Automatic Understanding of Natural Language Questions for Querying Chinese Knowledge Bases
Xu Ku · Beijing Daxue Xuebao. Zirankexueban · 2014
A framework to transform natural language questions into computer-understoodable structured queries is presented. The authors propose to use query semantic graph to represent the semantics in Chinese questions, and adopt predicate and entity disambiguation to match the query graph to the schema of a knowledge base. The authors collect a benchmark of 42 frequently-asked questions randomly sampled from 3 categories of Baidu Knows, including person, location and organization. Experiment results show that proposed framework can effectively convert natural language questions into SPARQL queries, and lay a good foundation for the next generation of intelligent question answering systems.