E-learning is the acquisition and application of knowledge that is primarily provided and assisted through electronic means. Ontology is the pillar on which the semantic web stands. They provide a common framework for organizing and sharing knowledge, making it easier for machines to process and make inferences from data. The proliferation of online content has made it increasingly difficult to differentiate between true and false information. This underscores the importance of evaluating the credibility of documents within the semantic web. Provenance can help in this as it refers to a collection of records that shed light on the activities, participants, and entities involved in creating, updating, and utilizing web-based data. In this chapter, the authors have used PROV-DM model to add provenance to our academic ontology which will help in building trust. The system generates effective decision and trustworthiness results, supporting users in retrieving authentic and up-to-date information.
TopIntroduction
E-learning is a new way to deliver well-designed, learner-centered, interactive, and supported learning environments to anyone, anywhere, and at any time. E-learning has become a widely acknowledged method of learning in recent years. Developing ontology in e-learning is essential to promote structured data utilizing provenance metadata that is embedded into ontology. Also validating the trustworthiness using the parsers and the reasoners is done to verify the relationships between datasets. In addition to factual knowledge, rules and restrictions are required to represent the semantics for inference, known as Axiomatization. Rules can be used to generate new facts from current information and to ensure that knowledge is consistent.
Figure 1. Block diagram depicting the usage of ontology and incorporation of provenance
The formation and description of the ontology using protégé (Protégé, n.d.-b) depict the relationship between the various objects as well as embedding provenance concepts, as a result, the desired response is extracted from the agents' requests.
Ontologies still lack the component of trust. We must build strategies or models (Obitko, 2007) to develop faith in them. Ontologies (Protégé, n.d.-a; Protégé Frames, n.d.) can be made more trustworthy by including provenance descriptions. Following that, we must assess the effectiveness of the resulting ontology using parameters such as recency, authenticity, source correctness, and so on. We have used PROV-DM model to add provenance to our academic ontology.
The information models to capture the provenance of scientific workflows, known as provenance models, have been of wide interest and studied in many fields, including the Semantic Web. (Butt, 2021)
Now we want to address the issues by proposing solutions that can be adopted during the research. Following the development of the ontology, we would use the PROV-DM model to incorporate provenance descriptions, and then use the parsers, reasoners, and DL queries to implement DL logic and assess its trustworthiness. In the next section, we will construct an ‘Academic ontology’:
TopCodes And Implementations: The Creation Of ‘Academic Ontology’
Protégé is being utilized for ontology development and visualization in this case, and it can be illustrated using a basic academic concept:
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‘Teacher’ is teaching ‘Modules’.
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‘Modules’ are studied by the ‘Students’.
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Individuals, classes, data attributes, and object properties have now been recognized.
Categories In Ontology
The term “class” refers to an abstract group, set, or collection of objects. In this part, we define the parent and child classes:
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Parental classes
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Individual
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Module
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Classes for children
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College students
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Teacher
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Computer Science Module
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Mathematics Module
Object Properties
The object property specifies how classes might be related to one another based on their instances. The object has two characteristics.
Data Properties
The domains of data properties are typed literals, analogous to object properties. These are they:
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Given name
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surname
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Index number
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Employee number