KBQA: Learning Question Answering over QA Corpora and Knowledge Bases
Table 9: Results on QALD-1.
['[EMPTY]', '#pro', '#ri', '#par', 'R [ITALIC] BFQ', 'R∗ [ITALIC] BFQ', 'P', 'P∗']
Results on QALD-1 DEANNA is a state-of-the-art synonym based approach, which also focuses on BFQs. For DEANNA #pro is the number of questions that are transformed into SPARQL queries. The results show that the precision of KBQA is significantly higher than that of DEANNA. Since DEANNA is a typical synonym based approach, this verifies that template based approach is superior to the synonym based approach in terms of precision.