An Assembly Instruction Question Answering System Based on Knowledge Graph

Due to the widespread use of computers, assembly language is indispensable in operating computer software. Learning assembly language is particularly important. However, it requires strong logical and spatial imagination, making it difficult to learn and master. In order to enhance the efficiency of learning assembly language, this paper proposes the use of Chinese language to learn assembly language and designs a Knowledge Graph-based Assembly Language Question Answering (KG-ALQA) System. Taking the assembly language of the 8051 microcontroller as an example, firstly, we construct a knowledge graph of 8051 assembly language. Secondly, We construct a KG-ALQA system that utilizes templates to enable Chinese-to-8051 assembly language question answering (QA) functionality. Finally, to address the problem of template limitations in the KG-ALQA system, We propose a joint Transformer model to enhance the flexibility and accuracy of the system. Experimental results show that utilizing knowledge graph (KG) and deep learning techniques can effectively achieve Chinese-to-8051 assembly language QA functionality.