Ontology-based translation of natural language queries to SPARQL
Abstract:
We present an implemented approach to transform natural language sentences into SPARQL, using background knowledge from ontologies and lexicons. Therefore, eligible technologies and data storage possibilities are analyzed and evaluated. The contributions of this paper are twofold. Firstly, we describe the motivation and current needs for a natural language access to industry data. We describe several scenarios where the proposed solution is required. Resulting in an architectural approach based on automatic SPARQL query construction for effective natural language queries. Secondly, we analyze the performance of RDBMS, RDF and Triple Stores for the knowledge representation. The proposed approach will be evaluated on the basis of a query catalog by means of query efficiency, accuracy, and data storage performance. The results show, that natural language access to industry data using ontologies and lexicons, is a simple but effective approach to improve the diagnosis process and the data search for a broad range of users. Furthermore, virtual RDF graphs do support the DB-driven knowledge graph representation process, but do not perform efficient under industry conditions in terms of performance and scalability.
Title: Ontology-based translation of natural language queries to SPARQL
Authors: Ulli Waltinger, Malte Sander
Pub/Conf: Proceedings of the Association for the Advancement of Artificial Intelligence (AAAI) Symposium on Natural Language Access to Big Data
BibTeX:
@inproceedings{SanderWaltinger2014AAAI,
author = {Malte Sander, Ulli Waltinger, Mikhail Roshchin and Thomas Runkler},
url = {http://www.aaai.org/ocs/index.php/FSS/FSS14/paper/view/9172/0},
title = {Ontology-based translation of natural language queries to SPARQL},
booktitle = {Proceedings of the Association for the Advancement of Artificial Intelligence (AAAI) Symposium on Natural Language Access to Big Data},
year = {2014},
month = {November},
date = {13-15},
address = {Arlington, VA, US},
language = {english},
}
