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Traditional relational databases contain a lot of latent semantic informationthat have largely remained untapped due to the difficulty involved inautomatically extracting such information. Recent works have proposedunsupervised machine learning approaches to extract such hidden information bytextifying the database columns and then projecting the text tokens onto afixed dimensional semantic vector space. However, in certain databases,task-specific class labels may be available, which unsupervised approaches areunable to lever in a principled manner. Also, when embeddings are generated atindividual token level, then column encoding of multi-token text column has tobe computed by taking the average of the vectors of the tokens present in thatcolumn for any given row. Such averaging approach may not produce the bestsemantic vector representation of the multi-token text column, as observedwhile encoding paragraphs or documents in natural language processing domain.With these shortcomings in mind, we propose a supervised machine learningapproach using a Bi-LSTM based sequence encoder to directly generate columnencodings for multi-token text columns of the DrugBank database, which containsgold standard drug-drug interaction (DDI) labels. Our text data driven encodingapproach achieves very high Accuracy on the supervised DDI prediction task forsome columns and we use those supervised column encodings to simulate andevaluate the Analogy SQL queries on relational data to demonstrate the efficacyof our technique.

DrugDBEmbed : Semantic Queries on Relational Database using Supervised Column Encodings