Affiliations: [a] Department of Electronic and Communication Engineering, North China Electric Power University, Beijing, China | [b] Power Dispatching Control Center, State Grid Fuxin Power Supply Company, Fuxin, Liaoning, China
Correspondence:
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Corresponding author: Jiarong Zhang, Department of Electronic and Communication Engineering, North China Electric Power University, Beijing, China. E-mail: zjraaaaa@126.com.
Abstract: Intelligent answering technology, which enables computers to solve problems automatically, is often used to develop tutorial systems, and has a wide range of application prospects. However, due to the lack of linguistic analysis and understanding methods, there are few researches on intelligent algorithms for solving kinematics problems. Developing such an algorithm is challenging, because solving kinematics problems is a complex task that includes text understanding, problem analysis, and automatic solution. To understand all these complexities involved in kinematics problems requires background knowledge. And only when an automatic solver contains a powerful internal knowledge representation system can it perform these tasks. We, thus, develop KinRob, an tutorial system for solving kinematics problems by combining neural network and ontology. Firstly, we propose an ontology for KinRob, which defines the knowledge of kinematics, and can help the robot understand a kinematics problem. Secondly, to match the text in natural language with the ontology, we propose a novel tagging scheme based on the kinematic problem understanding model in named entity recognition (NER). Finally, extensive experiments are conducted, and the experimental results show that the performance of the proposed method on a dataset of kinematic problems from authoritative sources better than the baseline algorithms.
Keywords: Robot, kinematic problems, knowledge representation, ontology, named entity recognition
