Automatically generating high quality metadata by analyzing the document code of common file types

Edvardsen, Lars Fredrik Hoimyr; Sølvberg, Ingeborg; Aalberg, Trond; Trætteberg, Hallvard

doi:10.1145/1555400.1555406

Cited by 5 publications

(16 citation statements)

References 9 publications

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“…This research has been implemented, tested and evaluated. It extends previous work [2,3] Using Automatic Metadata Generation to reduce the knowledge and time requirements for making SCORM Learning Objects using available resources, Chapter 5 presents Harvesting possibilities, Chapter 6 presents Extraction possibilities, Chapter 7 presents generation of SCORM LOs, while Chapter 8 concludes and presents future work.…”

Section: Introductionsupporting

confidence: 59%

“…Metadata is classified into 9 categories with sub-elements: (1) General, (2) Life Cycle, (3) Meta-metadata, (4) Technical, (5) Educational, (6) Rights, (7) Relation, (8) Annotation, (9) Classification. The most major drawback of using this schema is that it requires extensive user pre-training and that it is labor intensive to generate metadata records [2,3,20]. IEEE LOM metadata records are known to take more than one hour to manually label [12].…”

Section: The Ieee Lom and Scorm Standardsmentioning

confidence: 99%

“…• Extraction based on document code analysis: This approach uses analysis of the code of the document directly without the need for additional content presentation applications to interpret the document content [2,3]. This enables full and direct access to the entire document's content.…”

Section: The Ieee Lom and Scorm Standardsmentioning

confidence: 99%

“…Based on this, why should we manually reproduce something which is already available? AMG strive to avoid excessive manual efforts when similar metadata can be generated automatically based on existing data sources [2,3,4,5,6,7].…”

Section: Introductionmentioning

confidence: 99%

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Using automatic metadata generation to reduce the knowledge and time requirements for making SCORM learning objects

Edvardsen

Sølvberg

Aalberg

et al. 2009

2009 3rd IEEE International Conference on Digital Ecosystems and Technologies

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The Internet is packed with resources which can be used for educational purposes, referred to as Learning Objects (LOs). Locating the LO which is best suited for your educational purposes can be extremely challenging. This since the context surrounding the LO in regards to intended user group, educational level etc. are not included in the resource. The SCORM standard has changed this by including contextual metadata as part of the resource. However, SCORM LOs are scarcely created, much as a result of high knowledge and time requirements needed for creating the necessary metadata. This research has been using Automatic Metadata Generation tools to assist in the metadata creation process, enabling LOs to share common contextual metadata while receiving additional high quality LO specific metadata without the need for manual metadata creation efforts.

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Section: Introductionsupporting

confidence: 59%

Section: The Ieee Lom and Scorm Standardsmentioning

confidence: 99%

Section: The Ieee Lom and Scorm Standardsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Using automatic metadata generation to reduce the knowledge and time requirements for making SCORM learning objects

Edvardsen

Sølvberg

Aalberg

et al. 2009

2009 3rd IEEE International Conference on Digital Ecosystems and Technologies

View full text Add to dashboard Cite

show abstract

“…17,22,25,29,[33][34][35][36][37][38] The conceptual framework also includes a dimension for metadata quality, to highlight the importance of this factor in determining data quality and relevance for the intended use. 15,16,30,[39][40][41][42][43] Using data that are of high quality, usable, and relevant for intended use will enable the FIGURE 1 • Factors That Influence the Use of Governmental Data From Open Data Websites and the Possible Impacts on Subsequent Health Outcomes q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q…”

Section: • Methodsmentioning

confidence: 99%