A Semantic Reasoning Method Towards Ontological Model for Automated Learning Analysis
Book chapter
Okoye, Kingsley, Tawil, A., Naeem, U. and Lamine, Elyes 2015. A Semantic Reasoning Method Towards Ontological Model for Automated Learning Analysis. in: Pillay, Nelishia, Engelbrecht, Andries P., Abraham, Ajith, Plessis, Mathys C. du, Snášel, Václav and Muda, Azah Kamilah (ed.) Advances in Nature and Biologically Inspired Computing Cham, Switzerland Springer International Publishing.
Authors | Okoye, Kingsley, Tawil, A., Naeem, U. and Lamine, Elyes |
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Editors | Pillay, Nelishia, Engelbrecht, Andries P., Abraham, Ajith, Plessis, Mathys C. du, Snášel, Václav and Muda, Azah Kamilah |
Abstract | Semantic reasoning can help solve the problem of regulating the evolving and static measures of knowledge at theoretical and technological levels. The technique has been proven to enhance the capability of process models by making inferences, retaining and applying what they have learned as well as discovery of new processes. The work in this paper propose a semantic rule-based approach directed towards discovering learners interaction patterns within a learning knowledge base, and then respond by making decision based on adaptive rules centred on captured user profiles. The method applies semantic rules and description logic queries to build ontology model capable of automatically computing the various learning activities within a Learning Knowledge-Base, and to check the consistency of learning object/data types. The approach is grounded on inductive and deductive logic descriptions that allows the use of a Reasoner to check that all definitions within the learning model are consistent and can also recognise which concepts that fit within each defined class. Inductive reasoning is practically applied in order to discover sets of inferred learner categories, while deductive approach is used to prove and enhance the discovered rules and logic expressions. Thus, this work applies effective reasoning methods to make inferences over a Learning Process Knowledge-Base that leads to automated discovery of learning patterns/behaviour. |
Keywords | Process Model; Learning Process; Ontology; Semantic Reasoning |
Book title | Advances in Nature and Biologically Inspired Computing |
Year | 2015 |
Publisher | Springer International Publishing |
Publication dates | |
18 Nov 2015 | |
Publication process dates | |
Deposited | 08 Mar 2017 |
Place of publication | Cham, Switzerland |
Series | Advances in Intelligent Systems and Computing (AISC) |
Event | 7th World Congress on Nature and Biologically Inspired Computing (NaBIC2015) in |
ISBN | 978-3-319-27399-0 |
978-3-319-27400-3 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-319-27400-3_5 |
Web address (URL) | http://link.springer.com/chapter/10.1007/978-3-319-27400-3_5 |
Journal citation | 419, pp. 49-60 |
Accepted author manuscript | License CC BY-NC-ND |
https://repository.uel.ac.uk/item/853xw
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