Abstract: Instance-based matching is the process of identifying the correspondences of schema elements by comparing the instances of different data sources. It is used as an alternative option when the schema-based matching fails. Instance-based matching is applied in many application areas such as website creation and management, data warehousing, database design, and data integration. Many recent approaches focus on instance-based matching. In this paper, we propose an approach that utilizes WordNet-based measure for string domain by getting the similarity coefficient in the range of [0..1]. In previous approach, the regular expression is achieved with a good accuracy for numerical instances only and is not implemented on string instances because we need to know the meaning of string to decide if there is a match or not. The using of WordNet-based measures for string instances should guarantee to improve the effectiveness in terms of Precision (P), Recall (R) and f-measure (F). In this paper w

• Publication Date: 09-Mar-2014
• DOI: 10.15224/978-1-63248-000-2-43
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Abstract: The integration of Information and Communication Technology (ICT) in the teaching and learning process in our today’s educational environments gave birth to what is known as e-learning technology. Correct implementation of these e-learning technologies can brings about a number of potential benefits to the educational systems that can ultimately leads to achieving the desired objectives through the use of these modern and qualitative innovations. When these technologies and innovations are effectively used in the instructional process not only they will reduced the cost effectiveness of the pedagogical and other relative constrains, but can also create a conducive atmosphere for exploring effective designed components from different perspectives. An important component of these e-learning systems in use today is called the intelligent tutoring system (ITS). ITS is a software system designed using artificial intelligent techniques (comprising of Fuzzy Logic, Neural-Networks, Bayesian ne

• Publication Date: 09-Mar-2014
• DOI: 10.15224/978-1-63248-000-2-44
• Views: 0
• Downloads: 0