Ontology mappings to improve learning resource search

Gašević, Dragan; Hatala, Marek

doi:10.1111/j.1467-8535.2006.00611.x

Cited by 66 publications

(34 citation statements)

References 16 publications

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“…The approach concentrates on mapping rules in order to deal with not only name conflicts but also structure conflicts. Various methodologies [10], [12]- [15] are proposed as a specific resolution to the problem of providing data interoperability. They differ from the proposed approach at the level of address interoperability problems, with respect to allowing for both name conflicts and structure conflicts.…”

Section: Inference Making For Learning Resource Interoperabilitymentioning

confidence: 99%

Ontology-based Metadata Integration Approach for Learning Resource Interoperability

Banlue

Arch-int

2010

2010 Sixth International Conference on Semantics, Knowledge and Grids

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Owing to the increasing demand of learning resource sharing, data integration is a promising approach to support system interoperability across heterogeneous e-Learning systems. One of the main problems to deal with in data heterogeneity is semantic conflict and although a variety of methodologies have been proposed which focus on name conflicts, structure conflicts have received little attention. This paper proposes an approach of ontology-based metadata integration for sharing learning resources between the heterogeneous Learning Management Systems. In order to achieve semantic interoperability, the standard metadata DC/LOM, WordNet and mapping rules are proposed to cope with not only name conflict but also structure conflict arising in the real-world system. The preliminary result provides the ability of learning resource interoperability between the different repositories that can establish a community-shared and reusable semantic pattern base over educational institutes. I. INTRODUCTIONThe need for the sharing of learning resources is a promising approach to provide data interoperability between the different repositories. It is relevant to a number of heterogeneous e-Learning systems. These heterogeneities are of concern to both architecture and data. Since an e-Learning system provides learning materials for the users who attend a course, it still provides different content formats such as text, image, audio, or video as well as a variety of metadata. Therefore, data integration is a possible approach in overcoming data heterogeneity.Ontology has been extensively used in data integration systems [1]; however, semantic conflict is one of the main sources of problems amongst heterogeneous ontologies. For this reason, semantic reconciliation is a major challenge for integration techniques in order to reach interoperability. There are two problems [2] which occur when the information is integrated over different ontologies. The first is metadata heterogeneity and is concerned with the meaning of described information; the second problem is instance heterogeneity and involves the different representations of instance values in the same ontology (this is also known as representation conflicts). This conflict needs to be addressed before ontology interoperation takes place. This paper concentrates on the first problem i.e. metadata heterogeneity.There are two types of conflicts related to metadata heterogeneity and they are classified as follows:

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Section: Inference Making For Learning Resource Interoperabilitymentioning

confidence: 99%

Ontology-based Metadata Integration Approach for Learning Resource Interoperability

Banlue

Arch-int

2010

2010 Sixth International Conference on Semantics, Knowledge and Grids

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“…One approach is to provide standardized domain ontologies, or at least ontologies that will be adopted by a wider community of users (Bodoff, Ben-Menachem, and Hung, 2005). An alternative is to employ results of very extensive research on ontology mapping (Gašević and Hatala, 2006;Shvaiko and Euzenat, 2005). Next, the original purpose of ontologies presumed involvement of domain experts to defined conceptualization of a domain, but ontology engineering and knowledge management practices report on the need to construct domain ontologies automatically by applying ontology learning techniques (Maedche and Staab, 2001) or to use ontology learning techniques to evaluate validity of the ontology to check whether it still properly represents the target domain (Warren, 2006).…”

Section: Domain Modelmentioning

confidence: 99%

Leveraging the Semantic Web for Adaptive Education

Kravčík

Gašević

2007

JIME

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Abstract:In the area of technology-enhanced learning reusability and interoperability issues essentially influence the productivity and efficiency of learning and authoring solutions. There are two basic approaches how to overcome these problems -one attempts to do it via standards and the other by means of the Semantic Web. In practice, these approaches meet and many existing solutions are based on ontologies that take into account the available specifications and try to integrate them. Moreover, these ontologies can help us to achieve a certain kind of consensus and to contribute to the harmonization of the existing standards. This paper aims at addressing the issues of leveraging the Semantic Web to improve mechanisms for knowledge representation in the area of adaptive education. We attempt to view adaptive education from different perspectives and consider relationship of various aspects that often appear not to be connected.

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“…Given that no universal ontology exists for the WWW, work has focused on finding semantic correspondences between similar elements of different ontologies, i.e., ontology mapping. Automatic ontology mapping is important to various practical applications such as the emerging Semantic Web [3], information transformation and data integration [2], query processing across disparate sources [7], and many others [4].…”

Section: Introductionmentioning

confidence: 99%

Ontology Mapping: An Information Retrieval and Interactive Activation Network Based Approach

Mao

2007

The Semantic Web

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Abstract. Ontology mapping is to find semantic correspondences between similar elements of different ontologies. It is critical to achieve semantic interoperability in the WWW. This paper proposes a new generic and scalable ontology mapping approach based on propagation theory, information retrieval technique and artificial intelligence model. The approach utilizes both linguistic and structural information, measures the similarity of different elements of ontologies in a vector space model, and deals with constraints using the interactive activation network. The results of pilot study, the PRIOR, are promising and scalable.

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Ontology mappings to improve learning resource search

Cited by 66 publications

References 16 publications

Ontology-based Metadata Integration Approach for Learning Resource Interoperability

Ontology-based Metadata Integration Approach for Learning Resource Interoperability

Leveraging the Semantic Web for Adaptive Education

Ontology Mapping: An Information Retrieval and Interactive Activation Network Based Approach

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