RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem
Abstract
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.
Cite
Text
Shekarpour et al. "RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem." AAAI Conference on Artificial Intelligence, 2017. doi:10.1609/AAAI.V31I1.11131Markdown
[Shekarpour et al. "RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem." AAAI Conference on Artificial Intelligence, 2017.](https://mlanthology.org/aaai/2017/shekarpour2017aaai-rquery/) doi:10.1609/AAAI.V31I1.11131BibTeX
@inproceedings{shekarpour2017aaai-rquery,
title = {{RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem}},
author = {Shekarpour, Saeedeh and Marx, Edgard and Auer, Sören and Sheth, Amit P.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2017},
pages = {3936-3943},
doi = {10.1609/AAAI.V31I1.11131},
url = {https://mlanthology.org/aaai/2017/shekarpour2017aaai-rquery/}
}