-
Metadata and semantics research : 9th Research Conference, MTSR 2015, Manchester, UK, September 9-11, 2015, Proceedings (2015)
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- LCSH
- Artificial intelligence
- Subject
- Artificial intelligence
-
Maule, R.W.: Cognitive maps, AI agents and personalized virtual environments in Internet learning experiences (1998)
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- Abstract
- Develops frameworks to help Internet media designers address end user information presentation preferences by advancing structures for assessing metadata design variables. Design variables are then linked to user cognitive styles. An underlying theme is that artificial intelligence methodologies may be used to help automate the Internet media design process and to provide personalized and customized experiences. User preferences concerning knowledge acquisition in online experiences provide the basis for discussions of cognitive analysis, and are extended into structural implications for media design and interaction
-
Jimenez, V.O.R.: Nuevas perspectivas para la catalogacion : metadatos ver MARC (1999)
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- Date
- 30. 3.2002 19:45:22
- Source
- Revista Española de Documentaçion Cientifica. 22(1999) no.2, S.198-219
-
Metadata and semantics research : 8th Research Conference, MTSR 2014, Karlsruhe, Germany, November 27-29, 2014, Proceedings (2014)
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- LCSH
- Artificial intelligence
- Subject
- Artificial intelligence
-
Perkins, M.: Why don't search engines work better? (1997)
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- Abstract
- Despite the proliferation of new search engines and improvements to existing ones, their use with the WWW continues to produce innumerable false hits. The reason for this is that HTML is mainly a presentation tool, and does a fairly poor job of describing the contents of a document while search engines are a long way from artificial intelligence. The use of SGML would ease the problem considerably, but is much more complex and time consuming to learn to be of general use. The alternative 'metadata' approach is proving slow to get off the ground. Researchers are investigating these and various other lines of enquiry
-
Corby, O.; Dieng, R.; Hébért, C.: ¬A conceptual graph model for W3C resource description framework (2000)
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- Series
- Lecture notes in computer science; vol.1867: Lecture notes on artificial intelligence
-
Andresen, L.: Metadata in Denmark (2000)
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- Date
- 16. 7.2000 20:58:22
-
MARC and metadata : METS, MODS, and MARCXML: current and future implications (2004)
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- Source
- Library hi tech. 22(2004) no.1
-
Moen, W.E.: ¬The metadata approach to accessing government information (2001)
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- Date
- 28. 3.2002 9:22:34
-
MARC and metadata : METS, MODS, and MARCXML: current and future implications (2004)
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- Source
- Library hi tech. 22(2004) no.1
-
MARC and metadata : METS, MODS, and MARCXML: current and future implications part 2 (2004)
0.02
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- Library hi tech. 22(2004) no.2
-
Martin, P.: Conventions and notations for knowledge representation and retrieval (2000)
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- Series
- Lecture notes in computer science; vol.1867: Lecture notes on artificial intelligence
-
Mayernik, M.S.; Acker, A.: Tracing the traces : the critical role of metadata within networked communications (2018)
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- Abstract
- The information sciences have traditionally been at the center of metadata-focused research. The US National Security Agency (NSA) intelligence documents revealed by Edward Snowden in June of 2013 brought the term "metadata" into the public consciousness. Surprisingly little discussion in the information sciences has since occurred on the nature and importance of metadata within networked communication systems. The collection of digital metadata impacts the ways that people experience social and technical communication. Without such metadata, networked communication cannot exist. The NSA leaks, and numerous recent hacks of corporate and government communications, point to metadata as objects of new scholarly inquiry. If we are to engage in meaningful discussions about our digital traces, or make informed decisions about new policies and technologies, it is essential to develop theoretical and empirical frameworks that account for digital metadata. This opinion paper presents 5 key sociotechnical characteristics of metadata within digital networks that would benefit from stronger engagement by the information sciences.
-
Vorndran, A.; Grund, S.: Metadata sharing : how to transfer metadata information among work cluster members (2021)
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- Footnote
- Teil eines Themenheftes: Artificial intelligence (AI) and automated processes for subject sccess
-
Handbook of metadata, semantics and ontologies (2014)
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- LCSH
- Artificial intelligence
- Subject
- Artificial intelligence
-
Broughton, V.: Automatic metadata generation : Digital resource description without human intervention (2007)
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- Date
- 22. 9.2007 15:41:14
-
Sowa, J.F.: Ontology, metadata, and semiotics (2000)
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- Series
- Lecture notes in computer science; vol.1867: Lecture notes on artificial intelligence
-
Liu, X.; Qin, J.: ¬An interactive metadata model for structural, descriptive, and referential representation of scholarly output (2014)
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- Abstract
- The scientific metadata model proposed in this article encompasses both classical descriptive metadata such as those defined in the Dublin Core Metadata Element Set (DC) and the innovative structural and referential metadata properties that go beyond the classical model. Structural metadata capture the structural vocabulary in research publications; referential metadata include not only citations but also data about other types of scholarly output that is based on or related to the same publication. The article describes the structural, descriptive, and referential (SDR) elements of the metadata model and explains the underlying assumptions and justifications for each major component in the model. ScholarWiki, an experimental system developed as a proof of concept, was built over the wiki platform to allow user interaction with the metadata and the editing, deleting, and adding of metadata. By allowing and encouraging scholars (both as authors and as users) to participate in the knowledge and metadata editing and enhancing process, the larger community will benefit from more accurate and effective information retrieval. The ScholarWiki system utilizes machine-learning techniques that can automatically produce self-enhanced metadata by learning from the structural metadata that scholars contribute, which will add intelligence to enhance and update automatically the publication of metadata Wiki pages.
-
Caplan, P.; Guenther, R.: Metadata for Internet resources : the Dublin Core Metadata Elements Set and its mapping to USMARC (1996)
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- Date
- 13. 1.2007 18:31:22
- Source
- Cataloging and classification quarterly. 22(1996) nos.3/4, S.43-58
-
Tennant, R.: ¬A bibliographic metadata infrastructure for the twenty-first century (2004)
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- Date
- 9.12.2005 19:22:38
- Source
- Library hi tech. 22(2004) no.2, S.175-181
