# Online Knowledge Learning Model Based on Gravitational Field Theory
Deep learning models often use deep neural networks and other information in knowledge bases to get a better performance. It is significant using more information in knowledge bases, such as textual information. DKRL directly learns vector embedding from entity descriptions, which is useful in knowledge graph completion by using a brief description to represent a new entity. TransC learns embedding for instances, concepts, and relations in the same space by using concepts and instance information in the knowledge base. Path-based TransE(PTransE) [27] considers path-based representation for the vector of the relation path. The mirror of inverse relation and an encoding-decoding model consisting of Recurrent Neural Networks are introduced in Semantical Symbol Mapping Embedding(SSME) [14] to improve the ability of knowledge expression. ConvE [28] applies 2D convolution directly in the embedding process, thus inducing spatial structure in the embedding space. When using the above methods for training, these methods first need to extract all entities and relationships in the knowledge base, then learn the triples in the knowledge base. The above methodstend to learn static and outdated triples, and cannot update models when new or modified triples that only include existing entities and relationships are added to the knowledge base. Although some algorithms propose using strategies to update the knowledge base, they are still retrained without using the results of previous training [29], such as [PERSON] [30] proposed to construct a update frequency predictor based on hot entities, update the knowledge from the Internet to the knowledge base, then retrain and learn the knowledge base. Obviously, this method only reduces the frequency of retraining the whole knowledge base when the knowledge changes, but cannot be learned and updated online. For example, in the knowledge graph of elderly diseases, the diseases of the elderly may change over time. Once the disease information of the elderly changes, current methods need to retrain all entities and relationships in this knowledge base to obtain the latest knowledge representation model. We need a learning method that only updates the knowledge related to this individual; other knowledge need not be updated. This method should adapt to the growth of knowledge without retraining. To learn changed knowledge online without retraining the whole knowledge base and only updating the change-related knowledge, we propose a novel translation embedding method named TransOnLine. In our method, we assumed that changed knowledge will have some impact and the impact will spread in the knowledge space, inspired by the theory of gravitational field, which describes the spatial effects of gravitation and its propagation in space. Our work can be summarized as follows: (1) We understand current knowledge representation methods of the perspective of dynamic programming and explain why current methods do not learn and update online; (2) we refer to the theory of gravitational field to define some functions we call energy functions and path-based energy propagation. Experiments on data FB15K and WN18 [31] show that the method can learn and update knowledge online and the performance in entity prediction of TransOnLine is not much different to current advanced methods.