Towards Automatic Mathematical Exercise Solving

• Main Authors: Tianyu Zhao, Chengliang Chai, Yuyu Luo, Jianhua Feng, Yan Huang, Songfan Yang, Haitao Yuan, Haoda Li, Kaiyu Li, Fu Zhu, Kang Pan
• Format: Article
• Language: English
• Published: SpringerOpen 2019-09-01
• Series: Data Science and Engineering
• Subjects: Knowledge graph; Mathematical exercise; Knowledge reasoning; Crowdsourcing
• Online Access: http://link.springer.com/article/10.1007/s41019-019-00098-w
• ISSN: 2364-1185; 2364-1541

Abstract Knowledge graphs are widely applied in many applications. Automatically solving mathematical exercises is also an interesting task which can be enhanced by knowledge reasoning. In this paper, we design MathGraph, a knowledge graph aiming to solve high school mathematical exercises. Since it requires fine-grained mathematical derivation and calculation of different mathematical objects, we design a crowdsourcing-based method to help build MathGraph. MathGraph supports massive kinds of mathematical objects, operations and constraints which may be involved in exercises. Furthermore, we propose an algorithm to align a semantically parsed exercise to MathGraph and figure out the answer automatically. Extensive experiments on real-world datasets verify the effectiveness of MathGraph.