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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)
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Kopácsi, S. et al.: Development of a classification server to support metadata harmonization in a long term preservation system (2016)
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- Source
- Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou
-
Hajra, A. et al.: Enriching scientific publications from LOD repositories through word embeddings approach (2016)
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- Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou
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Mora-Mcginity, M. et al.: MusicWeb: music discovery with open linked semantic metadata (2016)
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- Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou
-
Tallerås, K.; Massey, D.; Husevåg, A.-S.R.; Preminger, M.; Pharo, N.: Evaluating (linked) metadata transformations across cultural heritage domains (2014)
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Suominen, O.; Hyvönen, N.: From MARC silos to Linked Data silos? (2017)
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Rannharter, N.; Teetor, S.: Maintaining an image data base : a use case based on the Digital Research Archive for Byzantium (DIFAB) (2017)
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-
White, H.: Examining scientific vocabulary : mapping controlled vocabularies with free text keywords (2013)
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- Date
- 29. 5.2015 19:09:22
-
Alves dos Santos, E.; Mucheroni, M.L.: VIAF and OpenCitations : cooperative work as a strategy for information organization in the linked data era (2018)
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- Date
- 18. 1.2019 19:13:22
-
Çelebi, A.; Özgür, A.: Segmenting hashtags and analyzing their grammatical structure (2018)
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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.
-
Tallerås, C.; Dahl, J.H.B.; Pharo, N.: User conceptualizations of derivative relationships in the bibliographic universe (2018)
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-
Ilik, V.; Storlien, J.; Olivarez, J.: Metadata makeover (2014)
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- Date
- 10. 9.2000 17:38:22
-
Pfister, E.; Wittwer, B.; Wolff, M.: Metadaten - Manuelle Datenpflege vs. Automatisieren : ein Praxisbericht zu Metadatenmanagement an der ETH-Bibliothek (2017)
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- Source
- B.I.T.online. 20(2017) H.1, S.22-25
-
Baker, T.: Dublin Core Application Profiles : current approaches (2010)
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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
-
DeZelar-Tiedman, C.: Exploring user-contributed metadata's potential to enhance access to literary works (2011)
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- Date
- 10. 9.2000 17:38:22
-
Wartburg, K. von; Sibille, C.; Aliverti, C.: Metadata collaboration between the Swiss National Library and research institutions in the field of Swiss historiography (2019)
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- Date
- 30. 5.2019 19:22:49
-
Belém, F.M.; Almeida, J.M.; Gonçalves, M.A.: ¬A survey on tag recommendation methods : a review (2017)
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- Date
- 16.11.2017 13:30:22
-
Cho, H.; Donovan, A.; Lee, J.H.: Art in an algorithm : a taxonomy for describing video game visual styles (2018)
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- Abstract
- The discovery and retrieval of video games in library and information systems is, by and large, dependent on a limited set of descriptive metadata. Noticeably missing from this metadata are classifications of visual style-despite the overwhelmingly visual nature of most video games and the interest in visual style among video game users. One explanation for this paucity is the difficulty in eliciting consistent judgements about visual style, likely due to subjective interpretations of terminology and a lack of demonstrable testing for coinciding judgements. This study presents a taxonomy of video game visual styles constructed from the findings of a 22-participant cataloging user study of visual styles. A detailed description of the study, and its value and shortcomings, are presented along with reflections about the challenges of cultivating consensus about visual style in video games. The high degree of overall agreement in the user study demonstrates the potential value of a descriptor like visual style and the use of a cataloging study in developing visual style taxonomies. The resulting visual style taxonomy, the methods and analysis described herein may help improve the organization and retrieval of video games and possibly other visual materials like graphic designs, illustrations, and animations.
-
Roy, W.; Gray, C.: Preparing existing metadata for repository batch import : a recipe for a fickle food (2018)
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- Date
- 10.11.2018 16:27:22
-
Willis, C.; Greenberg, J.; White, H.: Analysis and synthesis of metadata goals for scientific data (2012)
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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.
