An Evaluation of Link Prediction Approaches in Few-Shot Scenarios

Abstract

Semantic models are utilized to add context information to datasets and make data accessible and understandable in applications such as dataspaces. Since the creation of such models is a time-consuming task that has to be performed by a human expert, different approaches to automate or support this process exist. A recurring problem is the task of link prediction, i.e., the automatic prediction of links between nodes in a graph, in this case semantic models, usually based on machine learning techniques. While, in general, semantic models are trained and evaluated on large reference datasets, these conditions often do not match the domain-specific real-world applications wherein only a small amount of existing data is available (the cold-start problem). In this study, we evaluated the performance of link prediction algorithms when datasets of a smaller size were used for training (few-shot scenarios). Based on the reported performance evaluation, we first selected algorithms for link prediction and then evaluated the performance of the selected subset using multiple reduced datasets. The results showed that two of the three selected algorithms were suitable for the task of link prediction in few-shot scenarios.