RDF question/answering (Q/A) can translate questions into SPARQL queries by employing question translation.One of the challenges of RDF Q/A is predicting the performance of questions before they are translated. Performance characteristics, such as the translation time, can help data consumers identify unexpected long-running questions before they start and estimate the system workload for scheduling. In this paper, we adopt machine learning techniques to predict the performance of question translation in RDF Q/A.Our work focuses on modeling features of a question to a vector representation. Our feature modeling method does not depend on the knowledge of underlying systems and the structure of the underlying data, but only on the nature of questions. Then we use these features to train prediction models.Finally, based on this model, we designed a single parallel-batching RDF Q/A application.Evaluations are performed on real-world questions, whose translation time ranges from milliseconds to minutes. The results demonstrate that our approach can effectively predict question translation performance.
[1]
Lina Yao,et al.
Learning-based SPARQL query performance modeling and prediction
,
2017,
World Wide Web.
[2]
Dongyan Zhao,et al.
Natural language question answering over RDF: a graph data driven approach
,
2014,
SIGMOD Conference.
[3]
Lei Zou,et al.
Answering Natural Language Questions by Subgraph Matching over Knowledge Graphs
,
2018,
IEEE Transactions on Knowledge and Data Engineering.
[4]
Josiane Mothe,et al.
Query Performance Prediction Focused on Summarized Letor Features
,
2018,
SIGIR.
[5]
Zhiyong Feng,et al.
Multi-Query Optimization in RDF Q/A System
,
2019,
ISWC Satellites.