TITLE:
Knowledge Graph Question-Answering Method Based on Neural Networks
AUTHORS:
Basilia Muakuku Obono, Wei Liu
KEYWORDS:
Deep Learning, Knowledge Graphs, Neural Networks, Question Answering, Information Retrieval, Movie Dataset
JOURNAL NAME:
Open Access Library Journal,
Vol.12 No.4,
April
30,
2025
ABSTRACT: Knowledge Graph (KG) and neural network (NN) based Question-answering (QA) systems have evolved into the realm of intelligent information retrieval as they have been able to reach a high level of precision in terms of capturing user-to-query understanding. However, more traditional systems still face various stumbling blocks, including those with limited data sources, incomplete feature extraction, and low accuracy. This research represents a question-answer system to implement deep learning technology linked with knowledge graphs applied to KGs and neural networks to tackle these inefficiencies. The knowledge base is subjected to the model and it is then used to predict the necessary entities for the user to select the answer from the options displayed. From the experiments, it is clear that the implementation is more reliable and effective than previous processes. The paper introduces a prototype application that can graphically display Q&A results, and this includes a dual-data-source model for upgrading question understanding and increasing accuracy.