A Knowledge Representation Method for Question Answering Service in Mobile Edge Computing Environment

Driven by the rapid development of mobile computing and the Internet of Things, the number of devices connected to Internet has increased dramatically in recent years. Such development has generated massive amounts of data and highlighted the importance and urgency of using the accumulated big data to improve frequently used services. Deploying the question answering service in the mobile edge computing environment is considered a good way to make efficient use of the data and improve user experiences. Powered by the breakthroughs of deep learning technologies, question answering system based on knowledge graph (KBQA) has flourished in recent years. Knowledge representation, as a key technology of KBQA, can express the knowledge graph as the vectors containing more semantic information and thereby improve the accuracy of the question answering system. This paper proposes a knowledge representation method that integrates more features than the traditional methods. In our method, knowledge is represented as a combination of a structured vector reflecting the target triple and the domain information around the entity. By representing richer semantic vectors, our method outweighs TransE, ConvE, and KBAT, in terms of link prediction.