RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem

TitleRQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem
Publication TypeConference Paper
Year of Publication2017
AuthorsSaeedeh Shekarpour, Edgard Marx, Sören Auer, Amit Sheth
Conference Name31st AAAI Conference on Artificial Intelligence (AAAI-17)
Date Published02/2017
Conference LocationSan Francisco, California
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

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Citation Format:
Shekarpour, S., Marx, E., Auer, S. & Sheth, A. (2017). RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem. 31st AAAI Conference on Artificial Intelligence, San Francisco, California.

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