Research on Data Link Ontology Mapping Algorithm Based on Bayesian Network Model

Abstract

Interconnection between multiple data link systems is an urgent problem for wireless control systems. Its difficulty lies in the fact that data link messages are multi-source heterogeneous, By analyzing its sub-domain characteristics, we constructs the data message domain ontology and establishes a data link message ontology model based on Bayesian network(DLMOBN). It includes the study of nodes, directed edges and node similarity probability distribution and so on, convert multi-source heterogeneous messages into mathematical models. We propose a data link message ontology mapping algorithm, the OWL syntax is used to formally describe the acquired domain ontology, extract useful information such as concepts, attributes, and instances, and then store the information in a preset data structure, k-means algorithm is used to cluster them to form ''cluster'', which is used as a classification index to classify the similarity pair as a node in the Bayesian network, and pass the concept of the lower layer between nodes to the prior concept of the upper layer. The semantic distance, the attribute, the feature and other factors of the similar pair are used to calculate the semantic similarity. Finally, the final semantic similarity value is obtained by weighting. It is verified by experiments that the method improves the recall rate and precision, reduces the time complexity.

Computer Image Scene and Object Information Extraction based on Bayesian Network Model

“…This method is based on the local feature sub-method and the probability statistic method, and the target category is extracted by learning the local structure of the target category [19]. After analyzing the experimental results, it is also found that a construction object extraction algorithm based on local feature and probability statistics method is difficult to effectively extract the local feature of aerial image, which affects the performance of the algorithm to extract the construction object;There is no modeling environment context for extracting the target category in the test image by learning the local structure of the target category, which leads to the unsatisfactory effect of extracting the building target from the aerial image characterized by complex scenes [20].…”