Duration-HyTE: A Time-Aware Knowledge Representation Learning Method Based on Duration Modeling

Knowledge representation learning is the foundation of knowledge acquisition and reasoning. It is widely used in entity extraction, entity alignment, recommendation system and other fields. It has become an important issue throughout the whole process of knowledge graph construction and application. With the development of large-scale knowledge graphs containing time labels, time-aware knowledge representation learning has become one of the research hotspots in this field in recent years. Traditional time-aware knowledge representation learning methods can not effectively use the distribution of knowledge valid duration. In this paper, we propose an improved time-aware knowledge representation learning method combined hyperplane model and duration modeling to solve this problem. Firstly, we divide meta facts into persistent facts and instantaneous facts according to their valid duration. Then we model the valid duration of knowledge, so we get the calculation method of valid reliability. Finally, we propose a new knowledge representation learning method by improving score function with valid reliability. Wikidata12K and YAGO11K are two knowledge graph data sets containing time labels. We extract two new persistent facts datasets from these two datasets. We do a series of comparative experiments on these four data sets. The results show that Duration-HyTE method of link prediction and time prediction performance has been effectively promoted. Especially on Wikidata12K dataset, the accuracy of link prediction of the head entity and tail entity of the Duration-HyTE method is improved by 25.7% and 35.8% respectively compared with other traditional and advanced knowledge representation methods.