出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：In recent years, problem solving, automatic proof and human-like test-tasking have become a
hot spot of research. This paper focus on the study of solving physical problem in Chinese.
Based on the analysis of physical corpus, it is found that the physical problem are made up of ntuples
which contain concepts and relations between concepts, and the n-tuples can be
expressed in the form of UP-graph (The graph of understanding problem), which is the semantic
expression of physical problem. UP-graph is the base of problem solving which is generated by
using physical knowledge graph (PKG). However, current knowledge graph is hard to be used
in problem solving, because it cannot store methods for solving problem. So this paper presents
a model of PKG which contains concepts and relations, in the model, concepts and relations are
split into terms and unique IDs, and methods can be easily stored in the PKG as concepts.
Based on the PKG, DKP-solving is proposed which is a novel approach for solving physical
problem. The approach combines rules, statistical methods and knowledge reasoning effectively
by integrating the deep learning and knowledge graph. The experimental results over the data
set of real physical text indicate that DKP-solving is effective in physical problem solving.
关键词：Knowledge Graph; Deep Learning; Problem Solving; & Physical Problem
