Enhancing Keyword Search in Relational Databases With Word Embeddings

Keyword search in relational databases allows the users to query these databases using natural language keywords, bridging the gap between structured data and intuitive querying. However, ambiguity in user queries as well as the complexities of structured database relationships often complicate the retrieval of relevant results. This paper introduces Key2Vec as a framework for leveraging the Natural Language Processing (NLP) techniques, specifically Word2Vec embeddings, for enhancing the keyword search in relational databases. Key2Vec enhances both the query relevance and the execution efficiency by semantically interpreting the keywords and optimizing the query classes, those categories of user queries which are grouped by their specific objectives and their data requirements. Experimental evaluations, on benchmark databases, demonstrate that Key2Vec improves the accuracy about 25% and reduces the query processing time by 30%, establishing it as a robust solution.