Question Answering over Knowledge Graphs via Structural Query Patterns
Table 1: The performance when only the query graphs are generated.
['Method', 'LC-QuAD Precision', 'LC-QuAD Recall', 'LC-QuAD F1-Measure', 'QALD-8 Precision', 'QALD-8 Recall', 'QALD-8 F1-Measure', 'QALD-9 Precision', 'QALD-9 Recall', 'QALD-9 F1-Measure']
Comparing with the previous methods. As can be seen from the table, our proposed method outperforms the existing approach by a large margin 17.4% absolute gain on LC-QuAD. The performance improves further if our system is given one correctly identified entity in DBpedia. The performance on QALD-8 and QALD-9 get worse than that on LC-QuAD. There are two main reasons: (1) QALD-8 and QALD-9 provide less training data; (2) QALD-8 and QALD-9 are more challenging as several questions are outside the scope of the system. Further analysis is discussed in the next subsection.