Since the data gathering from the user profile and its interaction with the content over time is important to concretize the recommendation of the appropriate learning materials, the representation of data plays a key role in this matter.
Previously, the web content was targeted for human consumption, since the meaning of the data was not machine-accessible which is why many times there were difficulties while searching particular data for relevant items. So, to generate a specific search for data located
7.2 Semantics 101 in different items required performing several steps. The first step was to generate different data, analyze the response and extract the needed information [145].
Therefore, the need to facilitate and automate the knowledge representation in web tech- nologies resulted in the Semantic Web. With the rise of Sematic Web technologies, knowledge became organized in conceptual spaces according to its meaning. And furthermore, the web was organized either for human or machine retrieval. The use of semantic web as a new generation of Web has promoted the new paradigm of World Wide Web, aiming toward automate search, reuse of web resources as machine readable resources. The contribution of semantics has promoted in the understanding of the machines, example: interoperability, applicability across agents and services, usability etc.
Fig. 7.1 Semantic Web Layers [146]
As depicted in Figure 7.1, the bottom- up layers needs to be followed when developing semantic webs[146]. The XML + NS + XMLschema is used for writing structure content with defined vocabulary. The next layer, the RDF (Resource Description Framework) + rdfschema applies basic data model like Entity Relationship Diagram (ERD) which has an Extensible Markup Language (XML) syntax in its own. Following Ontology Layers, the vocabulary defined here is used to provide a shared understanding of domain for improving the web search accuracy. The logic, proof and trust Layers establish the truth of statements, and so enable intelligent reasoning with meaningful data [147]. For using semantic web that will be shared and reused in different applications it is necessary to create conceptual boundaries for that particular domain. Ontology (further discussed in subsection 7.3) is used by all users for locating and reusing the resources as building blocks for creating meaning and furthering relationships.
Many approaches could have been used to establish concept and resource boundaries through extracting the preferred meta-data and minimizing the inevitable inconsistencies. In order to enhance knowledge based on the eLearning environment, in [148] a new approach is proposed for enriching domain ontology, by extracting concepts using a combination of contextual and semantics. The proposal in [148] follows an observation matrix, which
exploits the statistical feature extraction by using frequency of occurrence of common terms, font size and font type. Those concepts were scored further through selection of appropriate words for describing the particular item. Those concepts that were used at the highest level, are then selected as concepts for enriching the Ontology.
Another layered model for picking up new concepts and also updating the latest modi- fication in order to avoid inconsistency is suggested in [147]. The concepts were gathered from: the learning domain layer, learning resources layer and profiles of learners’ layer. The extracted concepts from the learner profile layer were used to enrich ontology with users’ profile information. Based on that information the framework suggested courses that could be of interest to users and also created a relation between users with common competences and learning goals. The automatic selection of keywords called LVD-F (Lesk Visualness and Disambiguation with Frequency of Occurrence) [149] selected the most appropriate keywords for representing video lectures and describing topic content. The selected keywords from categories and titles were tokenized. All the same words that had different meanings were passed through a filter to distinguish them. Each word then is processed to calculate visualness values and occurrence frequency. The combination of visualness and frequency of occurrence informed generation of the most appropriate word for enriching the Ontology.
7.3
Ontologies
As discussed in section 3.4, the use of Ontologies as a formal specification of shared concep- tualisation could be used through various purposes, such as for modeling learners’ knowledge background, describing the learning materials, modeling the learning objectives/outcomes[99] and also for modeling the structure of learning materials as part of a course[100], which furthermore could be part of a particular curriculum.
Cloud eLearning (CeL), for the sake of personalising the learning process for specific learners, uses the ACM Computing Classification System. The ACM CCS ontology is used because the learning materials that will be proposed to the learners, as part of the Experimental show case that is demonstrated in Chapter 9 deal with Computer Science domain, specifically with Programming Language otherwise the selection of the ontology would need to have been reconsidered. The ontology needs to be reconsidered because ACM CCS currently provides the ontology only for computing domain.
In Cloud eLearning case, the use of ACM CCS made it possible to generalize and/or specialize the learners intentions and interests. Technically, the ACM CCS uses a hier- archical approach, by constructing the concepts and their relations as topics/subtopics of the computing domain. The coverage, the user-friendliness of the interface, the use of a
7.3 Ontologies 103 hierarchical approach of controlled vocabulary and a well-planned classification system, are among the reasons prompting ACM CCS selection [150]. The partial taxonomy of ACM CCS is depicted in Figure 7.2.
Fig. 7.2 The ACM Computer Classification System - A partial tree architecture The overall process of using ACM CCS Ontology to generalize the interest of the learners in order to filter the appropriate Cloud eLearning Learning Objects (CeLLOs) is going to be described in the following sections. We selected the ACM CCS Ontology because the case which will be represented in Chapter 10 is part of computer science domain. Further, the Cloud eLearning Recommender System, proposed in chapter 5, combines the Semantic and Ontology technology in order to represent the CeL Learning materials (CeLLO), and generalize/specialize the learners’ desire, respectively. Accordingly, to this, the CeLRS in general, and the functionality of CeLRS in particular is described in the following sections.