Artificial intelligence has been widely used in daily wireless networks for its flexibility and adaptability in solving extremely complex problems in real-time. These wireless network applications using artificial intelligence will generate a lot of valuable data. Knowledge graph is able to mine and store useful information from these large amounts of data and make data-driven wireless network applications more intelligent. Existing knowledge graph represents knowledge facts in triples whose examinations are restricted within two entities and binary relations. This nature induces the weakness of expanding to complex learning scenarios with multi-entity relations, which motivates the research insights into the $n$-ary relation. However, existing $n$-ary relation link prediction all embedding-based which are limited by their interpretability. We investigate the ternary relation link prediction task and propose a novel unified framework TRFR which is the first path-based model on $n$-ary relational data. TRFR incorporates the hierarchical attention mechanism and reinforcement learning technique. In addition, we release a newly constructed dataset NELL-995-3 to fill the shortage of learning resources for $n$-ary link prediction. Extensive experiments demonstrate the superiority of the proposed framework compared with a wide variety of state-of-the-art knowledge graph-based approaches in ternary relation link prediction task on one benchmark dataset and one newly constructed dataset.

人工智能由于其灵活性和适应性，可以实时解决极其复杂的问题，因此已广泛用于日常无线网络中。这些使用人工智能的无线网络应用程序将生成大量有价值的数据。知识图能够从这些大量数据中挖掘和存储有用的信息，并使数据驱动的无线网络应用程序更加智能。现有知识图以三元组表示知识事实，其知识检查限于两个实体和二元关系内。这种性质导致了扩展到具有多实体关系的复杂学习场景的弱点，这激发了对$ñ$-ary关系。但是，现有$ñ$三元关系链接预测都是基于嵌入的，受其可解释性的限制。我们调查三元关系链接预测任务，并提出了一个新颖的统一框架TRFR，这是第一个基于路径的模型$ñ$-ary关系数据。TRFR结合了分层注意机制和强化学习技术。此外，我们发布了一个新构建的数据集NELL-995-3，以填补学习资源的不足$ñ$-ary链接预测。大量实验证明，与在一个基准数据集和一个新构建的数据集上的三元关系链接预测任务中的各种基于最新知识图的方法相比，该框架具有优越性。