Self-service Ad-hoc Querying Using Controlled Natural Language

TL;DR

This paper introduces a natural language and ontology-based approach to simplify ad-hoc data querying, enabling business experts to access data directly and quickly, thereby improving decision-making efficiency.

Contribution

It presents a novel controlled natural language and semistar ontology framework that reduces the complexity of ad-hoc querying for non-technical users.

Findings

Significantly shortens learning curve for ad-hoc querying

Enables direct data access for business experts

Facilitates faster decision-making processes

Abstract

The ad-hoc querying process is slow and error prone due to inability of business experts of accessing data directly without involving IT experts. The problem lies in complexity of means used to query data. We propose a new natural language- and semistar ontology-based ad-hoc querying approach which lowers the steep learning curve required to be able to query data. The proposed approach would significantly shorten the time needed to master the ad-hoc querying and to gain the direct access to data by business experts, thus facilitating the decision making process in enterprises, government institutions and other organizations.