# Cognitive Database: A Step towards Endowing Relational Databases with Artificial Intelligence Capabilities

arXiv: https://arxiv.org/abs/1712.07199 · HTML: https://mlsanity.com/paper/1712.07199/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2017.

## Built on by (most cited first, 6 of 6)

- [Cross Modal Data Discovery over Structured and Unstructured Data Lakes](https://mlsanity.com/paper/2306.00932/) (2023)
- [Selecting Walk Schemes for Database Embedding](https://mlsanity.com/paper/2401.11215/) (2024)
- [Efficient Approximate Search for Sets of Vectors](https://mlsanity.com/paper/2107.06817/) (2021)
- [Towards a GML-Enabled Knowledge Graph Platform](https://mlsanity.com/paper/2303.02166/) (2023)
- [Efficient and Accurate In-Database Machine Learning with SQL Code Generation in Python](https://mlsanity.com/paper/2104.03224/) (2021)
- [ML Based Lineage in Databases](https://mlsanity.com/paper/2109.06339/) (2021)

## Abstract (arXiv)

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.