Search (214 results, page 2 of 11)

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Abstract

Cornell University Library (CUL) has developed a largely automated method for providing title-level catalog access to electronic journals made available through aggregator packages. CUL's technique for automated e-journal record creation and maintenance relies largely on the conversion of externally supplied metadata into streamlined, abbreviated-level MARC records. Unlike the Cooperative Online Serials Cataloging Program's recently implemented aggregator-neutral approach to e-journal cataloging, CUL's method involves the creation of a separate bibliographic record for each version of an e-journal title in order to facilitate automated record maintenance. An indexed local field indicates the aggregation to which each title belongs and enables machine manipulation of all the records associated with a specific aggregation. Information encoded in another locally defined field facilitates the identification of all of the library's e-journal titles and allows for the automatic generation of a Web-based title list of e-journals. CUL's approach to providing title-level catalog access to its e-journal aggregations involves a number of tradeoffs in which some elements of traditional bibliographic description (such as subject headings and linking fields) are sacrificed in the interest of timeliness and affordability. URLs (Uniform Resource Locators) and holdings information are updated on a regular basis by use of automated methods that save on staff costs.

Date

10. 9.2000 17:38:22

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Abstract

This paper presents findings of a survey an the state of cataloging and metadata education. in ALA-accredited library and information science progranis in North America. The survey was conducted in response to Action Item 5.1 of the "Bibliographic Control of Web Resources: A Library of Congress Action Plan," which focuses an providing metadata education to new LIS professionals. The study found LIS programs increased their reliance an introductory courses to cover cataloging and metadata, but fewer programs than before had a cataloging course requirement. The knowledge of cataloging delivered in introductory courses was basic, and the coverage of metadata was limited to an overview. Cataloging courses showed similarity in coverage and practice and focused an print mater!als. Few cataloging educators provided exercises in metadata record creation using non-AACR standards. Advanced cataloging courses provided in-depth coverage of subject cataloging and the cataloging of nonbook resources, but offered very limited coverage of metadata. Few programs offered full courses an metadata, and even fewer offered advanced metadata courses. Metadata topics were well integrated into LIS curricula, but coverage of metadata courses varied from program to program, depending an the interests of instructors. Educators were forward-looking and agreed an the inclusion of specific knowledge and skills in metadata instruction. A series of actions were proposed to assist educators in providing students with competencies in cataloging and metadata.

Date

10. 9.2000 17:38:22

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Abstract

This paper describes the information properties of museum specimen labels and machine learning tools to automatically extract Darwin Core (DwC) and other metadata from these labels processed through Optical Character Recognition (OCR). The DwC is a metadata profile describing the core set of access points for search and retrieval of natural history collections and observation databases. Using the HERBIS Learning System (HLS) we extract 74 independent elements from these labels. The automated text extraction tools are provided as a web service so that users can reference digital images of specimens and receive back an extended Darwin Core XML representation of the content of the label. This automated extraction task is made more difficult by the high variability of museum label formats, OCR errors and the open class nature of some elements. In this paper we introduce our overall system architecture, and variability robust solutions including, the application of Hidden Markov and Naïve Bayes machine learning models, data cleaning, use of field element identifiers, and specialist learning models. The techniques developed here could be adapted to any metadata extraction situation with noisy text and weakly ordered elements.

Source

Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas

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Abstract

The digitized version of the only extant copy of Archimedes' key mathematical and scientific works contains over 6,500 images and 130 pages of transcriptions. Metadata is essential for managing, integrating and accessing these digital resources in the Web 2.0 environment. The Dublin Core Metadata Element Set meets many of our needs. It offers the needed flexibility and applicability to a variety of data sets containing different texts and images in a dynamic technical environment. The program team has continued to refine its data dictionary and elements based on the Dublin Core standard and feedback from the Dublin Core community since the 2006 Dublin Core Conference. This presentation cites the application and utility of the DCMI Standards during the final phase of this decade-long program. Since the 2006 conference, the amount of data has grown tenfold with new imaging techniques. Use of the DCMI Standards for integration across digital images and transcriptions will allow the hosting and integration of this data set and other cultural works across service providers, libraries and cultural institutions.

Source

Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas

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Date

30. 3.2002 19:45:22

Source

Revista Española de Documentaçion Cientifica. 22(1999) no.2, S.198-219

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Date

16. 7.2000 20:58:22

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Source

Library hi tech. 22(2004) no.1

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Abstract

Dublin Core is often presented as a modern form of catalog card -- a set of elements (and now qualifiers) that describe resources in a complete package. Sometimes it is proposed as an exchange format for sharing records among multiple collections. The founding principle that "every element is optional and repeatable" reinforces the notion that a Dublin Core description is to be taken as a whole. This paper, in contrast, is based on a much different premise: Dublin Core is a language. More precisely, it is a small language for making a particular class of statements about resources. Like natural languages, it has a vocabulary of word-like terms, the two classes of which -- elements and qualifiers -- function within statements like nouns and adjectives; and it has a syntax for arranging elements and qualifiers into statements according to a simple pattern. Whenever tourists order a meal or ask directions in an unfamiliar language, considerate native speakers will spontaneously limit themselves to basic words and simple sentence patterns along the lines of "I am so-and-so" or "This is such-and-such". Linguists call this pidginization. In such situations, a small phrase book or translated menu can be most helpful. By analogy, today's Web has been called an Internet Commons where users and information providers from a wide range of scientific, commercial, and social domains present their information in a variety of incompatible data models and description languages. In this context, Dublin Core presents itself as a metadata pidgin for digital tourists who must find their way in this linguistically diverse landscape. Its vocabulary is small enough to learn quickly, and its basic pattern is easily grasped. It is well-suited to serve as an auxiliary language for digital libraries. This grammar starts by defining terms. It then follows a 200-year-old tradition of English grammar teaching by focusing on the structure of single statements. It concludes by looking at the growing dictionary of Dublin Core vocabulary terms -- its registry, and at how statements can be used to build the metadata equivalent of paragraphs and compositions -- the application profile.

Date

26.12.2011 14:01:22

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Date

28. 3.2002 9:22:34

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Source

Library hi tech. 22(2004) no.1

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Source

Library hi tech. 22(2004) no.2

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Abstract

Das »Semantic Web« bildet einen der wichtigsten, aktuellen Trends in der Weiterentwicklung des World Wide Web. Ehrgeizige Vision dieser nächsten Generation des WWW ist es, durch semantische Anreicherung von Information eine neue Qualität in der Bereitstellung von Inhalten und Diensten zu erreichen und vollständig neue Anwendungsmöglichkeiten für das Web zu eröffnen. Wichtige Ziele der Entwicklung des Semantic Web sind dabei die verbesserte Unterstützung von Kooperation zwischen Menschen und Computern und die intelligente Assistenz bei der Durchführung von Aufgaben in kooperativen verteilten Informationsumgebungen. Schlüssel zur Erreichung dieser Ziele sind die Anreicherung von Daten im Web mit Metadaten, welche diese Daten in einen semantischen Kontext einbetten. Diese Kontextinformation wird durch Software-Anwendungen interpretiert und zur Informationsfilterung, Verfeinerung von Anfragen und zur Bereitstellung intelligenter Assistenten verwendet. Eine große Herausforderung stellt dabei die geeignete Modellierung und Beschreibung des Kontexts dar. Diese muss eine automatische, globale Interpretation ermöglichen, ohne dass auf ein allgemeingültiges semantisches Beschreibungsschema zurückgegriffen werden kann. Die Vereinbarung eines solchen allgemeingültigen Schemas ist in einem derart umfangreichen, heterogenen und autonomen Rahmen, wie ihn das WWW darstellt, nicht möglich.

Theme

Semantic Web

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Abstract

In diesem Kapitel wird die zentrale Bedeutung der Annotation von Webdokumenten bzw. von Ressourcen in einem Semantischen Web diskutiert. Es wird auf aktuelle Methoden und Techniken in diesem Gebiet eingegangen, insbesondere wird das Phänomen "Social Tagging" als zentrales Element eines "Social Semantic Webs" beleuchtet. Weiters wird der Frage nachgegangen, welchen Mehrwert "Tag Recommender" beim Annotationsvorgang bieten, sowohl aus Sicht des End-Users aber auch im Sinne eines kollaborativen Ontologieerstellungsprozesses. Schließlich wird ein Funktionsprinzip für einen semi-automatischen Tag-Recommender vorgestellt unter besonderer Berücksichtigung der Anwendbarkeit in einem Corporate Semantic Web.

Source

Social Semantic Web: Web 2.0, was nun? Hrsg.: A. Blumauer u. T. Pellegrini

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Abstract

This paper deals with the problem of modeling Web information resources using expert knowledge and personalized user information for improved Web searching capabilities. We propose a "Web information space" model, which is composed of Web-based information resources (HTML/XML [Hypertext Markup Language/Extensible Markup Language] documents an the Web), expert advice repositories (domain-expert-specified metadata for information resources), and personalized information about users (captured as user profiles that indicate users' preferences about experts as well as users' knowledge about topics). Expert advice, the heart of the Web information space model, is specified using topics and relationships among topics (called metalinks), along the lines of the recently proposed topic maps. Topics and metalinks constitute metadata that describe the contents of the underlying HTML/XML Web resources. The metadata specification process is semiautomated, and it exploits XML DTDs (Document Type Definition) to allow domain-expert guided mapping of DTD elements to topics and metalinks. The expert advice is stored in an object-relational database management system (DBMS). To demonstrate the practicality and usability of the proposed Web information space model, we created a prototype expert advice repository of more than one million topics/metalinks for DBLP (Database and Logic Programming) Bibliography data set. We also present a query interface that provides sophisticated querying fa cilities for DBLP Bibliography resources using the expert advice repository.

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Abstract

This article explores metadata quality issues in the creation and encoding of mappings or correlations of educational resources to K-12 achievement standards and the deployment of the metadata generated on the Semantic Web. The discussion is framed in terms of quality indicia derived from empirical studies of metadata in the Web environment. A number of forces at work in determining the quality of correlations metadata are examined including the nature of the emerging Semantic Web metadata ecosystem itself, the reliance on string values in metadata to identify achievement standards, the growing complexity of the standards environment, and the misalignment in terms of granularity between resource and declared objectives.