Expressivity and accuracy of by-example structured queries on Wikipedia

This paper discusses expressivity and accuracy of the By-Example Structured (BESt) Query paradigm implemented on the SWiPE system through the Wikipedia interface. We define an experimental setting based on the natural language questions made available by the QALD-4 challenge, in which we compare SWiPE against Xser, a state-of-the-art Question Answering system, and plain keyword search provided by the Wikipedia Search Engine. The experiments show that SWiPE outperforms the results provided by Wikipedia, and it also performs sensibly better than Xser, obtaining an overall 85% of totally correct answers vs. 68% of Xser. Among all answered questions, we obtain a precision of 100% and recall 96%. SWiPE is also able to answer more questions than the other systems. A formal characterization of the set of SPARQL queries supported by the BESt Query paradigm is also provided.