{"type":"ScholarlyArticle","title":"NaVQA: Mitigating Silent Failures in Question Answering over Virtual Knowledge Graph","authors":[{"name":"Guohui Xiao","slug":"guohui-xiao","given":"Guohui","family":"Xiao","affiliation":"School of Computer Science and Engineering , Southeast University , Nanjing , China; Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) , Ministry of Education , China"},{"name":"Haohan Xue","slug":"haohan-xue","given":"Haohan","family":"Xue","affiliation":"School of Computer Science and Engineering , Southeast University , Nanjing , China; Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) , Ministry of Education , China"},{"name":"Lin Ren","slug":"lin-ren","given":"Lin","family":"Ren","affiliation":"School of Computer Science and Engineering , Southeast University , Nanjing , China; Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) , Ministry of Education , China"},{"name":"Yishuai Geng","slug":"yishuai-geng","given":"Yishuai","family":"Geng","affiliation":"School of Computer Science and Engineering , Southeast University , Nanjing , China; Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) , Ministry of Education , China"},{"name":"Guilin Qi","slug":"guilin-qi","given":"Guilin","family":"Qi","affiliation":"School of Computer Science and Engineering , Southeast University , Nanjing , China; Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) , Ministry of Education , China"},{"name":"Shenyu Zhang","slug":"shenyu-zhang","given":"Shenyu","family":"Zhang","affiliation":"School of Computer Science and Engineering , Southeast University , Nanjing , China; Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) , Ministry of Education , China"},{"name":"DeHao Guo","slug":"dehao-guo","given":"DeHao","family":"Guo","affiliation":"School of Computer Science and Engineering , Southeast University , Nanjing , China; Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) , Ministry of Education , China"},{"name":"Marco Di Panfilo","slug":"marco-di-panfilo","given":"Marco","family":"Di Panfilo","affiliation":"Free University of Bozen-Bolzano , Italy"},{"name":"Davide Lanti","slug":"davide-lanti","given":"Davide","family":"Lanti","affiliation":"Free University of Bozen-Bolzano , Italy"},{"name":"Linfang Ding","slug":"linfang-ding","given":"Linfang","family":"Ding","affiliation":"Jiangsu Key Laboratory of Soil and Water Processes in Watershed , College of Geography and Remote Sensing , Hohai University , Nanjing , China"}],"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.","doi":"10.24963/ijcai.2026/455","url":"https://ijcai.org/proceedings/2026/455","pdf":"https://ijcai.org/proceedings/2026/0455.pdf","markdown":"https://ijcai.org/proceedings/2026/455.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/455","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Knowledge Representation and Reasoning","track":"Main Track","number":455,"pages":{"first":4088,"last":4096},"keywords":["Knowledge Representation and Reasoning: Semantic Web","Knowledge Representation and Reasoning: Description logics and ontologies","Natural Language Processing: Question answering"],"videos":[],"references":[{"position":1,"raw":"[Allemang and Sequeda, 2024] Dean Allemang and Juan Se- queda. 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