An Infrastructure for Acquiring High Quality Semantic Metadata

Lei, Yuangui; Sabou, Marta; López, Vanessa; Zhu, Jianhan; Uren, Victoria; Motta, Enrico

doi:10.1007/11762256_19

Cited by 14 publications

(10 citation statements)

References 17 publications

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“…Quality metadata also attaches a single semantic identifier to each entity regardless of different names or labels with which the entity is referred to. 5 As presented, the quality of metadata reflects the degree to which the metadata in question perform the core bibliographic functions of discovery, use, provenance, currency, authenticity, and administration. In other words, the principal purpose of metadata is to a large degree related to that of traditional online library catalogs and databases in finding, identifying, selecting, and obtaining items.…”

Section: Functional Perspectives On Metadata Qualitymentioning

confidence: 99%

Metadata Quality in Digital Repositories: A Survey of the Current State of the Art

Park

2009

Cataloging & Classification Quarterly

111

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This study presents the current state of research and practice on metadata quality through focus on the functional perspective on metadata quality, measurement, and evaluation criteria coupled with mechanisms for improving metadata quality. Quality metadata reflect the degree to which the metadata in question perform the core bibliographic functions of discovery, use, provenance, currency, authentication, and administration. The functional perspective is closely tied to the criteria and measurements used for assessing metadata quality. Accuracy, completeness, and consistency are the most common criteria used in measuring metadata quality in the literature. Guidelines embedded within a Web form or template perform a valuable function in improving the quality of the metadata. Results of the study indicate a pressing need for the building of a common data model that is interoperable across digital repositories.

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Section: Functional Perspectives On Metadata Qualitymentioning

confidence: 99%

Metadata Quality in Digital Repositories: A Survey of the Current State of the Art

Park

2009

Cataloging & Classification Quarterly

111

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“…While, to the authors' knowledge, there are no currently other initiatives to build an automatic quality assessment system for learning object metadata, in the Semantic Web area, [22] propose an automated system to assess the quality of the automatic extraction of semantic information from web pages. While the core idea, to have an automated way to ensure quality of an automated system, is similar to ours, it focus mainly in the correction of spelling errors and concept disambiguation through the use of ontologies and Semantic Web tools.…”

Section: Related Workmentioning

confidence: 99%

Towards Automatic Evaluation of Learning Object Metadata Quality

Ochôa

Duval

2006

Advances in Conceptual Modeling - Theory and Practice

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Abstract. Thanks to recent developments on automatic generation of metadata and interoperability between repositories, the production, management and consumption of learning object metadata is vastly surpassing the human capacity to review or process these metadata. However, we need to make sure that the presence of some low quality metadata does not compromise the performance of services that rely on that information. Consequently, there is a need for automatic assessment of the quality of metadata, so that tools or users can be alerted about low quality instances. In this paper, we present several quality metrics for learning object metadata. We applied these metrics to a sample of records from a real repository and compared the results with the quality assessment given to the same records by a group of human reviewers. Through correlation and regression analysis, we found that one of the metrics, the text information content, could be used as a predictor of the human evaluation. While this metric is not a definitive measurement of the "real" quality of the metadata record, we present several ways in which it can be used. We also propose new research in other quality dimensions of the learning object metadata.

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“…To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Semantic metadata are usually gathered from heterogeneous sources by using a variety of tools [13,9]. Numerous problems could happen in the generation process, which decrease their quality.…”

Section: Introductionmentioning

confidence: 99%

A framework for evaluating semantic metadata

Lei

Uren

Motta

2007

Proceedings of the 4th International Conference on Knowledge Capture

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Because poor quality semantic metadata can destroy the effectiveness of semantic web technology by hampering applications from producing accurate results, it is important to have frameworks that support their evaluation. However, there is no such framework developed to date. In this context, we proposed i) an evaluation reference model, SemRef, which sketches some fundamental principles for evaluating semantic metadata, and ii) an evaluation framework, SemEval, which provides a set of instruments to support the detection of quality problems and the collection of quality metrics for these problems. A preliminary case study of SemEval shows encouraging results.

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An Infrastructure for Acquiring High Quality Semantic Metadata

Cited by 14 publications

References 17 publications

Metadata Quality in Digital Repositories: A Survey of the Current State of the Art

Metadata Quality in Digital Repositories: A Survey of the Current State of the Art

Towards Automatic Evaluation of Learning Object Metadata Quality

A framework for evaluating semantic metadata

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