Research on Design and Implementation of Knowledge Map Storage Access System Based on Ontology Reasoning

Abstract

In order to support the intelligent combat capability of future wars, the network information system needs in-depth research and exploration in the field of artificial intelligence. This paper designs a distributed aggregation storage mode with the characteristics of balanced storage load distribution and local node aggregation storage under Big Table model. A distributed parallel query engine that uses the Group-By mode to distribute parallel computation query trees. A knowledge map completion method based on Bayesian inference is proposed. Bayesian probabilistic inference theory and RDF implication inference rules are used to jointly infer the potential relationships between entity nodes to predict the relationship between new nodes and original nodes, thus improving the mining efficiency of potential factors in the model and the accuracy of prediction of unknown relationships. Based on Big Table model, the entity sets stored row by row are evenly divided and stored through random prefix and pre-partition operations to realize load balancing. At the same time, random prefixes can be uniformly distributed to nodes for storing entities of the same type, and aggregated by entity category on a single node. With the continuous development of knowledge map technology, future knowledge map learning and reasoning technology can be integrated with new fields such as machine depth learning, cloud computing, block chain, big data, biological genetic engineering, etc. to play an important social value.