A Comparative Evaluation of Visual and Natural Language Question Answering Over Linked Data
This work addresses making Linked Data accessible to users without query language knowledge, offering an incremental improvement by combining visual and natural language interfaces.
The study compared visual diagrammatic and natural language question answering methods over Linked Data using the QALD7 benchmark, finding that the visual approach achieved higher performance but required more manual input, and suggesting their complementary use improves overall QA performance and enables features like data exploration.
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.