Search (41 results, page 1 of 3)

  • × theme_ss:"Metadaten"
  • × year_i:[2010 TO 2020}
  1. Ilik, V.; Storlien, J.; Olivarez, J.: Metadata makeover (2014) 0.03
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    Date
    10. 9.2000 17:38:22
  2. Baker, T.: Dublin Core Application Profiles : current approaches (2010) 0.02
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    Source
    Wissensspeicher in digitalen Räumen: Nachhaltigkeit - Verfügbarkeit - semantische Interoperabilität. Proceedings der 11. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation, Konstanz, 20. bis 22. Februar 2008. Hrsg.: J. Sieglerschmidt u. H.P.Ohly
  3. ¬The Dublin Core Metadata Element Set (2012) 0.02
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    Type
    n
  4. Willis, C.; Greenberg, J.; White, H.: Analysis and synthesis of metadata goals for scientific data (2012) 0.01
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    Abstract
    The proliferation of discipline-specific metadata schemes contributes to artificial barriers that can impede interdisciplinary and transdisciplinary research. The authors considered this problem by examining the domains, objectives, and architectures of nine metadata schemes used to document scientific data in the physical, life, and social sciences. They used a mixed-methods content analysis and Greenberg's () metadata objectives, principles, domains, and architectural layout (MODAL) framework, and derived 22 metadata-related goals from textual content describing each metadata scheme. Relationships are identified between the domains (e.g., scientific discipline and type of data) and the categories of scheme objectives. For each strong correlation (>0.6), a Fisher's exact test for nonparametric data was used to determine significance (p < .05). Significant relationships were found between the domains and objectives of the schemes. Schemes describing observational data are more likely to have "scheme harmonization" (compatibility and interoperability with related schemes) as an objective; schemes with the objective "abstraction" (a conceptual model exists separate from the technical implementation) also have the objective "sufficiency" (the scheme defines a minimal amount of information to meet the needs of the community); and schemes with the objective "data publication" do not have the objective "element refinement." The analysis indicates that many metadata-driven goals expressed by communities are independent of scientific discipline or the type of data, although they are constrained by historical community practices and workflows as well as the technological environment at the time of scheme creation. The analysis reveals 11 fundamental metadata goals for metadata documenting scientific data in support of sharing research data across disciplines and domains. The authors report these results and highlight the need for more metadata-related research, particularly in the context of recent funding agency policy changes.
  5. Palavitsinis, N.; Manouselis, N.; Sanchez-Alonso, S.: Metadata quality in digital repositories : empirical results from the cross-domain transfer of a quality assurance process (2014) 0.01
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  6. Kopácsi, S. et al.: Development of a classification server to support metadata harmonization in a long term preservation system (2016) 0.01
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    Source
    Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou
  7. Hajra, A. et al.: Enriching scientific publications from LOD repositories through word embeddings approach (2016) 0.01
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    Source
    Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou
  8. Mora-Mcginity, M. et al.: MusicWeb: music discovery with open linked semantic metadata (2016) 0.01
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    Source
    Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou
  9. Tallerås, K.; Massey, D.; Husevåg, A.-S.R.; Preminger, M.; Pharo, N.: Evaluating (linked) metadata transformations across cultural heritage domains (2014) 0.01
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  10. Suominen, O.; Hyvönen, N.: From MARC silos to Linked Data silos? (2017) 0.01
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  11. Rannharter, N.; Teetor, S.: Maintaining an image data base : a use case based on the Digital Research Archive for Byzantium (DIFAB) (2017) 0.01
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  12. Riley, J.: Understanding metadata : what is metadata, and what is it for? (2017) 0.01
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  13. White, H.: Examining scientific vocabulary : mapping controlled vocabularies with free text keywords (2013) 0.01
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    Date
    29. 5.2015 19:09:22
  14. Alves dos Santos, E.; Mucheroni, M.L.: VIAF and OpenCitations : cooperative work as a strategy for information organization in the linked data era (2018) 0.01
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    Date
    18. 1.2019 19:13:22
  15. Çelebi, A.; Özgür, A.: Segmenting hashtags and analyzing their grammatical structure (2018) 0.01
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    Abstract
    Originated as a label to mark specific tweets, hashtags are increasingly used to convey messages that people like to see in the trending hashtags list. Complex noun phrases and even sentences can be turned into a hashtag. Breaking hashtags into their words is a challenging task due to the irregular and compact nature of the language used in Twitter. In this study, we investigate feature-based machine learning and language model (LM)-based approaches for hashtag segmentation. Our results show that LM alone is not successful at segmenting nontrivial hashtags. However, when the N-best LM-based segmentations are incorporated as features into the feature-based approach, along with context-based features proposed in this study, state-of-the-art results in hashtag segmentation are achieved. In addition, we provide an analysis of over two million distinct hashtags, autosegmented by using our best configuration. The analysis reveals that half of all 60 million hashtag occurrences contain multiple words and 80% of sentiment is trapped inside multiword hashtags, justifying the need for hashtag segmentation. Furthermore, we analyze the grammatical structure of hashtags by parsing them and observe that 77% of the hashtags are noun-based, whereas 11.9% are verb-based.
  16. Tallerås, C.; Dahl, J.H.B.; Pharo, N.: User conceptualizations of derivative relationships in the bibliographic universe (2018) 0.01
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  17. Metadata and semantics research : 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings (2016) 0.01
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  18. Pfister, E.; Wittwer, B.; Wolff, M.: Metadaten - Manuelle Datenpflege vs. Automatisieren : ein Praxisbericht zu Metadatenmanagement an der ETH-Bibliothek (2017) 0.01
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    Source
    B.I.T.online. 20(2017) H.1, S.22-25
  19. Stiller, J.; Király, P.: Multitlinguality of metadata : measuring the miltilingual degree of Europeana's metadata (2017) 0.01
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  20. DeZelar-Tiedman, C.: Exploring user-contributed metadata's potential to enhance access to literary works (2011) 0.01
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    Date
    10. 9.2000 17:38:22

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