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XPathia: A Deep Learning Approach for Translating Natural Language into XPath Queries for Non-Technical Users

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Publié dans:International Journal of Advanced Computer Science and Applications vol. 16, no. 6 (2025)
Auteur principal: PDF
Publié:
Science and Information (SAI) Organization Limited
Sujets:
Translating
Matching
Deep learning
Queries
Information retrieval
Extensible Markup Language
Supervised learning
Query languages
Language
Text categorization
Datasets
Computer science
Syntax
Relevance
Data base management systems
Natural language processing
Documents
Accès en ligne:Citation/Abstract
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Résumé: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.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2025.01606102
Source:Advanced Technologies & Aerospace Database