Automatic and manual annotation using flexible schemas for adaptation on the semantic desktop

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

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  • Eindhoven University of Technology (TU/e)
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Details

OriginalspracheEnglisch
Titel des SammelwerksInnovative Approaches for Learning and Knowledge Sharing
UntertitelFirst European Conference on Technology Enhanced Learning, EC-TEL 2006, Proceedings
Herausgeber (Verlag)Springer Verlag
Seiten88-102
Seitenumfang15
ISBN (elektronisch)978-3-540-46234-7
ISBN (Print)978-3-540-45777-0
PublikationsstatusVeröffentlicht - 2006
Veranstaltung1st European Conference on Technology Enhanced Learning, EC-TEL 2006 - Crete, Griechenland
Dauer: 1 Okt. 20064 Okt. 2006

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band4227 LNCS
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Abstract

Adaptive Hypermedia builds upon the annotation and adaptation of content. As manual annotation has proven to be the main bottleneck, all means for supporting it by reusing automatically generated metadata are helpful. In this paper we discuss two issues. The first is the integration of a generic AH authoring environment MOT into a semantic desktop environment. In this setup, the semantic desktop environment provides a rich source of automatically generated meta-data, whilst MOT provides a convenient way to enhance this meta-data manually, as needed for an adaptive course environment. Secondly, we also consider the issue of source schema heterogeneity, especially during the automatic metadata generation process, as semantic desktop metadata are generated through a lot of different tools and at different times, so that schemas are overlapping and evolving. Explicitly taking into account all versions of these schemas would require a combinatorial explosion of generation rules. This paper investigates a solution to this problem based on malleable schemas, which allow metadata generation rules to flexibly match different versions of schemas, and can thus cope with the heterogeneous and evolving desktop environment.

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Automatic and manual annotation using flexible schemas for adaptation on the semantic desktop. / Cristea, Alexandra; Hendrix, Maurice; Nejdl, Wolfgang.
Innovative Approaches for Learning and Knowledge Sharing: First European Conference on Technology Enhanced Learning, EC-TEL 2006, Proceedings. Springer Verlag, 2006. S. 88-102 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 4227 LNCS).

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Cristea, A, Hendrix, M & Nejdl, W 2006, Automatic and manual annotation using flexible schemas for adaptation on the semantic desktop. in Innovative Approaches for Learning and Knowledge Sharing: First European Conference on Technology Enhanced Learning, EC-TEL 2006, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bd. 4227 LNCS, Springer Verlag, S. 88-102, 1st European Conference on Technology Enhanced Learning, EC-TEL 2006, Crete, Griechenland, 1 Okt. 2006. https://doi.org/10.1007/11876663_9
Cristea, A., Hendrix, M., & Nejdl, W. (2006). Automatic and manual annotation using flexible schemas for adaptation on the semantic desktop. In Innovative Approaches for Learning and Knowledge Sharing: First European Conference on Technology Enhanced Learning, EC-TEL 2006, Proceedings (S. 88-102). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 4227 LNCS). Springer Verlag. https://doi.org/10.1007/11876663_9
Cristea A, Hendrix M, Nejdl W. Automatic and manual annotation using flexible schemas for adaptation on the semantic desktop. in Innovative Approaches for Learning and Knowledge Sharing: First European Conference on Technology Enhanced Learning, EC-TEL 2006, Proceedings. Springer Verlag. 2006. S. 88-102. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11876663_9
Cristea, Alexandra ; Hendrix, Maurice ; Nejdl, Wolfgang. / Automatic and manual annotation using flexible schemas for adaptation on the semantic desktop. Innovative Approaches for Learning and Knowledge Sharing: First European Conference on Technology Enhanced Learning, EC-TEL 2006, Proceedings. Springer Verlag, 2006. S. 88-102 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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AU - Cristea, Alexandra

AU - Hendrix, Maurice

AU - Nejdl, Wolfgang

PY - 2006

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N2 - Adaptive Hypermedia builds upon the annotation and adaptation of content. As manual annotation has proven to be the main bottleneck, all means for supporting it by reusing automatically generated metadata are helpful. In this paper we discuss two issues. The first is the integration of a generic AH authoring environment MOT into a semantic desktop environment. In this setup, the semantic desktop environment provides a rich source of automatically generated meta-data, whilst MOT provides a convenient way to enhance this meta-data manually, as needed for an adaptive course environment. Secondly, we also consider the issue of source schema heterogeneity, especially during the automatic metadata generation process, as semantic desktop metadata are generated through a lot of different tools and at different times, so that schemas are overlapping and evolving. Explicitly taking into account all versions of these schemas would require a combinatorial explosion of generation rules. This paper investigates a solution to this problem based on malleable schemas, which allow metadata generation rules to flexibly match different versions of schemas, and can thus cope with the heterogeneous and evolving desktop environment.

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