ANAC followed the spirit of the Kenney et al. study that was, as they stated, "more exploratory than scientific." These ANAC evaluation results are shared with the hope of fostering constructive dialogue and discussions about the potential for semi-automated techniques or frameworks for library functions and services such as metadata creation. The DKC's research agenda emphasizes the development of tools that combine automated processes and human intervention, with the overall goal of involving humans at higher levels of analysis and decision-making. Others have looked at issues regarding the automated generation of metadata. A session at the 2003 Joint Conference on Digital Libraries was devoted to automatic metadata creation, and a session at the 2004 conference addressed automated name disambiguation. Commercial vendors such as OCLC, Marcive, and LTI have long used automated techniques for matching names to Library of Congress authority records. We began developing ANAC as a component of a larger suite of open source tools to support workflow management for digital projects. This article describes the goals for the ANAC tool, provides an overview of the metadata records used for testing, describes the architecture for ANAC, and concludes with discussions of the methodology and evaluation of the experiment comparing human cataloging and ANAC-generated results.

Type

a

0.0021343792 = product of:
  0.0042687585 = sum of:
    0.0042687585 = product of:
      0.008537517 = sum of:
        0.008537517 = weight(_text_:a in 1257) [ClassicSimilarity], result of:
          0.008537517 = score(doc=1257,freq=26.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.16076508 = fieldWeight in 1257, product of:
              5.0990195 = tf(freq=26.0), with freq of:
                26.0 = termFreq=26.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.02734375 = fieldNorm(doc=1257)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Over the past three years, the Dublin Core Metadata Initiative has achieved a broad international consensus on the semantics of a simple element set for describing electronic resources. Since the first workshop in March 1995, which was reported in the very first issue of D-Lib Magazine, Dublin Core has been the topic of perhaps a dozen articles here. Originally intended to be simple and intuitive enough for authors to tag Web pages without special training, Dublin Core is being adapted now for more specialized uses, from government information and legal deposit to museum informatics and electronic commerce. To meet such specialized requirements, Dublin Core can be customized with additional elements or qualifiers. However, these refinements can compromise interoperability across applications. There are tradeoffs between using specific terms that precisely meet local needs versus general terms that are understood more widely. We can better understand this inevitable tension between simplicity and complexity if we recognize that metadata is a form of human language. With Dublin Core, as with a natural language, people are inclined to stretch definitions, make general terms more specific, specific terms more general, misunderstand intended meanings, and coin new terms. One goal of this paper, therefore, will be to examine the experience of some related ways to seek semantic interoperability through simplicity: planned languages, interlingua constructs, and pidgins. The problem of semantic interoperability is compounded when we consider Dublin Core in translation. All of the workshops, documents, mailing lists, user guides, and working group outputs of the Dublin Core Initiative have been in English. But in many countries and for many applications, people need a metadata standard in their own language. In principle, the broad elements of Dublin Core can be defined equally well in Bulgarian or Hindi. Since Dublin Core is a controlled standard, however, any parallel definitions need to be kept in sync as the standard evolves. Another goal of the paper, then, will be to define the conceptual and organizational problem of maintaining a metadata standard in multiple languages. In addition to a name and definition, which are meant for human consumption, each Dublin Core element has a label, or indexing token, meant for harvesting by search engines. For practical reasons, these machine-readable tokens are English-looking strings such as Creator and Subject (just as HTML tags are called HEAD, BODY, or TITLE). These tokens, which are shared by Dublin Cores in every language, ensure that metadata fields created in any particular language are indexed together across repositories. As symbols of underlying universal semantics, these tokens form the basis of semantic interoperability among the multiple Dublin Cores. As long as we limit ourselves to sharing these indexing tokens among exact translations of a simple set of fifteen broad elements, the definitions of which fit easily onto two pages, the problem of Dublin Core in multiple languages is straightforward. But nothing having to do with human language is ever so simple. Just as speakers of various languages must learn the language of Dublin Core in their own tongues, we must find the right words to talk about a metadata language that is expressable in many discipline-specific jargons and natural languages and that inevitably will evolve and change over time.

Type

a

0.0020714647 = product of:
  0.0041429293 = sum of:
    0.0041429293 = product of:
      0.008285859 = sum of:
        0.008285859 = weight(_text_:a in 3870) [ClassicSimilarity], result of:
          0.008285859 = score(doc=3870,freq=12.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.15602624 = fieldWeight in 3870, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3870)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Data from multiple knowledge organization systems are combined to provide a global overview of the content holdings of a small personal library. Subject headings and classification data are used to effectively map the combined book and topic space of the library. While harvested and manipulated by hand, the work reveals issues and potential solutions when using automated techniques to produce topic maps of much larger libraries. The small library visualized consists of the thirty-nine, digital, English language books found in the Osama Bin Laden (OBL) compound in Abbottabad, Pakistan upon his death. As this list of books has garnered considerable media attention, it is worth providing a visual overview of the subject content of these books - some of which is not readily apparent from the titles. Metadata from subject headings and classification numbers was combined to create book-subject maps. Tree maps of the classification data were also produced. The books contain 328 subject headings. In order to enhance the base map with meaningful thematic overlay, library holding count data was also harvested (and aggregated from duplicates). This additional data revealed the relative scarcity or popularity of individual books.

Type

a

0.0020714647 = product of:
  0.0041429293 = sum of:
    0.0041429293 = product of:
      0.008285859 = sum of:
        0.008285859 = weight(_text_:a in 5444) [ClassicSimilarity], result of:
          0.008285859 = score(doc=5444,freq=12.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.15602624 = fieldWeight in 5444, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5444)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Columbia University Libraries recently embarked on a multi-phase project to migrate nearly 4,000 records describing over 70,000 linear feet of archival material from disparate sources and formats into ArchivesSpace. This paper discusses tools and methods brought to bear in Phase 2 of this project, which required us to look closely at how to integrate a large number of legacy finding aids into the new system and merge descriptive data that had diverged in myriad ways. Using Python, XSLT, and a widely available if underappreciated resource-the Google Sheets API-archival and technical library staff devised ways to efficiently report data from different sources, and present it in an accessible, user-friendly way,. Responses were then fed back into automated data remediation processes to keep the migration project on track and minimize manual intervention. The scripts and processes developed proved very effective, and moreover, show promise well beyond the ArchivesSpace migration. This paper describes the Python/XSLT/Sheets API processes developed and how they opened a path to move beyond CSV-based reporting with flexible, ad-hoc data interfaces easily adaptable to meet a variety of purposes.

Type

a

0.0020506454 = product of:
  0.004101291 = sum of:
    0.004101291 = product of:
      0.008202582 = sum of:
        0.008202582 = weight(_text_:a in 6066) [ClassicSimilarity], result of:
          0.008202582 = score(doc=6066,freq=6.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.1544581 = fieldWeight in 6066, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0546875 = fieldNorm(doc=6066)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

One hundred and one experts in resource description convened in Washington, D.C., November 2 through November 4, 1998, for the sixth Dublin Core Metadata Workshop. The registrants represented 16 countries on 4 continents, and many disciplines. As with previous workshops, many new issues were opened, and vigorous debate was a hallmark of the event. Unlike previous workshops, the focus of DC-6 was not to resolve questions in plenary meetings, but rather to identify unresolved issues and assign them to formal working groups for resolution. The result of this process was an ambitious workplan for 1999. This report summarizes that workplan, highlights the progress that has made been on the workplan, and identifies a few significant projects that exemplify this progress.

Type

a

0.0020506454 = product of:
  0.004101291 = sum of:
    0.004101291 = product of:
      0.008202582 = sum of:
        0.008202582 = weight(_text_:a in 1210) [ClassicSimilarity], result of:
          0.008202582 = score(doc=1210,freq=24.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.1544581 = fieldWeight in 1210, product of:
              4.8989797 = tf(freq=24.0), with freq of:
                24.0 = termFreq=24.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.02734375 = fieldNorm(doc=1210)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The Semantic Web activity is a W3C project whose goal is to enable a 'cooperative' Web where machines and humans can exchange electronic content that has clear-cut, unambiguous meaning. This vision is based on the automated sharing of metadata terms across Web applications. The declaration of schemas in metadata registries advance this vision by providing a common approach for the discovery, understanding, and exchange of semantics. However, many of the issues regarding registries are not clear, and ideas vary regarding their scope and purpose. Additionally, registry issues are often difficult to describe and comprehend without a working example. This article will explore the role of metadata registries and will describe three prototypes, written by the Dublin Core Metadata Initiative. The article will outline how the prototypes are being used to demonstrate and evaluate application scope, functional requirements, and technology solutions for metadata registries. Metadata schema registries are, in effect, databases of schemas that can trace an historical line back to shared data dictionaries and the registration process encouraged by the ISO/IEC 11179 community. New impetus for the development of registries has come with the development activities surrounding creation of the Semantic Web. The motivation for establishing registries arises from domain and standardization communities, and from the knowledge management community. Examples of current registry activity include:
* Agencies maintaining directories of data elements in a domain area in accordance with ISO/IEC 11179 (This standard specifies good practice for data element definition as well as the registration process. Example implementations are the National Health Information Knowledgebase hosted by the Australian Institute of Health and Welfare and the Environmental Data Registry hosted by the US Environmental Protection Agency.); * The xml.org directory of the Extended Markup Language (XML) document specifications facilitating re-use of Document Type Definition (DTD), hosted by the Organization for the Advancement of Structured Information Standards (OASIS); * The MetaForm database of Dublin Core usage and mappings maintained at the State and University Library in Goettingen; * The Semantic Web Agreement Group Dictionary, a database of terms for the Semantic Web that can be referred to by humans and software agents; * LEXML, a multi-lingual and multi-jurisdictional RDF Dictionary for the legal world; * The SCHEMAS registry maintained by the European Commission funded SCHEMAS project, which indexes several metadata element sets as well as a large number of activity reports describing metadata related activities and initiatives. Metadata registries essentially provide an index of terms. Given the distributed nature of the Web, there are a number of ways this can be accomplished. For example, the registry could link to terms and definitions in schemas published by implementers and stored locally by the schema maintainer. Alternatively, the registry might harvest various metadata schemas from their maintainers. Registries provide 'added value' to users by indexing schemas relevant to a particular 'domain' or 'community of use' and by simplifying the navigation of terms by enabling multiple schemas to be accessed from one view. An important benefit of this approach is an increase in the reuse of existing terms, rather than users having to reinvent them. Merging schemas to one view leads to harmonization between applications and helps avoid duplication of effort. Additionally, the establishment of registries to index terms actively being used in local implementations facilitates the metadata standards activity by providing implementation experience transferable to the standards-making process.

Type

a

0.0020296127 = product of:
  0.0040592253 = sum of:
    0.0040592253 = product of:
      0.008118451 = sum of:
        0.008118451 = weight(_text_:a in 1282) [ClassicSimilarity], result of:
          0.008118451 = score(doc=1282,freq=2.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.15287387 = fieldWeight in 1282, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.09375 = fieldNorm(doc=1282)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Type

a

0.0020296127 = product of:
  0.0040592253 = sum of:
    0.0040592253 = product of:
      0.008118451 = sum of:
        0.008118451 = weight(_text_:a in 3893) [ClassicSimilarity], result of:
          0.008118451 = score(doc=3893,freq=8.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.15287387 = fieldWeight in 3893, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=3893)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

In 2017, the University of Virginia (U.Va.) will launch a two year initiative to celebrate the bicentennial anniversary of the University's founding in 1819. The U.Va. Library is participating in this event by digitizing some 20,000 photographs and negatives that document student life on the U.Va. grounds in the 1960s and 1970s. Metadata librarians and archivists are well-versed in the challenges associated with generating digital content and accompanying description within the context of limited resources. This paper describes how technology and new approaches to metadata design have enabled the University of Virginia's Metadata Analysis and Design Department to rapidly and successfully generate accurate description for these digital objects. Python's pandas module improves efficiency by cleaning and repurposing data recorded at digitization, while the lxml module builds MODS XML programmatically from CSV tables. A simplified technique for subject heading selection and assignment in Google Sheets provides a collaborative environment for streamlined metadata creation and data quality control.

Type

a

0.0019633435 = product of:
  0.003926687 = sum of:
    0.003926687 = product of:
      0.007853374 = sum of:
        0.007853374 = weight(_text_:a in 1264) [ClassicSimilarity], result of:
          0.007853374 = score(doc=1264,freq=22.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.14788237 = fieldWeight in 1264, product of:
              4.690416 = tf(freq=22.0), with freq of:
                22.0 = termFreq=22.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.02734375 = fieldNorm(doc=1264)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Defining metadata as "data about data" provokes more questions than it answers. What are the forms of the data and metadata? Can we be more specific about the manner in which the metadata is "about" the data? Are data and metadata distinguished only in the context of their relationship? Is the nature of the relationship between the datasets declarative or procedural? Can the metadata itself be described by other data? Over the past several years, we have been engaged in a number of efforts examining the role, format, composition, and architecture of metadata for networked resources. During this time, we have noticed the tendency to be led astray by comfortable, but somewhat inappropriate, models in the non-digital information environment. Rather than pursuing familiar models, there is the need for a new model that fully exploits the unique combination of computation and connectivity that characterizes the digital library. In this paper, we describe an extension of the Warwick Framework that we call Distributed Active Relationships (DARs). DARs provide a powerful model for representing data and metadata in digital library objects. They explicitly express the relationships between networked resources, and even allow those relationships to be dynamically downloadable and executable. The DAR model is based on the following principles, which our examination of the "data about data" definition has led us to regard as axiomatic: * There is no essential distinction between data and metadata. We can only make such a distinction in terms of a particular "about" relationship. As a result, what is metadata in the context of one "about" relationship may be data in another. * There is no single "about" relationship. There are many different and important relationships between data resources. * Resources can be related without regard for their location. The connectivity in networked information architectures makes it possible to have data in one repository describe data in another repository. * The computational power of the networked information environment makes it possible to consider active or dynamic relationships between data sets. This adds considerable power to the "data about data" definition. First, data about another data set may not physically exist, but may be automatically derived. Second, the "about" relationship may be an executable object -- in a sense interpretable metadata. As will be shown, this provides useful mechanisms for handling complex metadata problems such as rights management of digital objects. The remainder of this paper describes the development and consequences of the DAR model. Section 2 reviews the Warwick Framework, which is the basis for the model described in this paper. Section 3 examines the concept of the Warwick Framework Catalog, which provides a mechanism for expressing the relationships between the packages in a Warwick Framework container. With that background established, section 4 generalizes the Warwick Framework by removing the restriction that it only contains "metadata". This allows us to consider digital library objects that are aggregations of (possibly distributed) data sets, with the relationships between the data sets expressed using a Warwick Framework Catalog. Section 5 further extends the model by describing Distributed Active Relationships (DARs). DARs are the explicit relationships that have the potential to be executable, as alluded to earlier. Finally, section 6 describes two possible implementations of these concepts.

Type

a

0.001913537 = product of:
  0.003827074 = sum of:
    0.003827074 = product of:
      0.007654148 = sum of:
        0.007654148 = weight(_text_:a in 1256) [ClassicSimilarity], result of:
          0.007654148 = score(doc=1256,freq=16.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.14413087 = fieldWeight in 1256, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.03125 = fieldNorm(doc=1256)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

This article describes an approach for linking geographically distributed collections of metadata so that they are searchable as a single collection. We describe the infrastructure, which uses standard Internet protocols such as the Lightweight Directory Access Protocol (LDAP) and the Common Indexing Protocol (CIP), to distribute queries, return results, and exchange index information. We discuss the advantages of using linked collections of authoritative metadata as an alternative to using a keyword indexing search-engine for resource discovery. We examine other architectures that use metadata for resource discovery, such as Dienst/NCSTRL, the AHDS HTTP/Z39.50 Gateway, and the ROADS initiative. Finally, we discuss research issues and future directions of the project. The Internet Scout Project, which is funded by the National Science Foundation and is located in the Computer Sciences Department at the University of Wisconsin-Madison, is charged with assisting the higher education community in resource discovery on the Internet. To that end, the Scout Report and subsequent subject-specific Scout Reports were developed to guide the U.S. higher education community to research-quality resources. The Scout Report Signpost utilizes the content from the Scout Reports as the basis of a metadata collection. Signpost consists of more than 2000 cataloged Internet sites using established standards such as Library of Congress subject headings and abbreviated call letters, and emerging standards such as the Dublin Core (DC). This searchable and browseable collection is free and freely accessible, as are all of the Internet Scout Project's services.
As well developed as both the Scout Reports and Signpost are, they cannot capture the wealth of high-quality content that is available on the Internet. An obvious next step toward increasing the usefulness of our own collection and its value to our customer base is to partner with other high-quality content providers who have developed similar collections and to develop a single, virtual collection. Project Isaac (working title) is the Internet Scout Project's latest resource discovery effort. Project Isaac involves the development of a research testbed that allows experimentation with protocols and algorithms for creating, maintaining, indexing and searching distributed collections of metadata. Project Isaac's infrastructure uses standard Internet protocols, such as the Lightweight Directory Access Protocol (LDAP) and the Common Indexing Protocol (CIP) to distribute queries, return results, and exchange index or centroid information. The overall goal is to support a single-search interface to geographically distributed and independently maintained metadata collections.

Type

a

0.001757696 = product of:
  0.003515392 = sum of:
    0.003515392 = product of:
      0.007030784 = sum of:
        0.007030784 = weight(_text_:a in 1238) [ClassicSimilarity], result of:
          0.007030784 = score(doc=1238,freq=6.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.13239266 = fieldWeight in 1238, 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=1238)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The emerging network environment brings access to an increasing population of heterogeneous repositories. Inevitably, these, have quite diverse metadata vocabularies (categorization codes, classification numbers, index and thesaurus terms). So, necessarily, the number of metadata vocabularies that are accessible but unfamiliar for any individual searcher is increasing steeply. When an unfamiliar metadata vocabulary is encountered, how is a searcher to know which codes or terms will lead to what is wanted? This paper reports work at the University of California, Berkeley, on the design and development of English language indexes to metadata vocabularies. Further details and the current status of the work can be found at the project website http://www.sims.berkeley.edu/research/metadata/

Type

a

0.0016913437 = product of:
  0.0033826875 = sum of:
    0.0033826875 = product of:
      0.006765375 = sum of:
        0.006765375 = weight(_text_:a in 1279) [ClassicSimilarity], result of:
          0.006765375 = score(doc=1279,freq=2.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.12739488 = fieldWeight in 1279, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.078125 = fieldNorm(doc=1279)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Type

a

0.0016913437 = product of:
  0.0033826875 = sum of:
    0.0033826875 = product of:
      0.006765375 = sum of:
        0.006765375 = weight(_text_:a in 2342) [ClassicSimilarity], result of:
          0.006765375 = score(doc=2342,freq=2.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.12739488 = fieldWeight in 2342, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.078125 = fieldNorm(doc=2342)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Type

a

0.0016913437 = product of:
  0.0033826875 = sum of:
    0.0033826875 = product of:
      0.006765375 = sum of:
        0.006765375 = weight(_text_:a in 1176) [ClassicSimilarity], result of:
          0.006765375 = score(doc=1176,freq=8.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.12739488 = fieldWeight in 1176, 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=1176)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The rapid growth of Internet resources and digital collections has been accompanied by a proliferation of metadata schemas, each of which has been designed based on the requirements of particular user communities, intended users, types of materials, subject domains, project needs, etc. Problems arise when building large digital libraries or repositories with metadata records that were prepared according to diverse schemas. This article (published in two parts) contains an analysis of the methods that have been used to achieve or improve interoperability among metadata schemas and applications, for the purposes of facilitating conversion and exchange of metadata and enabling cross-domain metadata harvesting and federated searches. From a methodological point of view, implementing interoperability may be considered at different levels of operation: schema level, record level, and repository level. Part I of the article intends to explain possible situations in which metadata schemas may be created or implemented, whether in individual projects or in integrated repositories. It also discusses approaches used at the schema level. Part II of the article will discuss metadata interoperability efforts at the record and repository levels.

Type

a