Multi-Granularity Temporal Knowledge Graph Question Answering Based on Data Augmentation and Convolutional Networks

The multi-granularity temporal knowledge graph question-answering model consists of two core tasks: question information extraction and knowledge graph embedding representation. Existing studies typically compute the relevance score between the question and the associated temporal knowledge graph to identify the answer. However, current multi-granularity temporal knowledge graph datasets are relatively scarce, and most research has not fully exploited the potential of these limited datasets, resulting in limitations in the model’s ability to capture complex temporal relationships. Additionally, existing methods often overlook the model’s ability to capture the rich semantic relationships within questions, especially when dealing with diverse and dynamically changing temporal information. To make full use of the limited datasets and help the model better capture the complex relationships in the question, this paper proposes a multi-granularity temporal knowledge graph question-answering model based on data augmentation and convolutional networks. By using semantic paraphrasing to expand the questions and obtain more comprehensive question representations, and incorporating convolutional networks in the feature learning phase to capture more diverse and richer question representations, the proposed method outperforms baseline models on publicly available datasets, as demonstrated by experimental results.