TERQA: Question answering over knowledge graph considering precise dependencies of temporal information on vectors

Abstract

Time questions involve explicit and implicit constraints as well as complex time interval interactions, making them critical criteria for measuring the effect of knowledge base question answering. Although attention to temporal questions has spurred the development of temporal knowledge graphs, existing studies have focused on the simple splicing and fusion of temporal information with question or knowledge base embeddings, losing sight of the hidden interaction features between temporal information and embedded vectors. In this paper, we proposed TERQA, a temporal knowledge base question-answering approach to explore precise spatial dependencies between temporal information and embedded vectors. The exploration of the deep dependency between time and embedded vectors was divided into two stages. In the first stage, the Transformer model of depth extraction was employed to extract richer features from questions and the representation was enhanced with temporal information; in the second stage, high-level capsules were adopted to extract the low-level vector features for detailed pose determination, allowing a more precise deep dependency of temporal facts on embedded vectors. We conducted an experiment using two temporal question answering datasets, TempQuestions and CronQuestions, and the results showed that accuracy for TERQA improved 11.3% from baseline on the dataset TempQuestions with higher annotated information. Additionally, the adapted TERQA also showed varying degrees of improvements over the baseline in the larger but simply annotated dataset CronQuestions.