Search (14 results, page 1 of 1)

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Abstract

The future of bibliographic control will be collaborative, decentralized, international in scope, and Web-based. Its realization will occur in cooperation with the private sector, and with the active collaboration of library users. Data will be gathered from multiple sources; change will happen quickly; and bibliographic control will be dynamic, not static. The underlying technology that makes this future possible and necessary-the World Wide Web-is now almost two decades old. Libraries must continue the transition to this future without delay in order to retain their relevance as information providers. The Working Group on the Future of Bibliographic Control encourages the library community to take a thoughtful and coordinated approach to effecting significant changes in bibliographic control. Such an approach will call for leadership that is neither unitary nor centralized. Nor will the responsibility to provide such leadership fall solely to the Library of Congress (LC). That said, the Working Group recognizes that LC plays a unique role in the library community of the United States, and the directions that LC takes have great impact on all libraries. We also recognize that there are many other institutions and organizations that have the expertise and the capacity to play significant roles in the bibliographic future. Wherever possible, those institutions must step forward and take responsibility for assisting with navigating the transition and for playing appropriate ongoing roles after that transition is complete. To achieve the goals set out in this document, we must look beyond individual libraries to a system wide deployment of resources. We must realize efficiencies in order to be able to reallocate resources from certain lower-value components of the bibliographic control ecosystem into other higher-value components of that same ecosystem. The recommendations in this report are directed at a number of parties, indicated either by their common initialism (e.g., "LC" for Library of Congress, "PCC" for Program for Cooperative Cataloging) or by their general category (e.g., "Publishers," "National Libraries"). When the recommendation is addressed to "All," it is intended for the library community as a whole and its close collaborators.
The Library of Congress must begin by prioritizing the recommendations that are directed in whole or in part at LC. Some define tasks that can be achieved immediately and with moderate effort; others will require analysis and planning that will have to be coordinated broadly and carefully. The Working Group has consciously not associated time frames with any of its recommendations. The recommendations fall into five general areas: 1. Increase the efficiency of bibliographic production for all libraries through increased cooperation and increased sharing of bibliographic records, and by maximizing the use of data produced throughout the entire "supply chain" for information resources. 2. Transfer effort into higher-value activity. In particular, expand the possibilities for knowledge creation by "exposing" rare and unique materials held by libraries that are currently hidden from view and, thus, underused. 3. Position our technology for the future by recognizing that the World Wide Web is both our technology platform and the appropriate platform for the delivery of our standards. Recognize that people are not the only users of the data we produce in the name of bibliographic control, but so too are machine applications that interact with those data in a variety of ways. 4. Position our community for the future by facilitating the incorporation of evaluative and other user-supplied information into our resource descriptions. Work to realize the potential of the FRBR framework for revealing and capitalizing on the various relationships that exist among information resources. 5. Strengthen the library profession through education and the development of metrics that will inform decision-making now and in the future. The Working Group intends what follows to serve as a broad blueprint for the Library of Congress and its colleagues in the library and information technology communities for extending and promoting access to information resources.

Editor

Library of Congress / Working Group on the Future of Bibliographic Control

Source

http://www.loc.gov/bibliographic-future/news/lcwg-report-draft-11-30-07-final.pdf

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Abstract

Diese Publikation beschreibt die Zusammenhänge zwischen wissenshaltigen begriffsorientierten Terminologien, Ontologien, Big Data und künstliche Intelligenz.

Date

13.12.2017 14:17:22

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Abstract

If one follows any of the major cataloging or library blogs these days, it is obvious that the topic of FRBR (Functional Requirements for Bibliographic Records) has increasingly become one of major significance for the library community. What began as a proposed conceptual entity-relationship model for improving the structure of bibliographic records has become a hotly debated topic with many tangled threads that have implications not just for cataloging but for many aspects of libraries and librarianship. In the fall of 2005, the Perseus Project experimented with creating a FRBRized catalog for its current online classics collection, a collection that consists of several hundred classical texts in Greek and Latin as well as reference works and scholarly commentaries regarding these works. In the last two years, with funding from the Mellon Foundation, Perseus has amassed and digitized a growing collection of classical texts (some as image books on our own servers that will eventually be made available through Fedora), and some available through the Open Content Alliance (OCA)2, and created FRBRized cataloging data for these texts. This work was done largely as an experiment to see the potential of the FRBR model for creating a specialized catalog for classics.

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Source

http://www.bl.uk/bibliographic/pdfs/british-library-consultation-fast-abridged-dewey.pdf

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Abstract

Die Gemeinsame Normdatei (GND) ist seit April 2012 die Datei, die die im deutschsprachigen Bibliothekswesen verwendeten Normdaten enthält. Folglich muss auf Basis dieser Daten eine Repräsentation für die Darstellung als Linked Data im Semantic Web etabliert werden. Neben der eigentlichen Bereitstellung von GND-Daten im Semantic Web sollen die Daten mit bereits als Linked Data vorhandenen Datenbeständen (DBpedia, VIAF etc.) verknüpft und nach Möglichkeit kompatibel sein, wodurch die GND einem internationalen und spartenübergreifenden Publikum zugänglich gemacht wird. Dieses Dokument dient vor allem zur Beschreibung, wie die GND-Linked-Data-Repräsentation entstand und dem Weg zur Spezifikation einer eignen Ontologie. Hierfür werden nach einer kurzen Einführung in die GND die Grundprinzipien und wichtigsten Standards für die Veröffentlichung von Linked Data im Semantic Web vorgestellt, um darauf aufbauend existierende Vokabulare und Ontologien des Bibliothekswesens betrachten zu können. Anschließend folgt ein Exkurs in das generelle Vorgehen für die Bereitstellung von Linked Data, wobei die so oft zitierte Open World Assumption kritisch hinterfragt und damit verbundene Probleme insbesondere in Hinsicht Interoperabilität und Nachnutzbarkeit aufgedeckt werden. Um Probleme der Interoperabilität zu vermeiden, wird den Empfehlungen der Library Linked Data Incubator Group [LLD11] gefolgt.
Im Kapitel Anwendungsprofile als Basis für die Ontologieentwicklung wird die Spezifikation von Dublin Core Anwendungsprofilen kritisch betrachtet, um auszumachen wann und in welcher Form sich ihre Verwendung bei dem Vorhaben Bereitstellung von Linked Data anbietet. In den nachfolgenden Abschnitten wird die GND-Ontologie, welche als Standard für die Serialisierung von GND-Daten im Semantic Web dient, samt Modellierungsentscheidungen näher vorgestellt. Dabei wird insbesondere der Technik des Vocabulary Alignment eine prominente Position eingeräumt, da darin ein entscheidender Mechanismus zur Steigerung der Interoperabilität und Nachnutzbarkeit gesehen wird. Auch wird sich mit der Verlinkung zu externen Datensets intensiv beschäftigt. Hierfür wurden ausgewählte Datenbestände hinsichtlich ihrer Qualität und Aktualität untersucht und Empfehlungen für die Implementierung innerhalb des GND-Datenbestandes gegeben. Abschließend werden eine Zusammenfassung und ein Ausblick auf weitere Schritte gegeben.

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Abstract

This is a report about the use and misuse of citation data in the assessment of scientific research. The idea that research assessment must be done using "simple and objective" methods is increasingly prevalent today. The "simple and objective" methods are broadly interpreted as bibliometrics, that is, citation data and the statistics derived from them. There is a belief that citation statistics are inherently more accurate because they substitute simple numbers for complex judgments, and hence overcome the possible subjectivity of peer review. But this belief is unfounded. - Relying on statistics is not more accurate when the statistics are improperly used. Indeed, statistics can mislead when they are misapplied or misunderstood. Much of modern bibliometrics seems to rely on experience and intuition about the interpretation and validity of citation statistics. - While numbers appear to be "objective", their objectivity can be illusory. The meaning of a citation can be even more subjective than peer review. Because this subjectivity is less obvious for citations, those who use citation data are less likely to understand their limitations. - The sole reliance on citation data provides at best an incomplete and often shallow understanding of research - an understanding that is valid only when reinforced by other judgments. Numbers are not inherently superior to sound judgments.
Using citation data to assess research ultimately means using citation-based statistics to rank things.journals, papers, people, programs, and disciplines. The statistical tools used to rank these things are often misunderstood and misused. - For journals, the impact factor is most often used for ranking. This is a simple average derived from the distribution of citations for a collection of articles in the journal. The average captures only a small amount of information about that distribution, and it is a rather crude statistic. In addition, there are many confounding factors when judging journals by citations, and any comparison of journals requires caution when using impact factors. Using the impact factor alone to judge a journal is like using weight alone to judge a person's health. - For papers, instead of relying on the actual count of citations to compare individual papers, people frequently substitute the impact factor of the journals in which the papers appear. They believe that higher impact factors must mean higher citation counts. But this is often not the case! This is a pervasive misuse of statistics that needs to be challenged whenever and wherever it occurs. -For individual scientists, complete citation records can be difficult to compare. As a consequence, there have been attempts to find simple statistics that capture the full complexity of a scientist's citation record with a single number. The most notable of these is the h-index, which seems to be gaining in popularity. But even a casual inspection of the h-index and its variants shows that these are naive attempts to understand complicated citation records. While they capture a small amount of information about the distribution of a scientist's citations, they lose crucial information that is essential for the assessment of research.
The validity of statistics such as the impact factor and h-index is neither well understood nor well studied. The connection of these statistics with research quality is sometimes established on the basis of "experience." The justification for relying on them is that they are "readily available." The few studies of these statistics that were done focused narrowly on showing a correlation with some other measure of quality rather than on determining how one can best derive useful information from citation data. We do not dismiss citation statistics as a tool for assessing the quality of research.citation data and statistics can provide some valuable information. We recognize that assessment must be practical, and for this reason easily-derived citation statistics almost surely will be part of the process. But citation data provide only a limited and incomplete view of research quality, and the statistics derived from citation data are sometimes poorly understood and misused. Research is too important to measure its value with only a single coarse tool. We hope those involved in assessment will read both the commentary and the details of this report in order to understand not only the limitations of citation statistics but also how better to use them. If we set high standards for the conduct of science, surely we should set equally high standards for assessing its quality.

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Abstract

In this paper, we describe in detail the Information Content Evaluation Map (ICE-Map Visualization, formerly referred to as IC Difference Analysis). The ICE-Map Visualization is a visual data mining approach for all kinds of concept hierarchies that uses statistics about the concept usage to help a user in the evaluation and maintenance of the hierarchy. It consists of a statistical framework that employs the the notion of information content from information theory, as well as a visualization of the hierarchy and the result of the statistical analysis by means of a treemap.

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Abstract

The increasingly pervasive nature of the Web, expanding to devices and things in everydaylife, along with new trends in Artificial Intelligence call for new paradigms and a new look onKnowledge Representation and Processing at scale for the Semantic Web. The emerging, but stillto be concretely shaped concept of "Knowledge Graphs" provides an excellent unifying metaphorfor this current status of Semantic Web research. More than two decades of Semantic Webresearch provides a solid basis and a promising technology and standards stack to interlink data,ontologies and knowledge on the Web. However, neither are applications for Knowledge Graphsas such limited to Linked Open Data, nor are instantiations of Knowledge Graphs in enterprises- while often inspired by - limited to the core Semantic Web stack. This report documents theprogram and the outcomes of Dagstuhl Seminar 18371 "Knowledge Graphs: New Directions forKnowledge Representation on the Semantic Web", where a group of experts from academia andindustry discussed fundamental questions around these topics for a week in early September 2018,including the following: what are knowledge graphs? Which applications do we see to emerge?Which open research questions still need be addressed and which technology gaps still need tobe closed?

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Date

22. 3.2018 17:30:43

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Abstract

"Europeana.eu is about ideas and inspiration. It links you to 6 million digital items." This is the opening statement taken from the Europeana WWW-site (http://www.europeana.eu/portal/aboutus.html), and it clearly is concerned with the mission of Europeana - without, however, being over-explicit as to the precise nature of that mission. Europeana's current logo, too, has a programmatic aspect: the slogan "Think Culture" clearly again is related to Europeana's mission and at same time seems somewhat closer to the point: 'thinking' culture evokes notions like conceptualisation, reasoning, semantics and the like. Still, all this remains fragmentary and insufficient to actually clarify the functional scope and mission of Europeana. In fact, the author of the present contribution is convinced that Europeana has too often been described in terms of sheer quantity, as a high volume aggregation of digital representations of cultural heritage objects without sufficiently stressing the functional aspects of this endeavour. This conviction motivates the present contribution on some of the essential functional aspects of Europeana making clear that such a contribution - even if its author is deeply involved in building Europeana - should not be read as an official statement of the project or of the European Commission (which it is not!) - but as the personal statement from an information science perspective! From this perspective the opening statement is that Europeana is much more than a machine for mechanical accumulation of object representations but that one of its main characteristics should be to enable the generation of knowledge pertaining to cultural artefacts. The rest of the paper is about the implications of this initial statement in terms of information science, on the way we technically prepare to implement the necessary data structures and functionality and on the novel functionality Europeana will offer based on these elements and which go well beyond the 'traditional' digital library paradigm. However, prior to exploring these areas it may be useful to recall the notion of 'knowledge' that forms the basis of this contribution and which in turn is part of the well known continuum reaching from data via information and knowledge to wisdom.

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Abstract

In this document we provide an overall view of the state of the art in ontology alignment. It is organised as a description of the need for ontology alignment, a presentation of the techniques currently in use for ontology alignment and a presentation of existing systems. The state of the art is not restricted to any discipline and consider as some form of ontology alignment the work made on schema matching within the database area for instance. Heterogeneity problems on the semantic web can be solved, for some of them, by aligning heterogeneous ontologies. This is illustrated through a number of use cases of ontology alignment. Aligning ontologies consists of providing the corresponding entities in these ontologies. This process is precisely defined in deliverable D2.2.1. The current deliverable presents the many techniques currently used for implementing this process. These techniques are classified along the many features that can be found in ontologies (labels, structures, instances, semantics). They resort to many different disciplines such as statistics, machine learning or data analysis. The alignment itself is obtained by combining these techniques towards a particular goal (obtaining an alignment with particular features, optimising some criterion). Several combination techniques are also presented. Finally, these techniques have been experimented in various systems for ontology alignment or schema matching. Several such systems are presented briefly in the last section and characterized by the above techniques they rely on. The conclusion is that many techniques are available for achieving ontology alignment and many systems have been developed based on these techniques. However, few comparisons and few integration is actually provided by these implementations. This deliverable serves as a basis for considering further action along these two lines. It provide a first inventory of what should be evaluated and suggests what evaluation criterion can be used.