Research on the Knowledge Representation Method of Power News Text Based on Time Hyperplane

Abstract

To address the problem that static knowledge graphs cannot evolve over time, which leads to the conflict between entities and relations in the process of knowledge representation, this paper combines the temporal hyperplane with the translation model in knowledge representation, and proposes a knowledge representation method based on the temporal hyperplane for power news texts. First, multiple temporal hyperplanes are established and the temporal factor is added to the scoring function of the translation model; then, the entities and relation of the power news are projected onto the temporal hyperplanes, and the optimal knowledge representation is determined according to the loss function. Taking the power news text as an example, this algorithm well resolves the time-related conflicts in the power news text, and the comprehensive indexes are significantly improved on the time-related triplets.