NaVQA: Mitigating Silent Failures in Question Answering over Virtual Knowledge Graph
Guohui Xiao, Haohan Xue, Lin Ren, Yishuai Geng, Guilin Qi, Shenyu Zhang, DeHao Guo, Marco Di Panfilo
Abstract
Virtual Knowledge Graphs (VKGs) provide unified access to legacy relational data sources through a high-level ontology modeling a domain of interest. The content of the ontology elements (classes and properties) is virtually mapped to underlying data sources through declarative mappings. The standard approach to interacting with a VKG system is to use SPARQL as the query language. However, the complexity of SPARQL creates a high entry barrier for normal users. In this paper, we study the VKG-QA task, which enables users to interact with the VKGs through a natural language (NL) interface by translating their questions into SPARQL queries. One critical challenge in such translation is silent failures, where a syntactically correct SPARQL query returns empty results because it involves ontology elements that lack mappings to the underlying data. To address this, we propose NaVQA (Navigation-based VKG Question Answering), a framework leveraging Large Language Models that (1) identifies and indexes the active ontology elements based on the mapping specifications, and (2) iteratively constructs a query graph from the NL question using the active ontology elements and their relations to synthesize SPARQL. Experiments on three real-world VKGs show that NaVQA significantly mitigates silent failures and outperforms existing baselines.
BibTeX
@inproceedings{ijcai2026_navqamitigatings,
title = {NaVQA: Mitigating Silent Failures in Question Answering over Virtual Knowledge Graph},
author = {Guohui Xiao and Haohan Xue and Lin Ren and Yishuai Geng and Guilin Qi and Shenyu Zhang and DeHao Guo and Marco Di Panfilo and Davide Lanti and Linfang Ding},
booktitle = {IJCAI 2026},
year = {2026}
}