Combining Word Embeddings-Based Similarity Measures for Transfer Learning Across Relational Domains

Statistical relational learning (SRL) algorithms have succeeded in many real-world applications as real-world data is relational and consists of different entities characterized by different sets of attributes. Real-world data can also be noisy and have incomplete information. Like traditional machine learning models, SRL models also assume training and testing data are sampled from the same distribution. If distributions differ, a new model must be trained using newly collected data. Employing Transfer Learning to machine learning models has become a great asset in handling such issues. It aims to leverage the knowledge learned in a source domain to train a model in a target domain. Moreover, SRL models may suffer from insufficient high-quality data instances and a long training time. Recent work has shown that applying transfer learning is suitable for SRL models as it admits training and testing data sampled from different distributions. However, an essential challenge is how to transfer the learned structure, mapping the vocabulary across different domains. This work relies on a previous approach that uses the similarity between pre-trained word embeddings to guide the mapping and applies theory revision to improve its inferential capacities. However, choosing the most suitable similarity metrics for a specific pair of source and target datasets is not trivial. Thus, we propose to combine different similarity metrics to map predicates. Experimental results showed that combining distinct similarity metrics has improved or equated performance compared to previous methods. It also requires less training time for some experiments.