Abstract:
Automatic question generation in Knowledge Graphs (KG) is a novel idea. KG is
the graphical representation of knowledge bases where knowledge base is world
knowledge. Intelligent Tutoring System (ITS) is a computer program that uses
artificial intelligence techniques to improve learning process in the knowledge
domain. An ITS provides adaptive learning instruction and feedback to students in
problem solving process. KG can be used in Intelligent Tutoring Systems for
education purposes. In this research we address the problem of automatically
generating knowledge questions from a KG with assigned difficulty level (easy and
hard). Questions of this kind have ample applications, for instance, in Intelligent
Tutoring Systems, to evaluate the knowledge of students in a specific domain.
Automatic question generation can reduce human effort as making questions
manually is resource and time consuming. To solve the problem, we propose a novel
approach of generating questions automatically. To generate questions, we first
select a domain for the knowledge graph; then process the KG to generate structured
triple-pattern triples, which are then used to generate questions. A key challenge is
estimating how to generate easy and difficult questions. To do this, we used single
triple for easy questions and more than one triple for difficult questions which means
that using only one triple involves two nodes and more than one triple contains more
than two nodes, so when the graph is traversed for easy questions only two nodes are
visited and for difficult questions more than two nodes are visited which increases
the complexity.
