DsMtGCN: A Direction-sensitive Multi-task framework for Knowledge Graph Completion

However, due to the limitation of available resources, it is impractical to store all facts in KGs, which leads to the incompleteness [5], and the algorithms of KGC are required to solve the problem. There are a lot of researches focusing on KGC or link prediction tasks aiming to infer missing facts automatically based on known facts. Pioneering additive models [6-8] take the transformation from head entities to tail entities as a translation problem, while multiplicative models [9-12] try to measure the plausibility of unknown triplets by applying proper semantic similarity-based score function. Benefiting from the development of neural networks, several works concentrate on the deeper nonlinear interactions among entities and relations with innovative model structures [13-18]. Furthermore, some recent studies introduce GCN to take the structure information into consideration by aggregating neighborhood information [19-22], which brings significant improvement. Despite the high-performance of them, they fail to utilize direction information implied in different neighbors while merging them, which is important for making reasonable predictions. As shown in Figure 1, there exists original and inverse edges (relations) in KGs, according to the direction of them, the link prediction tasks can be divided into forward and backward sub-tasks, neighbors can also be grouped into forward and backward neighbors, and sub-tasks in different directions always have diverse preferences for neighbors. For example, while dealing with forward sub-task (Stan Lee, profession,?), it can be solved from backward neighbors including Spider-Man, Iron Man and Captain America; on the other hand, the answer for query (Stan Lee, ethnicity