Concept driven framework for Latent Table Discovery

TL;DR

This paper introduces a concept-driven framework to enhance semantic capabilities in database systems by improving latent table discovery, enabling meaningful connections between unrelated tables for better information retrieval.

Contribution

It proposes a novel concept-driven approach that extends latent table discovery to improve semantic linking of unrelated tables in databases.

Findings

Enhanced semantic linking between unrelated tables.

Improved information retrieval through concept-driven connections.

Framework supports natural language query interfaces.

Abstract

Database systems have to cater to the growing demands of the information age. The growth of the new age information retrieval powerhouses like search engines has thrown a challenge to the data management community to come up with novel mechanisms for feeding information to end users. The burgeoning use of natural language query interfaces compels system designers to present meaningful and customised information. Conventional query languages like SQL do not cater to these requirements due to syntax oriented design. Providing a semantic cover over these systems was the aim of latent table discovery focusing on semantically connecting unrelated tables that were not syntactically related by design and document the discovered knowledge. This paper throws a new direction towards improving the semantic capabilities of database systems by introducing a concept driven framework over the latent table discovery method.