2017/12/19 by Rajesh Bordawekar, Bordawekar, Rajesh, Bortik Bandyopadhyay +3 · 19 citations
Computer Science · #Advanced Database Systems and Queries #Artificial Intelligence (cs.AI) #Artificial intelligence #Computation and Language (cs.CL) #Computer science #Database #Database design #Database model #Databases (cs.DB) #FOS: Computer and information sciences #Information retrieval #Logic, Reasoning, and Knowledge #Natural language processing #Neural and Evolutionary Computing (cs.NE) #Relational database #SQL #Semantic Web and Ontologies #cs.AI #cs.CL #cs.DB #cs.NE
paper · pdf · doi:10.48550/arxiv.1712.07199
published in arXiv (Cornell University) (Cornell University)
arxiv created 2017/12/19 · openalex publication_date 2017/12/19 · arxiv updated 2017/12/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose Cognitive Databases, an approach for transparently enabling Artificial Intelligence (AI) capabilities in relational databases. A novel aspect of our design is to first view the structured data source as meaningful unstructured text, and then use the text to build an unsupervised neural network model using a Natural Language Processing (NLP) technique called word embedding. This model captures the hidden inter-/intra-column relationships between database tokens of different types. For each database token, the model includes a vector that encodes contextual semantic relationships. We seamlessly integrate the word embedding model into existing SQL query infrastructure and use it to enable a new class of SQL-based analytics queries called cognitive intelligence (CI) queries. CI queries use the model vectors to enable complex queries such as semantic matching, inductive reasoning queries such as analogies, predictive queries using entities not present in a database, and, more generally, using knowledge from external sources. We demonstrate unique capabilities of Cognitive Databases using an Apache Spark based prototype to execute inductive reasoning CI queries over a multi-modal database containing text and images. We believe our first-of-a-kind system exemplifies using AI functionality to endow relational databases with capabilities that were previously very hard to realize in practice.