Neural Backward Chaining Logic Algorithm Based on Dynamic Knowledge Region Segmentation in Knowledge Graph Completion

With the rapid development of the Internet, the information age has arrived in a comprehensive way. The explosive growth of information data has greatly increased people's demand for knowledge management. Knowledge graph, as an effective structured knowledge representation tool, greatly enhances the organization and retrieval capabilities of information. However, in practical applications, knowledge graphs often face problems of knowledge loss and incompleteness, which severely limit their widespread application. To address this issue, this study proposes a neural backward chain inference method based on dynamic knowledge region generation. This method overcomes the bottleneck of performance degradation of traditional static methods on large datasets by introducing a dynamic knowledge region generation mechanism, which significantly improves the completion effect of knowledge graphs. The experiment was conducted on the Unified Medical Language System dataset and the Nations dataset. The results showed that when the size of the Unified Medical Language System dataset reached 2500, the accuracy of the proposed method reached 0.85. It was 6.25%, 20%, and 51.8% higher than the 0.80 of the neural backward chain inference method generated by static knowledge regions, 0.70 of the conditional theorem prover algorithms, and 0.56 of the traditional neural theorems proving algorithm, respectively. In the Nations dataset, the accuracy of the proposed method was 0.80 at the same data scale, which was significantly better than other methods. In addition, the method based on dynamic knowledge region generation reduced the iteration time to 1.4 seconds, which was about 52% and 63% higher than the static method's 2.9 s and the traditional method's 3.8 s, respectively. The research results indicate that the proposed neural backward chain logic algorithm based on dynamic knowledge region generation exhibits good performance in completing knowledge graphs.