Multi-Granularity Temporal Knowledge Graph Question Answering Based on Data Augmentation and Convolutional Networks  
[PERSON]  
1 Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China; [EMAIL_ADDRESS] (Y.L. ); [EMAIL_ADDRESS] (L.W. ); [EMAIL_ADDRESS] (D.J.) 1 Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China; [EMAIL_ADDRESS] (Y.  
dynamic nature of relationships. Previous temporal knowledge graph datasets such as TempQuestions [4], CRONQUESTIONS [5], and TempoQR [6] typically use yearly granularity, neglecting finer temporal details and lacking time sensitivity in addressing real-world issues. Some researchers have proposed multi-granularity temporal knowledge graph datasets, such as MULTIQ [7], which enhance event timeliness by incorporating richer temporal granularity such as year, month, and day. Although MULTIQ [7] has a large dataset, multi-granularity temporal datasets are still scarce, and it is necessary to explore how to better learn data features to improve performance. Current temporal knowledge graph question-answering (KGQA) methods can be divided into two categories: those that use semantic frameworks and those that use embeddings. The decomposition method using semantic frameworks involves breaking the original question into a non-temporal question and a temporal constraint question, and then utilizing traditional KGQA methods to find candidate answers for the sub-questions. Finally, temporal constraints are applied to filter and produce the final answer. For example, TEQUILA [8] decomposes the question into simpler sub-questions with some form of rewriting, while SF-TQA [9] establishes constraints and their possible interpretations, exploring relevant knowledge graph (KG) facts to build these constraints. Methods using semantic frameworks decompose the question using predefined templates, which limits their generalization ability. This approach requires human experts to create rules, and it is not suitable for addressing complex questions. Additionally, it necessitates the creation of an extensive template library, and the expansion of templates is time-consuming and labor-intensive. Embedding-based methods, on the other hand, use vector similarity and relevance scoring functions to find answers related to the question. As shown in Figure 1, these methods map entities and relationships into low-dimensional vector spaces, allowing computers to better understand and manipulate the information within the knowledge graph. There are many knowledge graph embedding methods currently available. TransE [10] can solve one-to-one relationships but struggles with one-to-many, many-to-one, and many-to-many relationships. TransH [11] can partially address many-to-many relationships, while ComplEx [12] introduces complex numbers into knowledge graph embeddings (KGEs) for the first time, solving more complex symmetric types beyond just symmetric and asymmetric relationships. The main contributions of this paper include the following:  
(1) A temporal knowledge graph question-answering framework is constructed, utilizing data augmentation through question paraphrasing to enhance the model's ability to understand different ways of phrasing. The diverse set of paraphrased questions helps  
Figure 1: Basic process of temporal knowledge graph question answering. the system capture the complex dynamic relationships between entities and events in the temporal knowledge graph, improving the performance of the question-answering system in complex temporal reasoning tasks. (2) Leveraging a deep learning framework, convolutional neural networks (CNNs) are introduced during the question vector representation phase. Through convolution operations, the model extracts richer temporal features from the raw data, capturing the dynamic relationships between entities at different time points and enhancing the understanding of complex relationships.