Abstract: XPath is a widely used language for navigating and extracting data from XML documents due to its simple syntax and powerful querying capabilities. However, non-technical users often struggle to retrieve the needed information from XML files, as they lack knowledge of XML structures and query languages like XPath. To address this challenge, we propose XPathia, a novel deep learning-based model that automatically translates natural language questions into corresponding XPath queries. Our approach employs supervised learning on an annotated XML dataset to learn accurate mappings between natural language and structured XPath expressions. We evaluate XPathia using two standard metrics: Component Matching (CM) and Exact Matching (EM). Experimental results demonstrate that XPathia achieves a state-of-the-art performance with an accuracy of 25.85% on the test set.

Karam Ahkouk and Mustapha Machkour, “XPathia: A Deep Learning Approach for Translating Natural Language into XPath Queries for Non-Technical Users” International Journal of Advanced Computer Science and Applications(IJACSA), 16(6), 2025. http://dx.doi.org/10.14569/IJACSA.2025.01606102

@article{Ahkouk2025,
title = {XPathia: A Deep Learning Approach for Translating Natural Language into XPath Queries for Non-Technical Users},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.01606102},
url = {http://dx.doi.org/10.14569/IJACSA.2025.01606102},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {Karam Ahkouk and Mustapha Machkour}
}