# A Comparative Evaluation of Visual and Natural Language Question Answering Over Linked Data

## TL;DR

This paper compares visual diagrammatic and natural language question answering methods over Linked Data, finding that combining both approaches enhances performance and data exploration capabilities.

## Contribution

It introduces a comparative evaluation of visual and natural language QA systems over Linked Data and demonstrates the benefits of their combination.

## Key findings

- Visual QA method achieves higher performance but requires more manual input.
- Combining visual and natural language approaches significantly improves QA results.
- The integrated approach facilitates better data exploration.

## Abstract

With the growing number and size of Linked Data datasets, it is crucial to make the data accessible and useful for users without knowledge of formal query languages. Two approaches towards this goal are knowledge graph visualization and natural language interfaces. Here, we investigate specifically question answering (QA) over Linked Data by comparing a diagrammatic visual approach with existing natural language-based systems. Given a QA benchmark (QALD7), we evaluate a visual method which is based on iteratively creating diagrams until the answer is found, against four QA systems that have natural language queries as input. Besides other benefits, the visual approach provides higher performance, but also requires more manual input. The results indicate that the methods can be used complementary, and that such a combination has a large positive impact on QA performance, and also facilitates additional features such as data exploration.