Using Multi-Label Classification for Improved Question Answering
This work addresses the challenge of enhancing question answering performance for users of RDF data by integrating diverse systems, though it is incremental as it builds on existing methods.
The paper tackled the problem of improving question answering accuracy over RDF data by combining multiple existing systems into a metasystem using multi-label classification and 14 question features, resulting in a 14% F-measure improvement over the best single system on the QALD-6 benchmark.
A plethora of diverse approaches for question answering over RDF data have been developed in recent years. While the accuracy of these systems has increased significantly over time, most systems still focus on particular types of questions or particular challenges in question answering. What is a curse for single systems is a blessing for the combination of these systems. We show in this paper how machine learning techniques can be applied to create a more accurate question answering metasystem by reusing existing systems. In particular, we develop a multi-label classification-based metasystem for question answering over 6 existing systems using an innovative set of 14 question features. The metasystem outperforms the best single system by 14% F-measure on the recent QALD-6 benchmark. Furthermore, we analyzed the influence and correlation of the underlying features on the metasystem quality.