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There are massive amounts of textual data residing in databases, valuable formany machine learning (ML) tasks. Since ML techniques depend on numerical inputrepresentations, word embeddings are increasingly utilized to convert symbolicrepresentations such as text into meaningful numbers. However, a naiveone-to-one mapping of each word in a database to a word embedding vector is notsufficient and would lead to poor accuracies in ML tasks. Thus, we argue toadditionally incorporate the information given by the database schema into theembedding, e.g. which words appear in the same column or are related to eachother. In this paper, we propose RETRO (RElational reTROfitting), a novelapproach to learn numerical representations of text values in databases,capturing the best of both worlds, the rich information encoded by wordembeddings and the relational information encoded by database tables. Weformulate relation retrofitting as a learning problem and present an efficientalgorithm solving it. We investigate the impact of various hyperparameters onthe learning problem and derive good settings for all of them. Our evaluationshows that the proposed embeddings are ready-to-use for many ML tasks such asclassification and regression and even outperform state-of-the-art techniquesin integration tasks such as null value imputation and link prediction.

RETRO: Relation Retrofitting For In-Database Machine Learning on Textual Data