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Home / programming language / Online Java Projects / A novel approach to generate MCQs from domain ontology: Considering DL semantics and open-world assumption
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A novel approach to generate MCQs from domain ontology: Considering DL semantics and open-world assumption

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SKU: PROJ7601 Categories: 2017 Projects, Datamining Projects, Final Year Projects, Online Java Projects, programming language Tags: Android Project, Asp.Net Project, C# Project, Computer Engineering Final Year Projects, Computer Science Final Year Projects, Electronics Projects Engineering Students Final Year, Engineering Student Project Ideas, Final Semester Projects, Final Year Project Center, Final Year Projects, ieee ECE Projects, ieee EEE Projects, ieee Final Year Projects, IEEE Projects, ieee Projects Networking, Image Processing Projects, J2EE Project, Java Project, Java Project Code, Matlab Project, PHP Project, Students Projects
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A novel approach to generate MCQs from domain ontology: Considering DL semantics and open-world assumption

Abstract— Ontologies are structures, used for knowledge representation, which model domain knowledge in the form of concepts, roles, instances and their relationships. This knowledge can be exploited by an assessment system in the form of multiple choice questions (MCQs). The existing approaches which use ontologies expressed in the Web Ontology Language (OWL) for MCQ generation, are limited to simple concept related questions — “What is C?” or “Which of the following is an example of C?” (where C is a concept symbol) — or analogy type questions involving roles. There are no efforts in the literature which make use of the terminological axioms in the ontology such as existential, universal and cardinality restrictions on concepts and roles for MCQ generation. Also, there are no systematic methods for generating incorrect answers (distractors) from ontologies. Distractor generation process has to be given much importance, since the generated distractors determine the quality and hardness of an MCQ. Two new MCQ generation approaches, which generate MCQs that are very useful and realistic in conducting assessment tests, and the corresponding distractor generating techniques. Our distractor generation techniques, unlike other methods, consider the open-world assumption, so that the generated MCQs will always be valid (falsity of distractors is ensured). Furthermore, we present a measure to determine the difficulty level (a value between 0 and 1) of the generated MCQs. < final year projects >

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