RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem
For non-expert users, a textual query is the most popular and simple means for communicating with a retrieval or question answering system. However, there is a risk of receiving queries which do not match with the background knowledge. Query expansion and query rewriting are solutions for this problem but they are in danger of potentially yielding a large number of irrelevant words, which in turn negatively influences runtime as well as accuracy. In this paper, we propose a new method for automatic rewriting input queries on graph-structured RDF knowledge bases. We employ a Hidden Markov Model to determine the most suitable derived words from linguistic resources. We introduce the concept of triple-based co-occurrence for recognizing co-occurred words in RDF data. This model was bootstrapped with three statistical distributions. Our experimental study demonstrates the superiority of the proposed approach to the traditional n-gram model.
Published in Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17), 2017, pages 3936-3943.
© 31st AAAI Conference on Artificial Intelligence, 2017
Shekarpour, S., Marx, E., Auer, S., & Sheth, A. P. (2017). RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem.