H.3.5. Knowledge Representation Formalisms and Methods
N. Khozouie; F. Fotouhi Ghazvini; B. Minaei
Abstract
Context-aware systems must be interoperable and work across different platforms at any time and in any place. Context data collected from wireless body area networks (WBAN) may be heterogeneous and imperfect, which makes their design and implementation difficult. In this research, we introduce a model ...
Read More
Context-aware systems must be interoperable and work across different platforms at any time and in any place. Context data collected from wireless body area networks (WBAN) may be heterogeneous and imperfect, which makes their design and implementation difficult. In this research, we introduce a model which takes the dynamic nature of a context-aware system into consideration. This model is constructed according to the four-dimensional objects approach and three-dimensional events for the data collected from a WBAN. In order to support mobility and reasoning on temporal data transmitted from WBAN, a hierarchical model based on ontology is presented. It supports the relationship between heterogeneous environments and reasoning on the context data for extracting higher-level knowledge. Location is considered a temporal attribute. To support temporal entity, reification method and Allen’s algebra relations are used. Using reification, new classes Time_slice and Time_Interval and new attributes ts_time_slice and ts_time_Interval are defined in context-aware ontology. Then the thirteen logic relations of Allen such as Equal, After, Before is added by OWL-Time ontology to the properties. Integration and consistency of context-aware ontology are checked by the Pellet reasoner. This hybrid context-aware ontology is evaluated by three experts using the FOCA method based on the Goal-Question-Metrics (GQM) approach. This evaluation methodology diagnoses the ontology numerically and decreases the subjectivity and dependency on the evaluator’s experience. The overall performance quality according to completeness, adaptability, conciseness, consistency, computational efficiency and clarity metrics is 0.9137.
H.3.15.1. Adaptive hypermedia
M. Tahmasebi; F. Fotouhi; M. Esmaeili
Abstract
Personalized recommenders have proved to be of use as a solution to reduce the information overload problem. Especially in Adaptive Hypermedia System, a recommender is the main module that delivers suitable learning objects to learners. Recommenders suffer from the cold-start and the sparsity ...
Read More
Personalized recommenders have proved to be of use as a solution to reduce the information overload problem. Especially in Adaptive Hypermedia System, a recommender is the main module that delivers suitable learning objects to learners. Recommenders suffer from the cold-start and the sparsity problems. Furthermore, obtaining learner’s preferences is cumbersome. Most studies have only focused on similarity between the interest profile of a user and those of others. However, it can lead to the gray-sheep problem, in which users with consistently different opinions from the group do not benefit from this approach. On this basis, matching the learner’s learning style with the web page features and mining specific attributes is more desirable. The primary contribution of this research is to introduce a feature-based recommender system that delivers educational web pages according to the user's individual learning style. We propose an Educational Resource recommender system which interacts with the users based on their learning style and cognitive traits. The learning style determination is based on Felder-Silverman theory. Furthermore, we incorporate all explicit/implicit data features of a page and the elements contained in them that have an influence on the quality of recommendation and help the system make more effective recommendations.
