With the wide application of knowledge graphs, knowledge graph completion has garnered increasing attention in recent years. However, we find that the long tail relation is more common in the KG. These relations typically do not have a large number of triples for training and are referred to as few-shot relations. The knowledge graph completion in the few-shot scenario is a major challenge currently. The current mainstream knowledge graph completion algorithms have the following drawbacks. The metric-based methods lack interpretability of results, while the algorithms based on path interaction are not suitable for few-shot scenarios and the availability of the model is limited in sparse knowledge graphs. In this paper, we propose a one-shot relational learning of knowledge graphs completion based on multi-hop information enhancement(MHEC). Firstly, MHEC extracts entity concepts from multi-hop paths to obtain task related entity concepts and filters out noisy neighbor attributes. Then, MHEC combines multi-hop path information between head and tail to represent entity pairs. Compared to previous completion methods that only consider structural features of entities, MHEC considers the reasoning logic between entity pairs, which not only includes structural features but also possesses rich semantic features. Next, MHEC introduces a reasoning process in the completion task to address the issues of lack of interpretability in the one-shot scenario. In addition, to improve completion and reasoning quality in sparse knowledge graphs, MHEC utilizes contrastive learning to enhance pre-training vector representations of entities and relations and proposes a matching processor that leverages the semantic information of pre-training vectors to assist the reasoning model in expanding the multi-hop paths. Experiments demonstrate that MHEC outperforms the state-of-the-art completion techniques on real-world datasets NELL-One and FB15k237-One.
